Spatially Resolved Star Formation History of Milky Way Satellites: the Case of Carina

UNIVERSIDAD DE CHILE FACULTAD DE CIENCIAS F´ISICAS Y MATEMATICAS´ DEPARTAMENTO DE ASTRONOM´IA

SPATIALLY RESOLVED STAR FORMATION HISTORY OF MILKY WAY SATELLITES: THE CASE OF CARINA

TESIS PARA OPTAR AL GRADO DE DOCTOR EN CIENCIAS, MENCION´ ASTRONOM´IA

FELIPE ANTONIO SANTANA ROJAS

PROFESOR GU´IA: RICARDO RODRIGO MUNOZ˜ VIDAL MIEMBROS DE LA COMISION:´ JULIO CHANAME´ DOM´INGUEZ ANDRES´ ESCALA ASTORQUIZA GUILLERMO BLANC MENDIBERRI

SANTIAGO DE CHILE ENERO 2016 RESUMEN DE LA TESIS PARA OPTAR AL GRADO DE DOCTOR EN CIENCIAS, MENCION´ ASTRONOM´IA

HISTORIA DE FORMACION´ ESTELAR ESPACIALMENTE RESUELTA DE SATELITES´ DE LA V´IA LACTEA´

En este trabajo presento un estudio acerca de la historia de formaciónestelar de galaxias del Grupo Local y su aplicaciónpara el caso de la galaxia enana esferoidal, Carina. La primera secciónde este trabajo presenta un catálogode alta precisiónde satélitesdel Halo externo de la V´ıaLáctea. Este catálogova a proveer información muy importante acerca de los parámetrosestructurales y poblaciones estelares de los satélitesde la V´ıaLáctea.

Esta tesis tambiénpresenta el estudio de estrellas azules rezagadas encontradas en satélitesde la V´ıa Láctea. Con esta informaciónen mano derivamos una técnica para discriminar entre el númerode estrellas azules rezagadas y el numero de estrellas jóvenes. Esta técnicaayudarásignificativamente a derivar fracciones de estrellas jovenes sin el sesgo que producen las azules rezagadas, al momento de derivar las his- torias de formaciónestelar.

Luego, presento nuestro método para derivar la historia de formacion estelar, llamado Talos. Esta rutina presenta varias ventajas con respecto a otras implementaciones, principalmente porque usa la informacion de todas las regiones del CMD, usa también la distribuciónde metalicidad del sistema como input y por tanto no es necesario hacer suposiciones previas acerca de la relacion entre la metalicidad y la edad, y finalmente porque considera varias propiedades de los datos observados al momento de construir los modelos sintéticos,y de esta forma estos modelos son directamente comparables con los datos.

Finalmente presento mi implementaciónde la rutina Talos para derivar la historia de formaciónestelar espacialmente resuelta de la galaxia enana esferoidal Carina. Gracias a la alta calidad de los datos usados, las ventajas del método usado para derivar la historia de formaciónestelar y la implementaciónde nuestra técnicapara disciminar entre estrellas azules rezagadas y estrellas jóvenes, los resultados aqu´ıpresentados al- canzan una alt´ısimaprecisióncon respecto a trabajos previos. En este trabajo pudimos descifrar que la poblaciónde estrellas jóvenes comúnmente encontradas en Carina, son en realidad estrellas azules rezagadas que han sido mal clasificadas. A partir de los resultados obtenidos concluimos que la formaciónestelar de Carina estádominada por un proceso de evolucin interna, y no por la influencia de marea que le ejerce la V´ıa Láctea,como mayoritariamente se concluye acerca de esta galaxia.

i SUMMARY OF THESIS FOR THE DEGREE OF DOCTOR OF PHYLOSOPHY IN ASTRONOMY

SPATIALLY RESOLVED STAR FORMATION HISTORY OF MILKY WAY SATELLITES: THE CASE OF CARINA

I present a complete study about the derivation of star formation histories of local dwarf galaxies and apply our method to the case of the Carina dSph galaxy. The ﬁrst section of the work presents a high quality photometric catalogue of Milky Way satellites in the outer Halo. This catalogue will then provide important information about the structural properties and stellar populations of Milky Way satellites. At the same time, these results, once derived, will shed valuable information about structure assembly, dark matter properties, formation of the Milky Way, galaxy interaction, and conditions of the early Universe, all of which are closely related to the properties of the systems in the Local Group.

This thesis also presents a study of blue straggler stars found in Milky Way satellites. With this information we derived fundamental properties about these type of stars in very low stellar density galaxies, an environment in which these stars had practically never been studied before. With this information, we derived a new technique for discriminating blue straggler counts and young star counts. This technique will help signiﬁcantly to derive unbiased young star population fractions in the studies of star formation histories of dwarf galaxies.

After that, I present the routine I used for deriving the star formation history of Carina dSph galaxy, which is based on the synthetic CMD method, and is called Talos. This routine presents several advantages with respect to other implementations, mainly becuase it uses as input the information from all the CMD regions, it uses independent metallicity measurements that enable us to avoid assuming an age-metalicity relation, and because it consideres several characteristics of the observations when constructing the synthetic models and in this way this models are directly comparable to the data.

Finally I present my implementation of the Talos routine to derive the spatially resolved star formation history of the Carina dSph. Thanks to the high quality data, the state-of-the-art routine, and the new technique to discriminate between blue stragglers counts and young star counts, the results presented in here reach an unprecedented accuracy. In this study, we were able to derive that the young star population often found in Carina, are actually missclassiﬁed blue stragglers, and hence, there are only two episodes of star formation in Carina instead of three as it has been often claimed. With all this new information in hand, we concluded that the star formation of Carina is dominated by an internal evolution process as opposed to tidal inﬂuence from the Milky Way as is the current consensus.

ii Acknowledgements

The work of this thesis was founded by two different projects. The first one is CONICYT-PCHA/Doctorado Nacional/2010-21100133, and the second one is CONI- CYT Anillo project ACT-1122. I thank the founding from those project, that enable me to carry out my PhD program and the consequent thesis. I thank my advisor Ricardo Muñozfor his support throughout all the length of my PhD program, for all the scientific discussions, and for all the opportunities he provided for me to travel abroad and work in different Universities during my PhD program. I also thank the committee for helpful comments on my thesis text that significantly helped improve the final result. I thank the professors in charge of the astronomy courses I received during all these years, which significantly help me to form my current knowledge about a variety of topics in astronomy.

I sincerely thank my family for being around me throughout my life, for their advices, support, and for letting me follow my vocation with nothing but support and effort from them. I thank also my school friends, Ignacio, Pablo, Diego, Sebastián,Roc´ıo, Natalia y Constanza, with which I keep close relations until these days. Sharing with them filled me with very valuable advices and experiences, and were a tremendous injection of energy was received by them each time things during the PhD became a little messy. I also thank Pedro, a close friend from my time at Beaucheff, and with which I share very valuable memories. I also thank my office mates during this six years, Andrés,Sergio, Thomas, Julián,and Sebastián.Their company was very useful for astronomical discussions, general advices about the student life, and mostly to make every day at the University more pleasent and fun.

Finally, I thank my girlfriend the most, Celia, the love of my life, who was also my office mate during my PhD program. She significantly helped me with the text and work of this thesis, along with almost every decision I have made regarding my astronomical carrer and most of all, for making me extremely happy day after day, and motivating me to keep with my best effort every day.

iii Table of Contents

1 Introduction 1

2 Photometric Catalogue of Outer Milky Way Satellites 6

2.1 Introduction: Milky Way Satellites and the Local Group ...... 6

2.1.1 Early History and Deﬁnitions ...... 6

2.1.2 Connecting Dwarf Galaxies with Dark Matter Halos and the Missing Satellites Problem ...... 8

2.1.3 Scaling relations in dwarf galaxies and connection to formation mechanisms ...... 12

2.2 Complete Catalogue: General Remarks ...... 22

2.3 Southern Region Catalogue Construction ...... 23

2.3.1 Photometry: Instrumental magnitude determination ...... 23

2.3.2 Astrometry ...... 25

2.3.3 Magnitude calibration: Sloan ﬁlters magnitude determination . 27

2.3.4 Merging Individual Files into Final Files ...... 32

2.4 Catalogue ﬁnal results example ...... 34

3 Blue stragglers in Outer Milky Way Satellites† 46

3.1 Introduction ...... 46

3.2 Data and Blue Straggler Selection ...... 49

3.3 Results ...... 53

iv 3.3.1 Blue Straggler Speciﬁc Fractions ...... 53

3.3.2 Blue straggler/Young Stars Discrimination ...... 57

3.3.3 Radial Distribution Analysis ...... 62

3.4 Discussion ...... 63

3.4.1 Dwarf Galaxies ...... 63

3.4.2 Globular Clusters ...... 64

3.4.3 Discriminating between Blue Straggler Counts and Young Star Counts ...... 66

3.5 Conclusions ...... 67

4 Star Formation History of Milky Way Satellites 69

4.1 Deﬁnitions and Generalities of the Star Formation History ...... 69

4.2 The Synthetic CMD Method ...... 71

4.2.1 Our Application of the Synthetic CMD Method: Talos . . . . . 78

4.3 Star Formation History Measurements on Milky Way Satellites . . . . . 82

5 Spatially Resolved Star Formation History of the Carina dSph Galaxy 91

5.1 Introduction ...... 91

5.2 Data ...... 93

5.2.1 Photometry ...... 93

5.2.2 Spectroscopy ...... 94

5.3 Method for deriving the Star Formation History of Carina ...... 94

5.3.1 General Setup ...... 95

5.3.2 Additional input ﬁles ...... 97

5.4 Results ...... 98

5.5 Discussion ...... 103

5.5.1 Young Population v/s Blue Straggler Population ...... 103

v 5.5.2 Star Formation History of Carina: Internal Evolution v/s Exter- nal inﬂuence ...... 109

6 Conclusion 113

Bibliography 121

vi Chapter 1

Introduction

This thesis presents the compilation of the work done during my PhD program in astronomy at Universidad de Chile. The main topic of this study is the derivation of star formation histories of Milky Way satellites, and its implementation on the Carina dwarf spheroidal galaxy. The star formation history is the mathematical representation of the number of stars formed by a stellar system as a function of time. Is the characterization of when a galaxy or cluster formed its stars and at which chemical composition. The star formation history has historically been derived for a variety of stellar systems in astronomy, to quantitatively assess their evolution and with that to try and elucidate the environmental conditions and physical processes that govern their history.

In this work, we study the history of a particular type of stellar systems, the satellites of the Milky Way. As we know, our own planetary system resides in the Milky Way galaxy and our Sun, along with all its planets, revolve around the center of the galaxy. In a similar way, there are galaxies and stellar clusters signiﬁcantly smaller that the Milky Way that are trapped by its gravitational inﬂuence, and hence, orbit around our home galaxy. For all these reasons, this stellar systems are called Milky Way satellites. Even though they reside in the vecinity of the Milky Way, they are self-bound systems that were formed in their own gravitational wells.

The Milky Way satellites are in broad terms composed by two types of systems: the first ones are globular clusters which are spherical collections of bound stars orbiting a galaxy (in this case the Milky Way), and dwarf galaxies which are small galaxies formed in their own halos but whose history is oftenly influenced by larger galaxies lying in their proximity. The main difference between these two types of systems is the presence of dark matter, which is not found in globular clusters but conformes large fraction of the mass of the dwarf galaxies. Dark matter is, a very special type of mass that until now astronomers have been able to detect only through the gravitational effect it produces on other objects. These agglomerations of dark matter are collectively called dark matter halos, and by definition all the galaxies are born within its own dark halo, where they transform gas into stars. A little farther than the Milky Way

1 satellites there is another galaxy similar in size than the Milky Way, called Andromeda or Messier 31 (M31), which, as the Milky Way, has its own satellites. These two big galaxies and their own satellites form a self-gravitating association called the Local Group, and hence, the satellite galaxies that reside in it are called local dwarf galaxies.

Local dwarf galaxies are crutial for understanding several key aspects of modern astrophysics (Mateo, 1998; Tolstoy et al., 2009). First, they are extremely dominated by dark matter (Simon & Geha, 2007), meaning that they have very high fractions of dark matter over baryonic matter (which includes the mass in the form of gas, dust and stars). This implies that these systems are ideal laboratories to study the nature of dark matter and how it influences the evolution of gas and stars in galaxies. Moreover, dwarf galaxies are among of the oldest systems in the Universe (Kauffmann et al., 1993), and some of them have evolved very little since their formation, thus, these systems are often considered fossils from the early Universe (e.g. Brown et al., 2014). In addition, the ones that reside in the Local Group lie at close proximity to us, and hence, we can study them at greater detail than any other type of galaxy. One of the biggest difficulties about studying this type of systems is the fact that their extremely low stellar numbers make them sometimes very hard to characterize (Martin et al., 2008a). This is the main reason why many aspects about the properties of Milky Way satellites remain ellusive. These topics include a proper differentiation betweeen the different types of satellites, a complete characterization of their star formation histories and the physical processes governing them and how this type of systems gave origin to the Local Group in the early Universe.

The main objetive of this thesis work is to provide relevant and original information about the evolution of Milky Way satellites. For that purpose, this study is divided in 4 parts: (1) constructing a milky way satellites catalogue, (2) analyze blue straggler stars in our catalogue and elucidate a method for distinguishing them from young stars, (3) study the diﬀerent methods for deriving the star formation history of Milky Way satellites, and (4) apply a method for deriving the star formation history of Carina, a local dwarf galaxy with a particular evolution that has intrigued astronomers since its discovery (e.g. Smecker-Hane et al., 1994; Hurley-Keller et al., 1998; Koch et al., 2006; de Boer et al., 2014). In the following paragraphs, I am going to give a quick summary of the chapters of this thesis along with a brief explanation of how each section help us reach our ﬁnal goal of providing relevant information about the evolution of Milky Way satellites.

The ﬁrst step to unravel any of the physical properties of Milky Way satellites is using a complete an homogeneous data sample of these objects. Nevertheless, a catalogue with those characteristics does not exist at the moment. This is the reason why this thesis starts with explaining how the collaboration group that I am part of, developed a complete sample of the stellar overdensities in the outer region of the Milky Way. This survey provides a signiﬁcant contribution of Milky Way satellite data due to its high sensisivity and area coverage compared to former observations. With this new catalogue we can detect and characterize stars too faint to be accounted for in previous

2 surveys. Moreover, the area mapped for each system in this catalogues includes the vast majority of the stars belonging to the system and not just the most central ones as it is done in prior studies with smaller area coverage (see for example Tolstoy et al., 2009; Weisz et al., 2014, for reviews that highlight the current state of photometry on local dwarf galaxies). In this way, this new survey provides relevant and new information about Milky Way satellites that had not been considered before. In addition, all the information from the different systems in the catalogue have been extracted using the same instruments and the same procedure, therefore, there is no bias in this work as the one that would be produced by using data from different sources and with different characteristics. For these reasons, using this data sample as the angular stone of this work, provides us with a significant advantage with respect to other studies and gives us the oportunity for making relevant discoveries within and further the scope of this thesis. Given that the work done during my PhD program represented a very important part of the creation of this catalogue, I explain in this thesis some of the most important details involved in developing this catalogue. The main objetives of this section is to make the procedure completely replicable by anyone who would like to do something similar, and to provide validity on the results obtained in the following sections, which is based in part on the quality of the data used.

Another requirement for constructing statistically significant, unbiased star formation history is a proper distinction between the different stellar populations conforming the system. Astrophysical models that reproduce the evolution of stars in isolation have been very successful in explaining the main properties observed for stellar populations (e.g Dotter et al., 2008). However, things get more complicated and unknown when we consider interactions between stars which alters the evolution of the stars involved. A good example of the product of this interaction is blue stragglers, which are stars coeval to a given stellar population but that are brighter and bluer than the stars in the main-sequence turnoff. The color and brightness of this stars mimick the one that a younger population would have. Since the derivation of the star formation history is mainly based on the position of the stars in the color magnitude diagram (CMD), young stars and blue stragglers might get easily confused and their distinction is not straightforward (Momany et al., 2007). This is our main motivation to study the properties of blue stragglers in our Milky Way satellites catalogue. By systematically counting blue stragglers in these systems and analyzing the relationship between those numbers and the properties of the host system we developed a method for deriving the fraction of actual young stars populating the region of the CMD shared by blue stragglers and young stars. This method was then applied for the specific case of Carina where the discrimintion between these two types of stars has further complications. This is the first time a star formation history work explicitly discriminates between young stars and blue stragglers and the aplication of this method in upcoming studies can provide an efective way of eliminating an important source of bias when deriving the star formation histories that could otherwise mislead the analysis. Furthermore, the properties we have discovered for blue stragglers in this study are useful beyond their application to star formation history derivations. In fact, the properties we discovered about blue stragglers provide key information about the origin of these stars

3 and the inﬂuence from the surrounding environment. This in turn help us elucidate important details about the dynamical processes happening within the satellites of the Milky Way.

Once we had constructed the catalogue for Milky Way satellites and analyzed the blue straggler star properties to distinguish them from young stars, we could start studying the topic of the star formation history derivation. The third section adresses the work I did about this topic. The main purpose of this section is to describe the different methods for deriving the star formation history for different types of data samples, highlighting the advantages of each approach. This section also includes a background guide to star formation history studies, were we indicate how the mathematical representation of the star formation history relates with other astrophysical quantities. Another thing presented in this chapter is a list of the most important scientific discoveries that had been made in the last years on this topic. In this way, we can know what our starting point is and what the most important issues are, that remain to be adressed. After this, we present the star formation history derivation method specifically used for this study, which is the Talos (de Boer et al., 2012) routine. We show how this set of scripts define the goodness of fit for each model and also the minimization procedure performed to choose the model that best represents the input data. We also indicate all the advantages Talos represents with respect to other star formation history derivation methods and finally we present some examples of results obtained with this routine that show graphically the power of using this tool with good quality data.

Finally, in the last main section we apply the work presented in each of the previous sections. We present the derivation of the star formation history of the Carina dwarf spheroidal galaxy using the routine Talos. From all the systems present in the photometric catalogue we constructed, we chose the galaxy with the most intruiging evolution, which is the case of Carina, and for that one we derived its star formation history. The Carina dwarf spheroidal galaxy is the galaxy from the Local Group displaying the most noticeable evidence of an episodic star formation history (de Boer et al., 2014), which means that it has active epochs of star formation separated by at least one epoch with no star formation at all. Moreover, Carina is one of the local dwarf galaxies (excluding Sagittarius) displaying the most noticeable evidence of tidal features in its stellar populations (Muñozet al., 2006). These signatures, are probably the result of the tidal force that the Milky Way exerts on Carina. These two distinguishing characteristics observed in Carina have led astronomers to try and establish a connection between them, in the sense that the episodic star formation history of Carina is the results of the tidal influence from the Milky Way (e.g. Pasetto et al., 2011), and for example each episode of star formation in Carina occured whenever the galaxy experienced a close passage from the Milky Way. Nevertheless, the works that have tried to relate the star formation in Carina with the tidal influence from the Milky way had not been successful. This is the reason why until now one of the biggest open questions about Carina is the physical process that produced its episodic star formation history. The reason for its particular star formation history can be tidal influence from

4 the Milky way, a different external event like gas inflow, or just internal evolution. One of the main reason why the phyiscal origin of the intermittent star formation of Carina remains elusive, is that the details of the star formation history itself are still a matter of debate. There exist the general idea in the astronomical community that Carina formed stars in two or more episodes separated by gaps of star formation. However, the moment at which these episodes occured, the duration of each one, the metal content of the stars formed each time and the exact number of episodes of star formation are still uncertain and the values presented in the literature differ significantly from one another. This is why the main specific goal of this thesis is to characterize the different stellar populations in Carina with results that have larger statistical significance than any previous study on this topic. As mentioned earlier, we used a data sample of Ca- rina with unprecedented accuracy to run Talos. This presents several advantages with respect to the ones used in most star formation history works, mainly becuase it uses the information from all the regions of the CMDs and not just key fingerprints, and also becuase it uses independent metallicity measurements to avoid making assumptions on the chemical enrichment of the object Other key difference between this study and the ones found in the literature is that in our case we derive the star formation history independently in three different concentric regions of Carina. In this way, we can obtain more information about the evolution of Carina that what we could got by deriving a single star formation history for the entire Carina region as it has been done for most prior studies. The other main specific goal of this thesis is to analyze our results about the star formation history of Carina and elucidate the physical processes governing Carina’s evolution, i.e., internal evolution v/s gravitational influence from the Milky Way.

In summary, we will present a data sample and a method with which a high quality star formation history determination can be done for any satellite of the Milky Way, and both the data and the procedures presented here are going to be available for the scientiﬁc comunity for anyone who wants to investigate this topic. Additionally, we applied this technique to the case of the Carina dwarf spheroidal, which is one the most intriguing cases of galaxy evolution in the Local Group.

5 Chapter 2

Photometric Catalogue of Outer Milky Way Satellites

2.1 Introduction: Milky Way Satellites and the Lo- cal Group

2.1.1 Early History and Deﬁnitions

The Earth, our home planet, orbits around the Sun as other seven planets and minor bodies do. All these objects conform what we know as the Solar System. At the same time, the Sun with all the other objects of the Solar System, revolve around the center of our home galaxy called the Milky Way 1. For a long time, the Milky Way Galaxy was thought to represent the entire spatial extent of the Universe and nothing was conceived outside the Galaxy2. However, observations of extagalactic objects can be traced back as far in time as the year 964 A.C., when Abd al-Rahman Al-Sufi reported the first observations of what we now know as the Large Magellanic Cloud galaxy and the M31 galaxy, in his Book of Fixed Stars. Nevertheless, by that moment the Persian astronomer had no suspicion that the objects he was observing were independent galaxies. One of the first to propose the hypothesis of separate independent galaxies was Thomas Wright, who in 1750 hypothesized that the Milky Way was a flattened disk of stars and that some of the nebulae3 observed in the night sky were separated Milky Ways. However, Wright’s ideas were not based on astronomical observations, and for

1The term comes from the greek words “galax´ıask´yklos”,that can be translated as “milky circle”, and its due to the milky appearance of thousands of unresolved stars located at close angular distance to each other 2Each time the term “Galactic” or “Galaxy” are used with capital letters they refer to the Milky Way, whereas their lower case counterparts indicate any other galaxy 3Objects of the night sky independent from the projection of the Galactic disk, but that share their blurry appearance

6 more than a century they remained as pure theory. The first time the extragalactic nature of the nebulae was critically analyzed based on observations was during the early 1920’s when Harlow Shapley and Heber Curtis staged the “great debate” about the nature of the “Andromeda Nebulae”. The first one defended the hypothesis that it was small like the solar system, and hence, it was part of the Milky Way and that the Universe only extended this far. The second one stated that it was an independent object outside the Galaxy. The debate was closed by Edwin Hubble when he discovered variable cepheids and determined the distance to several nebulae which included M31, hence proving their extragalactic nature. Shortly after Hubble finally proved that the Milky Way was just one of the galaxies present in the Universe, he defined the close neighborhood where the Milky Way, M31 and other galaxies reside as the “Local Group”. In his book The Realm of the nebulæ(1936) he described this term by writing:

The galactic system is a member of a typical, small group of nabulæ which is isolated in the general ﬁeld. The known members of the “Local Group” are the galactic system with the Magellanic Clouds as its two companions; M31 with M32 and NGC 205 as its companions; M33, NGC 6822 and IC 1613. The three nabulæ, NGC 6946, IC 10 and 342, may be members, but their are so heavily obscured that their distances are indeterminate.

Despite this deﬁnition being 80 years old, it is still widely used today to describe the most immediate neighborhood in which the Galaxy resides, and is remarkably accurate in describing the broad conﬁguration of the group given the limited power of observation available at the epoch. The current view of the Local Group, as described by Hubble, is conformed by the Milky Way and a collection of galaxies that orbit around it, called Milky Way satellites, M31 along with its own satellites, and other galaxies that are not associated to the Milky Way nor M31, but are gravitationally bound by the potential of the Local Group as a unity. At the same time, all the galaxies that belong to the Local Group and are not the main Milky Way or M314, are denominated local dwarf galaxies5

In this section I am going to give a brief summary about the most important aspects of the Milky Way satellites and the Local Group. By reviewing the most key seminal papers on this topic, I am going to present the most relevant advances, the current status of our knowledge about the Local Group along with the biggest uncertainties that remain to be addressed. The main objetive of this introduction then, is to put our observations (that are going to be described in the following section) in context and highlight how important they are to help solve relevant aspects of modern astrophysics.

4With the exception of the M33 galaxy which is associated to M31 but is not a dwarf but a spiral galaxy 5These galaxies represent the local counterparts of other galaxies with similar characteristics found in other groups or clusters of galaxies, denominated dwarf galaxies

7 2.1.2 Connecting Dwarf Galaxies with Dark Matter Halos and the Missing Satellites Problem

The most striking difference between the Local Group that Hubble pictured in the 30’s and the Local Group that we conceive today is the number of galaxies that are observed to reside in it. The exponential increase in the amount of local dwarf galaxies discovered has revolutionized the field, and constantly changes our view about these systems and how we relate them with different topics in astrophysics, like Galaxy formation.

One of the ﬁrst trascendental links between properties of stellar populations in the Milky Way and a formation scenario for our Galaxy was made by Eggen & Lynden-Bell (1962; often known as the ELS paper for the initials of their three authors). These authors measured 3D velocities for 221 stars in the Galaxy and with that developed a collapse model for the Galaxy. In this model, the formation of the Milky Way started in the early Universe (around 10 Gyr ago) with the oldest stars forming from gas falling radially to the center and collapsing from the Halo to the disk of the Galaxy. After this, an equally important paper describing a somehow counterposed formation scenario for the Galaxy, was presented by Searle & Zinn (1978), who studied the photometrical and spectroscopical properties of 177 red giant branch stars in 19 globular clusters relatively far from the Galactic center. Based on the properties of these stars, the authors proposed that the globular clusters in the outer Halo were formed in proto- galactic fragments that started falling to the Galaxy in dynamical equilibrium after the central parts were formed, and are still falling in the present. These two papers are an important part of the basis of Galaxy formation studies, and the hierarchical accretion scenario proposed in Searle & Zinn (1978) represents one of the most fundamental theories that relate now the Milky Way satellites with the formation of our Galaxy.

One of the most important contributions to the study of Milky Way satellites and their relation with structure formation, was provided by Kauffmann et al. (1993) who developed a hierarchical clustering model for the formation of structures in the Uni- verse. In the scenario reproduced by their simulations, a galaxy forms in a dark matter halo, by converting gas into stars. Then, these small halos would merge due to dynamical friction with a larger halo, at which point the first galaxy loses its source of new gas and becomes a non-dominant object in a larger group or cluster. This picture describes in general terms the current perspective of the astronomical community for the formation of large galaxies like the Milky Way. The long standing acceptance of the scenario described by Kauffmann et al. (1993) is due to the accurate reproduction of the properties of stars observed in the Milky Way and other galaxies, and also thanks to similar simulations that came after, that showed similar results (e.g. Klypin et al., 1999; Bullock et al., 2001). In the work of Kauffmann et al. (1993) they compared the −1 halos with circular velocities of vc = 220 km s , with the properties of the Galaxy to set the free parameters that regulate baryonic physics (like efficiency of star formation, feedback an merging rate) and the ones describing the cosmology. These parameters

8 −1 were then used to compare the sub halos present in a vc = 1000 km s dominating halo, with the galaxies observed in the Virgo cluster. Here the authors found statistically signiﬁcant agreements in terms of luminosity, color, morphology and gas content of the galaxies. Similar results were obtained when comparing the results from the simulation to the properties of the Milky Way satellites. This paper came to solve a long standing problem in cosmology in terms of comparing observed galaxies with dark matter halos, which are a natural result in a Universe ruled by Λ-CDM cosmology. At this point, galaxy merging was already believed to be responsible for some important observables in galaxies like starburst, active galactic nuclei (AGN), and radio activity, and it was also thought they could change the Hubble type of a galaxy. One of the most important results from this simulation was that most sub halos and the galaxies that reside on them, were able to survive the merger process to become part of a group or a cluster. They also found a trend in the properties of galaxies conforming groups or clusters in the sense that the less luminous ones are more numerous, older, redder, and more gas deﬁcient, a relation that would only break for the brightest galaxies. Nev- ertheless, an important discrepancy was found when comparing halos with observed galaxies, in the sense that the standard Λ-CDM model predicted too many haloes and would results in a B-band luminosity density of the Universe that is two times higher than observed. This long standing issue of Λ-CDM cosmology was latter denominated as Missing Satellites Problem, referring to (dwarf) satellites that should be there according to simulations, but are somehow missed by observations, or simply do not exist.

With the following years the Missing Satellites Problem started to gain great attention from the astronomical community. An important reason why this problem was so critical and challenging, was that it implied that the cosmological theory that was so successful reproducing the large scale structure of the Universe, had a problem reproducing a fundamental observable of small scale structure, namely, the number of dwarf galaxies orbiting the Milky Way. Speciﬁcally, Λ-CDM has accurately reproduced CMB observations (Komatsu et al., 2011), galaxy clustering (Reid et al., 2010), or the stellar streams observed in the Milky Way or M31 (e.g. Majewski et al., 2003; McConnachie et al., 2009). This suggests that structure formation is indeed hierarchical. is hierarchical. This indicates that, al least, the Universe looks similar to the product of Λ-CDM, from the scale of horizon to about ∼ 50 kpc.

Despite the success of Λ-CDM, the missing satellites problem persisted, and was confirmed in the works of Moore et al. (1999) and Klypin et al. (1999). In the latter work, the authors confirmed that galaxies are formed by hierarchical assembly and this process is not fully efficient in destroying the accreted satellites as observed in the Lo- cal Group. As many other studies, they compared satellites and observed galaxies by measuring their circular velocity distribution function (VDF). The VDF corresponds to the cumulative distribution function of the maximum circular velocity of the systems (galaxies or satellites) present, in this case, in the Local Group. The maximum circular velocity represents the peak in the rotation curve, and can be measured, in principle, in both simulated dark matter halos and real dwarf galaxies, even though the latter

9 might not have stars moving in circular orbits. By comparing the VDF of simulated halos and dwarf galaxies in the Local Group, the authors found large discrepancies, and concluded that a large number of satellites had to be missed from observations, confirming what was proposed by Kauffmann et al. (1993). Klypin et al. (1999) discussed the possible reasons for this discrepancy. The first possibility was associating some satellites with the high velocity clouds observed in the Local Group. The second possibility proposed, was the existence of dark satellites that failed to accrete gas and form stars, either because of gas expulsion due to supernovae winds or because of gas heating by the intergalactic ionizing background. In the following years, several solutions to the missing satellites problem were proposed. Besides the reionization hypothesis, described in Klypin et al. (1999), that gain considerable acceptance (Bullock et al., 2000; Somerville, 2002; Benson et al., 2002), other solutions implied, for example, that the dwarf galaxies had suffered severe tidal stripping, and hence, were much more massive in the past (Mayer et al., 2001; Kravtsov et al., 2004). Moreover, proposed solutions to the missing satellites problem include a revision of the standard Λ-CDM paradigm. These hypothesis include, for example, modifying the power spectrum at small scales (e.g. Kamionkowski & Liddle, 2000), or invoking “warm” instead of “cold” dark matter (e.g. Bode et al., 2001).

In those years, the Sloan Digital Sky Survey (SDSS; York et al. 2000) was released. This photometric and spectroscopic catalogue dramatically changed the observations of Milky Way satellites, and as a consequence, provided trascendental information for the missing satellites problem. This survey reported the discovery of a new class of galaxies that were present in the Milky Way halo and which were smaller, dimmer and of lower surface brightness than any other galaxy ever discovered. These galaxies were denominated ultra-faint dwarf galaxies, and their extremely low stellar counts and sizes started defying the distinction between dwarf galaxies and globular clusters. Then, to confirm that the ultra-faint objects discovered in SDSS were actually galaxies, a proper kinematic characterization of these objects was needed, to look for the presence of dark matter, and discard the possibility that these objects, or at least a fraction of them, were globular clusters. The most complete and important kinematic study of these objects came later in the work of Simon & Geha (2007). In this article, the authors presented Keck-Deimos spectroscopy of stars from eight of the newly discovered ultra- faint dwarf galaxies, using 18 to 214 stars in each case, to measure the radial velocity dispersions. The first important contribution of this work was the confirmation that practically all these systems were dark matter dominated, and moreover, they exhibit mass-to-light (M/L) ratios approaching 1000 M /L , the highest ever measured for a galaxy. These measurements confirmed the galactic nature of these systems, and proved that, for the majority of them, tidal effects from the Milky Way were unable to explain the high velocity dispersions. The discovery of the ultra-faint dwarf galaxies and their later confirmation as dark matter dominated systems, proved empirically that there were actually satellites of the Milky Way that had been missed in previous observations. However, the new galaxies discovered were not numerous enough to reconcile the amount of satellites predicted by simulations and the dwarf galaxies observed in the Milky Way halo. Nonetheless, the measurements provided by Simon

10 & Geha (2007) provided a reliable starting point to compare the masses of simulated dark matter satellites with the ones of dwarf galaxies. These authors showed that, after correcting for sky coverage of SDSS, the discrepancy between halos predicted and galaxies observed significantly alleviates. Nevertheless, there is still a factor of four discrepancy over a wide range of masses. Then, the authors tried different hypothesis to explain the discrepancies and found that if the star formation is strongly suppressed after reionization for low-mass dark matter halos, the circular velocity function of CDM sub-halos and Milky Way satellite galaxies could be reconciled. The hypothesis of reionization as the reason for the missing satellites problem (explained for example in Bullock, 2010) is based on the hypothesis that the first generation of stars expelled high levels of high energy radiation that ionized the gas in the Universe. This would have reduced the possibility of cooling via neutral hydrogen, but most importantly, −1 evaporated the gas from low mass halos (vcirc . 10 km s ), because gravity is not able to overcome thermal pressure. Reionization is also believed to have affected halos −1 with slightly larger masses (vcirc ∼ 10–30 km s ), by preventing the accretion of fresh gas and promoting evaporation from the shallow edges of the potential wells, that were then replenished by gas from the central regions. All these effects imply that reionization would have strongly suppressed the star formation in low mass halos, and as a consequence, a large fraction of the smaller dwarf galaxies would have luminosities and surface brightness too low to be detected by current surveys, or could have even prevented any star formation in some of those systems, thus, producing dark galaxies.

The missing satellites problem and the constant comparison between properties of dwarf galaxies and simulated dark matter halos, are a great example of how Milky Way satellite galaxies can provide important information about galaxy assembly, baryonic physics involving star formation, such as, gas cooling, supernova (SN) feedback, evaporation), and can even constrain details about the early Universe, like the process of reionization. Nevertheless, the use of Milky Way satellites to constrain different astrophysical scenarios goes well beyond the missing satellites problem. For example, the tidal disruption of Milky Way satellites can trace the Galactic potential. In Law & Majewski (2010), the authors presented a N-body simulation that models the disruption of the Sagittarius dwarf galaxy, which could successfully reproduce the distances, radial velocities, and angular positions of the stars in its dynamically young (3 Gyr) tidal debris streams. In this study, it was proposed that the potential of the dark matter halo could be described as a triaxial shape that was slightly rotated about the Galactic Z-axis. While their inclined Halo reproduced the data of Sagittarius debris, it was not motivated in the CDM paradigm, and according to the authors it might simply serve as a numerical trick that mimics the effect of an undiscovered alternative, such as the influence from the Magellanic Clouds potential. More recently, the high dark matter density expected in some dwarf galaxies have motivated searches of “direct” dark matter detections coming from these galaxies. The idea is that annihilation of dark matter particles can be perceived through the emission of gamma rays, and that can, at the same time, confirm the high dark matter densities (through a method completely independent of velocity dispersions), and constraint the properties of the dark matter particles. This hypothesis has motivated the astronomical community in

11 the last years, and it has started to hint for the ﬁrst possible results (Hooper & Linden, 2015).

All these and several more approaches that have related the properties of Milky Way satellites with structure formation, key aspects of dark matter properties, and conditions from the early Universe, motivated the community to rename the field from “stellar populations” or “Galactic astronomy” to “near-field cosmology” in the year 2002 with the Freeman & Bland-Hawthorn (2002) paper, to state explicitly that this topic is far from being isolated and independent from other fields, and in turn, is closely related to fundamental aspects of the formation and evolution of the Universe.

2.1.3 Scaling relations in dwarf galaxies and connection to formation mechanisms

In the previous section, we saw how general properties of Milky Way satellites, or even just a proper census of these systems, can hint important things about structure assembly and cosmology. In this section we take one step further and we analyze how the different properties of individual local dwarf galaxies are related with each other. These trends followed by different systems are called Scaling Relations, and can tell us valuable information about the formation and evolution of these galaxies. These relations can also help us classify the different types of local dwarf galaxies and check if they correspond to different formation mechanisms, different environments or just artificial classification. Historically, the main types of dwarf galaxies have been the dwarf irregulars (dIrr) or late-type dwarfs and the early type dwarfs (e.g. Mateo, 1998). At the same time, the early-type dwarfs have been classified as dwarf ellipticals (dE) and dwarf spheroidals (dSph). Then, the ultra-faint dwarf galaxies discovered by the SDSS might represent an extension of the dSph or a completely separated class (Tolstoy et al., 2009). In very broad terms, late-type dwarfs are distinguished from early-types because the first ones have significant current star formation, whereas the latter ones do not. In turn, dE galaxies are larger and more luminous than dSph galaxies. Also, as the name suggests, dSphs display more spherical shapes than dEs. Finally the ultra-faint dwarf galaxy term was first used to determine the galaxies discovered in the SDSS, however, some evidence suggest that the difference between these galaxies and the ones previously discovered might be given by their assembly history more than a matter of how hard to detect they are (Brown et al., 2012). Throughout this section, we are going to review the most important properties of the different types of dwarf galaxies to look for connections or intrinsic differences between them that may hint us information about their formation mechanism and evolution.

The characterization of the local dwarf galaxies is extremely important since they are the most common type of galaxies in the Universe (Kauﬀmann et al., 1993), and they are believed to have been much more numerous in the past (Ellis, 1997). Moreover, these galaxies represent a unique opportunity to study a well deﬁned sample of low

12 luminosity galaxies, and their properties can be studied with more detail than any other type of galaxy, thanks to their proximity to us.

One of the most important works in the field of dwarf galaxies is the review of their properties by Mateo (1998). In this work, the author presented a census of local dwarf galaxies based on the most recent distance and velocity measurements at the time. +6 Up to this point the number of dwarf galaxies known in the Local Group was 38−2, and it was already certain that the census was not complete. In this work, important differences are presented between early-type and late-type dwarf galaxies. First, many HI properties show a clear progression from dIrr to dSph galaxies. The neutral hydrogen mass over total mass for galaxies varies drastically with type. For dIrr, it goes from 7 to 50%, in transition galaxies is 1–10% and in dSph is none or . 0.1%. Besides that, historically dIrr are fitted by exponential profiles, whereas early-type dwarfs are fitted with King profiles, as globular clusters. Photometrical properties also can help us distinguish these types of galaxies, in the sense that dIrr are more luminous and bluer than dSph.

In their article, they also mentioned a relation between luminosity and abundance, which by that moment had been known for a while for dIrr (e.g. Skillman et al., 1989) and dSph galaxies (e.g. Caldwell et al., 1992). This relation shows that more luminous systems have higher metallicities. If the higher enrichment were attributed to higher potentials that can retain metals more eﬀectively, the relation would be tighter when comparing dynamical (instead of stellar) mass with metallicity. If instead, for example, the higher enrichment was given by higher gas concentrations in the past, the relation would be tighter when comparing metallicity with surface brightness. However, as proven by Caldwell et al. (1998), the main parameter driving the increase in metallicity is indeed luminosity, which is one of the reasons why this relation has brought so much attention, and at the same time has been so hard to interpret. In their review, it is claimed that when early type and dIrrs are plotted together in an abundance v/s MV diagram, early type galaxies appear to be more metal rich for the same luminosity, and it is also noted that there might be a discontinuity at a luminosity corresponding to MV ∼ −13, above which most dIrr live. The latter observations, suggested intrinsic diﬀerences between early and late type dwarf galaxies, nevertheless, more recent studies have shown that discrepancies between dIrrs and dSphs in this diagram are no longer observed (see for example Kirby et al., 2013).

Other very important trend that dwarf galaxies follow (highlighted by Mateo, 1998) is the often called position v/s morphology relation, which shows that early-type dwarf galaxies are preferentially located closer to the Milky Way than late-type galaxies (ﬁrst noted by Einasto et al., 1974). The spatial segregation of dSph and dIrr appear consistent with the hypothesis that gas has been removed from the former systems, whether by internal processes like star formation (e.g. De Young & Heckman, 1994), or external inﬂuence like ram-pressure stripping (e.g. van den Bergh, 1994). This hints for a transition process between dIrr and early type dwarf galaxies, that would occur when the former loose their gas content and stop their star formation. However, Mateo (1998)

13 indicates that there are fundamental problems associated to this scenario. Besides the already mentioned systematical diﬀerence in the luminosity-metallicity relationship, Hunter & Gallagher (1985) and Bothun et al. (1986) found that the present-day surface brightness of dSph were inconsistent with evolved dIrr. Additionally, James (1991), found that the structural diﬀerences between dIrr and dSph in the Virgo cluster, were inconsistent with a common origin. Nevertheless, more recent studies like (Kormendy & Bender, 2012), have provided valuable evidence hinting that dSphs are indeed transformed dIrr galaxies.

Another important difference between dIrr and the other local dwarf galaxies, is the presence of rotation, which is always negligible in early-type dwarfs. All this evidence justify intrinsic differences between early and late type dwarf galaxies, although the physical processes responsible for these differences are still uncertain. The more popular possibilities up to this point, stated by Mateo (1998), were angular momen- tum, strength of early star formation, and environment, although current observations present major challenges to all these possibilities.

Kinematic data have also provided important information about the different types of dwarf galaxies. With large multi-epoch data for different galaxies, the errors in these measurements significantly decrease with respect to the earliest attempts. By the moment Mateo (1998) was released, the M/L ratios were reliable to a factor of 2, and the observations showed an anti-correlation of M/L with luminosity, and a systematically higher value of M/L for dIrr at the same absolute magnitude. However, at least two problems might bias the comparison between early-type and late-type M/L ratios. The first one is that early-type mass measurements come from the velocity dispersion of the oldest stars, whereas for late-type dwarfs, the mass is measured from the gas component or circular velocities at large radii, which can lead to an offset between both types of data. The other problem is that for deriving virial masses of the pressure supported early-type dwarfs, the King formalism assumes that mass follows light (i.e. the light distribution is proportional to the mass distribution), and hence it may be underestimating the characteristic radius of the system, because dark matter might be more extended. This, would lead in turn to an underestimate in the M/L ratios for these systems, a problem that would not be present for dIrr, where circular velocities are measured at large radii. These and other observational caveats, represent major challenges when interpreting M/L ratios in local dwarf galaxies, and I will present later on this section how more recent observations have changed the results and their interpretation. With the data available at the moment, Mateo (1998) fitted a curve to the M/L v/s MV diagram, and concluded that the early-type dwarfs had a luminous component with M/L=2.5, and a constant dark matter component with a 7 mass of MDM ∼ 10 M . The hypothesis of a constant mass dark matter halo caused great impact on the field, and I am going to present later how it has evolved with time with recent observations.

Other observations that have provided valuable information about local dwarf galaxies, are the signatures of tidal inﬂuence from the central dominant galaxy (Milky Way

14 or M31). The most destructive examples are Sagittarius (e.g. Ibata et al., 1997) in the Milky Way and NGC 205 (e.g. Bender et al., 1991) in the vecinity of M31. Additionally for the dwarf galaxies closer to the Milky Way, the tidal radius or the ellipticity has been found to strongly correlate with the strength of the tidal field (Irwin & Hatzidim- itriou, 1995), supporting the hypothesis that the Milky Way is the main responsible for molding the different types of Milky Way satellites. The general picture for tidal disruption of dwarf galaxies in the Milky Way halo is the following (see for example Johnston et al., 1995; Muñozet al., 2008). During strong interactions, stars are lost from dwarf galaxies in leading and trailing orbits that increase the volume of the galaxy and that follow stream motions. Extra tidal stars can be seen, although the majority remain bound, and the galaxy keeps their central velocity dispersion unaffected. At later stages the galaxy becomes very elongated, but not necessarily parallel to the orbit. At very late stages, it becomes a long thread stretched along its orbit with a small clump as the remnant of the original galaxy, and just at the very end the central velocity dispersion exceeds its virial value even in no DM models. In works like Mateo (1996), and Unavane et al. (1996), authors explore the possibility that a significant fraction of the Milky Way Halo could have formed from disrupted dwarf galaxies. One caveat that this work presents, is that survivor present day dSphs represent systems that could form stars for a longer period than the destroyed ones. They should also follow orbits different from the ones that might have formed the Halo. Therefore, present day dSph are not expected to match exactly the Halo population. With this in mind, they concluded that up to 50% of the Milky Way Halo could have formed from disrupted dwarf galaxies.

Now that I have presented the most important properties discovered for local dwarf galaxies at the moment the review of Mateo (1998) was presented, I am going to highlight some of the most relevant contributions made in the field in the more recent years. As we saw earlier, the work of Simon & Geha (2007) provided key information to confirm the galactic nature of the ultra-faint systems discovered by SDSS, and to help solve the missing satellites problem. This work also provided significant information about the properties of the ultra-faint dwarf galaxies. These authors found that the velocity dispersions measurements indicated that the total masses of the ultra-faint dwarf galaxies were correlated with luminosity, in opposition to what they got for the slightly brighter “classical” dSphs, which were compatible with having the same mass for different luminosities (as it was first pointed out by Mateo et al. 1993). The authors inferred from their data that the minimum mass for dwarf galaxies, if existed, had not been reached by observations yet. These observations of Simon & Geha (2007) were in opposition to what was found a year later by Strigari et al. (2008), who found that all dwarf galaxies (including the ultra-faints) had a common mass scale within 300 pc from the center, proving that this matter was not closed yet.

The work of Simon & Geha (2007) contributed signiﬁcantly in proving that the properties of dwarf galaxies could be even more extreme than previously thought, when the ultra-faint dwarfs were also taken into account. Besides being the least luminous and smaller galaxies ever discovered, these systems were found to have the highest M/L

15 ratios and one of the most metal poor stellar systems in the Universe (with average stellar metallicities between −2.3 and −2.0). With these new abundance measurements, the authors could expand the already known relation between metallicity and stellar mass found in dwarf galaxies (Caldwell et al., 1998), in a factor of 30 in luminosity, and they also proposed that systems that deviated from this trend (like Coma Berenices and Ursa Major II according to them) could be interpreted as evidence that they were much more massive in the past. Finally, Simon & Geha (2007) concluded that the galaxy Ursa Major II is in late stages of tidal disruption, and Coma Berenices might have lost part of its mass according to its high metallicity for the corresponding luminosity.

A further important leap in the field of local dwarf galaxies was presented by Martin et al. (2008a), who provided a significant improvement on the observations, by applying a maximum likelihood method for deriving the structural parameters of the ultra-faint dwarf galaxies. These authors proved that using the same methods to derive luminosity or morphological parameters than the ones used for the “classical” dSphs, could lead to a significant bias. For example, deriving morphological parameters through the intensity-weighted second moments technique required a previous smoothing of the mass for low stellar count systems, which could lead to an artificial rounding of the system. On the other hand, deriving the luminosities of these stellar systems by just adding the luminosities of individual stars can be severely affected by single bright stars. Therefore, these authors re-derived the structural parameters of these systems by using a maximum likelihood algorithm that directly fits the stellar density profile without smoothing it. The technique starts by assuming that the positions of the stars in the SDSS sample of each galaxy, have been drawn from a spatial model distribution that is described by j parameters. Then, the general purpose is to find the set of parameters for which the observations become most likely. This means maximizing the likelihood function, which is the multiplication of the individual probabilities that each star was observed, given the spatial distribution defined by the j parameters. These parameters determine the central position, position angle, ellipticity, half light radius, central surface brightness, and background level, which are all derived at the same time. By restricting the possible solutions to the ones that match the observed total number of stars observed in SDSS (above some threshold magnitude) they obtain the number of stars that belong to the system. With this value they construct fake CMDs for each object populating the different regions according to a standard initial mass function (IMF), a stellar population derived from SDSS data, and the photometrical errors associated to the survey. Finally, using these modeled CMDs they calculate the luminosity of the system, the stellar mass, and their associated errors.

With these results, the authors confirmed that the size difference that existed previously between dwarf galaxies and globular clusters became blurred at luminosities lower than MV ∼ −5 (as previously observed by Belokurov et al. 2007 and Gilmore et al. 2007), however, they recall that spectroscopic studies show that, unlike globular clusters, the least luminous galaxies are extremely dark matter dominated (Simon & Geha, 2007). The work of Martin et al. (2008a) also confirmed that dwarf galaxies are

16 significantly more elongated than globular clusters, which have ellipticities always lower than 0.25. They also found that dwarf galaxies with fainter magnitudes are flatter, and the faint and bright dwarf galaxies subsamples yield a very low (0.4%) probability that both ellipticity distributions are drawn from the same mother distribution. Finally, Martin et al. (2008b) showed that generally the apparent distorted morphology of low luminosity dwarf galaxies can be attributed to shot noise. This does not necessarily means tidal signatures are not there, but they say that it has to be confirmed by deeper data instead of individual inspection of the few currently observed stars. This proved quantitatively that conclusions based on the few stars found generally for ultra-faint dwarf galaxies have to be taken with extreme caution if one wants to avoid getting significantly biased by shot noise. In this work, the authors tried to explain the physical reason for the higher elongation of the lowest luminosity dwarf galaxies. Even though none of the possibilities can easily explain the observed trend, the less problematic hypothesis, according to the authors, is that the higher ellipticities are the result of tidal elongation from the Galaxy.

The conclusions derived by Simon & Geha (2007) and Martin et al. (2008a) point out to a scenario were even though tidal eﬀects from the Milky Way might be responsible for some observed properties in local dwarf galaxies, the majority of the very high M/L ratios and their shapes could be explained without invoking late stages of tidal disruptions in these systems. This stands in opposition with previous claims such as the one in Kroupa (1997) where dark matter content in dwarf galaxies is denied, and the high velocity dispersion measurements are strictly associated to interactions with the Milky Way. Instead, the more recent view, does not use the tidal interaction argument to discard the DM content, but explains the properties of the majority of the present-day Milky Way satellites as the result of dark matter systems undergoing mild episodes of tidal interactions that are far from total disruption.

To look over some of the most important aspects of the study of local dwarf galaxies, I am going to summarize now the results present in McConnachie (2012), which is one of the most updated and comprehensive works about positional, structural, and dynamical properties of local dwarf galaxies. This study also includes some dwarf galaxies located around the Local Group, to study the properties of dwarf galaxies that have not been significantly affected by the influence of neighbor giant galaxies, and compare their properties with local dwarf galaxies. In this work, the author starts by studying the membership of the galaxies to each sub-group (Milky Way & M31) and to the Local Group, by studying the radial velocities of each dwarf. McConnachie (2012) defines the Milky Way satellites as the galaxies whose 3D velocities were lower than the Galaxy escape velocity at that location, and with galactocentric distances smaller than the virial radius of the Milky Way, estimated in 300 kpc (Klypin et al. 20026). Is very remarkable that the author finds that there is an apparent gap in the distribution of the galactocentric distances of the satellites of the Milky Way from 280 kpc to 400 kpc, which coincides with the location of the Milky Way virial radius.

6In this article the authors deﬁne the virial radius as the one within which the mean density of the dark matter halo is 340 times larger than the critical density of the Universe

17 This behavior was also observed for the satellites of M31, and suggest that the deﬁned virial radius indeed denotes a physical limit for these sub-groups. McConnachie (2012) also highlights the lack of satellites at galactic longitude with values | b |. 20, which is in large part due to high obscuration from the Milky Way, and would not improve signiﬁcantly with the advent of new surveys.

An important point that is highlighted in McConnachie’s work, and that is worth keeping in mind is that, despite their huge utility, global structural parameters like luminosity or half light radius might not reﬂect the internal complexity that has been found in individual dwarf galaxies, and also dynamically diﬀerent sub populations in dwarfs make global velocity dispersion or rotation measurements incomplete. This reason might be one of the largest sources of uncertainty and scatter in the scaling relations found in local dwarf galaxies, and which could improve by decomposing the galaxies in several components (see for example McConnachie et al. 2007 for And II or Vanseviˇciuset al. 2004 for the case of Leo A).

As many other previous works, McConnachie (2012) plotted a diagram of MV against half light radius for all the satellites in the Local Group, plus some nearby galaxies and the Galactic globular clusters. As most authors concluded, McConnachie (2012) pointed out that globular clusters and Milky Way dwarf satellites can be (somewhat) distinguished in this 2D plot, however, the 1- dimensional distributions of size and luminosity overlap for galaxies and clusters. It had been pointed out earlier by Mc- Connachie & Irwin (2006) that Milky Way satellites were in average larger than the M31 population for the same luminosity. Nevertheless, Brasseur et al. (2011) concluded with a larger sample that the mean relations of Milky Way and M31 satellites are statistically consistent with each other. In the work of McConnachie (2012), is only concluded that the small number of galaxies in each sub-group make any comparison hard, and that perhaps the only thing that can be noticed is the lack of a strong correlation for local dwarf galaxies with luminosities larger than MV ∼ −7. However, the satellites with MV & −8 follow a well defined straight line that is extremely similar to a line defining a common surface brightness within the half light radius for these systems. This suggests that selection effects might be driving the size luminosity diagram for the dimmest satellites. When plotting the absolute magnitude against surface brightness (both central and averaged inside half light radius) the author sees that for objects with luminosities corresponding to MV . −9, there is a clear correlation between both parameters, however, for dwarfs dimmer than MV ∼ −8.5, the surface brightness is practically constant, which is remarkable considering the change in luminosity of about a factor ∼ 600. The selection effect for these and other trends are very hard to quantify and complex (depending on distance, magnitude and surface brightness), however, they may not be driving these scaling relations. For example, Koposov et al. (2008) pointed out that galaxies as faint as MV ∼ −4.4 could be recovered at distances of ∼ 180 kpc even if their central surface brightness are as faint as ∼ 30 mag arcsec−2. Moreover, there is tentative evidence that M31 satellites follow the same trend as Milky Way satellites. Therefore, there exists the possibility that the“break” in scaling relations around MV ∼ −9 may by real. If this is confirmed,

18 it implies that formation or processes of feedback/evolution set a lower limit for the surface brightness of dwarf galaxies.

Other important relation shown on McConnachie (2012), is the distance to the host (translated also to free-fall time) against the fraction of HI to visual luminosity, for galaxies in the Local Group. This diagram shows the position-morphology relation of Local Group galaxies, first noted by Einasto et al. (1974), who observed that gas- deficient dSph are preferentially found close to the host, while gas-rich dwarfs are located farther away. This effect has also been observed in other galaxy groupings (see for example Bouchard et al., 2009). This behavior has often been taken as the result of external effects on the evolution of dwarf galaxies, and it has also been reproduced in simulations (e.g. Mayer et al., 2006).

As done in several previous studies, the relation between luminosity and mass for dwarf galaxies was also analyzed by McConnachie (2012). To do this, the author calculated the dynamical mass within the half light radius of each system, Mdyn(≤rh). This radius was chosen as the limit up to which the mass is calculated, because, as proven by Walker et al. (2009) and Wolf et al. (2010), the mass within this limit can be obtained with mild assumptions on the spatial variation of the velocity anisotropies, something that would not hold for other, more arbitrary radii. The relation between Mdyn(≤rh) and MV shows there is a well defined power law relationship for local dwarf galaxies. Then, when the M/L ratios within the half light radius are compared with absolute magnitude, another power law is observed, only this time it increases with absolute magnitude. These plots hint that the dynamical mass of local dwarf galaxies grows with Lα, where L is the luminosity of the system and α is a number between zero and one. This result supports the one presented in Simon & Geha (2007) that indicated that a minimum mass for dwarf galaxies (and a maximum M/L), if exist, has not been reached yet by observations. On the other hand, it is somehow opposed to the conclusion of Strigari et al. (2008) who claimed that dwarf galaxy dark matter halos share a common mass scale within 300 pc. However, given the wide range of sizes of local dwarf galaxies (some significantly far from 300 pc) and the advantages of measuring the mass within the half light radius, the relation found in this study has much more physical meaning than the one presented by Strigari et al. (2008). Nevertheless, is important to keep in mind that diagrams like these, may suffer from significant bias, given the selection effect in dwarf galaxies, and the caveats in transforming velocity dispersion to masses. For the latter, reliable and small velocity uncertainties are needed, along with multi-epoch data for correcting for binaries and a proper dwarf-giant stars separation to avoid contamination from the Milky Way.

Finally, McConnachie (2012) computed the luminosity against the stellar metallicity for local dwarf galaxies, with which the author conﬁrmed the trend observed previously by Caldwell et al. (1992) in classical dSphs and Tremonti et al. (2004) in 53000 star forming galaxies in the SDSS data. As expected, the relation gets much cleaner when only including the galaxies for which the stellar metallicity has been derived spectroscopically, which proves the systematical bias introduced when deriving the metallicity

19 indirectly. Even though the relation hold for all the luminosity range covered by dwarf galaxies, McConnachie (2012), highlights that a possible break might be present in the trend for systems dimmer than MV ∼ −8. With this information, the author specu- lated that given the break in this relation coincided with the location of the break in the surface brightness v/s luminosity presented earlier, the density of baryons might be the one dominating the enrichment levels of each galaxy.

The most recent contribution to the field of dwarf galaxies is the work of Drlica- Wagner et al. (2015), where they presented the discovery of eight ultra-faint galaxy candidates from the second year of data of the Dark Energy Survey (DES), which add to the eight candidates that were discovered in the first year of data of this survey. The new ultra-faint systems were found using three independent techniques, and are identified as overdensities of stars, consistent with the isochrone and luminosity function of an old metal-poor population. When plotting these new systems in a MV v/s rh diagram, they cover a region that overlaps with the one spanned by the previous ultra-faint dwarf galaxies, discovered in the SDSS data. However, there is tentative evidence that some of them might have even dimmer central surface brightness values, −2 reaching up to µ0 ∼ 30 mag arcsec . If this is the case, it would go against the possible “floor” in central surface brightness of faint dwarf galaxies, and the concentration of −2 central surface brightness values observed close to µ0 ∼ 27.5 mag arcsec would be just due to observational bias. In any case, further observations are needed, first of all, to confirm which one of the DES candidates are dwarf satellite galaxies and which are false detections (most likely globular clusters), and then to further constraint the structural properties of these systems. The DES dwarf candidates from both years, reach magnitudes as faint as MV ∼ 0 and sizes as small as rh ∼ 10 pc. The confirmation of whether DES has extended the properties of known galaxies to even smaller and fainter regimes, leaves pending the confirmation of the galactic nature of their candidates. Although, one thing is starting to be clear, and that is the fact that separating galaxies and globular clusters solely based on their position in a MV v/s rh diagram is loosing validity.

Other fundamental property about the new DES candidates, is that they are mostly found around the Magellanic Clouds. By analyzing this particular spatial distribution of the satellites, the authors claim that the DES data alone can exclude a homogeneous distribution of satellites in the Milky Way halo, which in turn, lead them to conclude that a large fraction of the DES candidates are associated with the Magellanic System. They also predict that the full sky might hold ∼ 100 ultra-faint dwarf galaxies similar to the DES candidates and that ∼ 20–30% would by associated with the Magellanic Clouds. This idea of “satellites of satellites” has been studied for a long time and represents a natural prediction of galaxy assembly in the standard Λ-CDM cosmology. For example, the possibility of the existence of Magellanic System satellites has been discussed in Lynden-Bell (1976), and recently with the discovery of the DES candidates different authors have analyzed this possibility (e.g. Koposov et al., 2015; Deason et al., 2015). Although, for the first time, DES has provided observational evidence to this long standing hypothesis and the confirmation and characterization of these galaxies

20 could provide valuable information to test the efficiency of star formation in low-mass subhalos and the impact of environment on the history of these systems (Wetzel et al., 2015). Drlica-Wagner et al. (2015) also concluded that the DES data will not increase significantly in spatial coverage but it will on photometrical depth. This will most likely increase the number of satellites discovered in this survey, although at this depth the stars/galaxy separation can become a limiting problem. The latter has motivated the development of improved classification algorithms, as well as alternative search techniques that involve time dependence or radial velocities.

The exponential increase of systems discovered in the last years in the Milky Way halo and the Local Group, and the great number of questions that have arose through the study of these systems have even defy the very definition of the term “galaxy”. This shows how fundamental the information these systems provide might be. The most clear example of a critical view of this topic is the article of Willman & Strader (2012) titled “Galaxy Defined”, in which the authors come up with the following definition for a galaxy:

A galaxy is a gravitationally bound collection of stars whose properties cannot be explained by a combination of baryons and Newton’s laws of gravity.

One of the main advantages of this definition, is that it does not depend on the cosmological model of the Universe, and would stand even if the dark matter theory and/or MOND are disregarded. According to these authors, in practice, a diagnose of a system class with the current observation, could be made with a combination of the location in the MV and rh diagram, kinematical measurements of the M/L ratios and the metallicity spread measurement. Specifically MV and rh would be particularly helpful for separating galaxies in the high luminosity regime (MV . −8), while the metallicity spread measurements would help distinguish these systems in the opposite regime. The latter is proposed in the basis that for systems with luminosity lower than MV ∼ −10, galaxies show metallicity dispersions higher than 0.3 dex and clusters lower than 0.1 dex. These authors highlight that, even though the particular diagnos- tics used for discriminating between galaxies from other stellar systems could change with new data and theories, their definition of “galaxy” can stay unchanged, holding its consistency.

Up to this point I have summarized a small fraction of the large number of trascendental works made on the field of local dwarf galaxies or “near-field cosmology”, selecting the ones that I thought were the most relevant or comprehensive. By reviewing these articles I have highlighted the huge importance of studying the properties of these systems, whose characterization can provide crucial information for galaxy assembly, conditions of the early Universe, baryonic physics, and the nature of dark matter, among other topics. The main motivation for constructing the catalogue we built, was helping to better characterize these systems and with that contribute to the number of open questions regarding local dwarf galaxies. The excellent quality data provided by this catalogue has already presented the opportunity to develop different works about

21 some aspects of these system (Muñozet al., 2012a; Santana et al., 2013), while others are in preparation (e.g. R. R. Muñoz2016, in preparation). The catalogue presented in this chapter also represents the angular stone from which I took the data to construct the study presented in Chapter 5. That work has the main results of this thesis and takes advantage of the information in all the other Chapters. All these works represent just a small fraction of the total number of studies that will be constructed from this catalogue, in order to benefit from the full capacity of the data presented here.

2.2 Complete Catalogue: General Remarks

During the last years, the collaboration group I am part of, constructed a catalogue aimed at surveying nearly all the stellar overdensities in the outer halo of the Milky Way known up to the year 2012, regardless of their classification. The region covered by this catalogue includes everything farther than 25 kpc from the Milky Way center, but located within the virial radius of our Galaxy (∼400 kpc). The 25 kpc limit was chosen because inside this region the stars have different metal content and kinematics (Carollo et al., 2010). From the region described above, the Magellanic Clouds and the Sagittarius dwarf spheroidal have been excluded mainly due to their large extensions. With all those considerations the final sample includes a total of 44 objects. To cover both the northern and the southern sky, observations were made with two different telescopes. Objects from the northern sky were observed at the Canada−France−Hawaii Telescope (CFHT) at the Mauna Kea Observatory on Hawaii’s Big Island and the sample included 30 systems. On the other hand, objects from the southern sky were observed at the Magellan II−Clay telescope at Las Campanas Observatory, in the At- acama Region, in Chile, and this sample included 17 systems, three of which were already observed at CFHT and were taken to check for consistency between northern and southern data.

The instrument used for the CFHT observations is the MegaCam wide field imager that is composed of 36 charge-coupled device (CCD) chips, each one with 2048 × 4612 pixels, covering a total field of view of almost a full 1 × 1 deg2, and with a pixel scale of 0.18700/pixel. The number of fields observed for each object depended on the area coverage needed. For smaller objects a single field was observed, while for larger objects up to four fields were observed reaching a coverage of 2 × 2 deg2. Now, for each field, six dithered exposures were taken in each Sloan g and r bands. The images were taken in mostly dark conditions with typical seeing of 0.7–0.900. The dithered images are a group of exposures with slightly different central positions taken in order to cover the gaps between different chips. The instrument used for Magellan II−Clay telescope is also called MegaCam, and it was built to have similar characteristics than its northern counterpart. It is also a mosaic CCD camera with 36 chips, each one with 2048 × 4608 pixels, although in this case the pixel scale is 0.0800/pixel and it covers a full field of view of 24 × 24 02. For small objects like globular clusters only one field was taken and for the largest objects up to 16 different fields were taken covering a total

22 area of 2 × 2 deg2. Observations were taken in mostly dark time with typical seeing of 0.7–1.100. This time, for each field, five dithered exposures were taken in each Sloan g and r bands. In summary, for each object we have 36 × 5 × N individual images in each band, where N represents the number of fields taken for that object.

Data from both instruments used for this survey are pre-processed before it is de- livered to the observers to provide them with frames corrected for the instrumental signatures. This procedure includes bad pixel correction, bias subtraction and flat fielding. Also, preliminary astrometric and photometric solution are included in the pre-processed headers. Nevertheless, these solutions are approximate and thus, we did not use them as the final results and we perform our own photometry and astrometry.

The following step is the data reduction, where the images are processed through a set of scripts to finally obtain the files containing all the stellar sources in each system with their celestial coordinates and calibrated values for their brightness at different bands. This step was performed by Ricardo Muñoz for the images taken with the CFHT telescope and myself for the images taken with the Clay telescope. The data reduction process is a long and delicate process with a lot of details that have to be considered in order to obtain good quality results. In the following section I am going to summarize some of the most important details about the different steps that were involved in the data reduction and that were an important part of the work I did during my PhD. Since I was in charge of the reduction for the images from the Clay telescope, I am going to explain the process made for those images. For the construction of the final files for the objects in the northern sky, the reader is referred to the article Muñoz et al. 2016 in prep., where all the details about the survey are presented.

2.3 Southern Region Catalogue Construction

This section summarizes the steps involved in obtaining the final files for each of the 17 objects observed with the MegaCam instrument at the Magellan II-Clay Telescope starting from the individual pre-processed images. Final files for each system contain the equatorial coordinates and calibrated magnitudes in the g− and r− Sloan bands, for each source detected in the corresponding cluster or galaxy.

2.3.1 Photometry: Instrumental magnitude determination

For each of the objects in our catalogue we observe one or several fields, and for each field five mosaic frames were obtained in the g− and r− bands. Now, each mosaic consisted of 36 chips, and the first step was to split each mosaic into its 36 individual chip images. After this, we carried out the photometry on the individual, non-coadded images, by running DAOPHOT/Allstar. The main purpose of this software is to performed Point Spread Function (PSF) photometry on the individual images. For this

23 purpose, the “best” isolated and brighter stars from each image are picked, and with the brightness spatial profile of those stars a single PSF model is built. This model is an analytical representation of the spatial distribution of the flux of each stars around its central position, and considers how this function varies throughout the extent of the image. After a PSF model is built for the image and stored in the .psf files, the total flux from each star is calculated by fitting the model into the actual brightness spatial distribution of each star according to the position of the star in the image. The advantages of PSF photometry over aperture photometry, that just adds all the flux that comes from the stars, are multiple. The model represents the brightness spatial distribution of a typical star in the image, and this model is fitted to adjust the flux distribution coming from the star, thus, the flux determination is less susceptible to suffer from shot noise and outlier pixels. The other advantage is that if the image have stars that are so close together that their brightness distributions overlap with each other, the model is fitted to all those stars simultaneously, and hence, the con- voluted brightness profile from the group can be separated into the contribution of each individual star. Therefore, PSF photometry is far less susceptible to suffer from overcrowding which is especially important in the central regions of stellar clusters or bright galaxies.

We will call the 10 individual images from each chip as zpn g1.fits, zpn g2.fits, zpn g3.fits, zpn g4.fits, zpn g5.fits, zpn r1.fits, zpn r2.fits, zpn r3.fits, zpn r4.fits and zpn r5.fits, where the “zpn” prefix indicates that a zenithal polynomial projection has been used to describe their XY coordinates, and the “g” or “r” character indicates whether it is a g− band or r− band image. Once PSF photometry was performed in each of these images, we obtained .als files for each one containing the sources with their XY position and the instrumental magnitude in the band (g− or r−) corresponding to that image. After this, for each chip of each field we took the 10 individual photometry .als and process them simultaneously to obtain one combined file for each chip. For this, the first step is to use DAOMATCH that calculates the coordinate transformation between the different images. Then we run DAOMASTER that uses the DAOMATCH transformation and creates an optimum list of stars in the chip taking into consideration all the .als files from the individual images. After this, we run ALLFRAME that uses the list of stars from DAOMASTER, the transformation from DAOMATCH, the individual fits images, and the .psf files of the individual images containing the PSF models and performs photometry simultaneously on the five g− images and five r− images corresponding to the chip. The results are one .alf file for each individual image. These files have the same format as the .als files with XY positions and one value for the instrumental magnitude of the corresponding band of the image. However, the .alf files have far more sources than the .als files in them, since sources from all the images in the chip have been considered to create the .alf file for each individual image. After this, we run DAOMATCH and DAOMASTER again to combine the individual .alf files and look for all the sources in the chip that were detected at least in one g− and one r− image. The final file is called combined zpn g1.raw file created by DAOMASTER that contains all the sources from the chip, where the parameters obtained for each source combine the information obtained for each individual image photometry. The

24 first parameters in combined zpn g1.raw are the XY coordinates for each source, and since different sources were observed in different images with different XY coordinates, the coordinates in the .raw file are transformed into a reference image from the chip which in our case was the image zpn g1.fits. This means that the coordinates of each source in our case correspond to the ones that the source would have had (given its equatorial coordinates) if observed in the image zpn g1.fits, and this is what the word zpn g1 indicates in the name of the combined photometry file. The other fundamental parameters present in this file for each source are the g− and r− magnitudes along with their errors, which as noted above, are a combination of the magnitudes obtained in each individual image photometry for that given source. The last values in the .raw file are the “chi” parameter indicating how precise was the PSF model when fitting the brightness spatial distribution of the source, and the “sharpness” parameter indicating how narrow or wide was the brightness spatial distribution of the source. These last parameters are often used to distinguish stars, from unresolved galaxies and cosmic rays for example. At this stage we already have a file containing the photometry for each chip in each field observed for each object. Now the next step is to obtain celestial coordinates for each source instead of XY coordinates that have no physical meaning. This process is called “astrometry” and will be explained in the following section.

2.3.2 Astrometry

In this section I explain how we obtained celestial coordinates from our stars, starting from the XY positions in the .raw photometry files. Celestial coordinates are widely used in astronomy to depict the angular position of different astronomical sources in the night sky, in such a way that those positions do not depend on the geographical coordinates of the observer or the time of the day. The general purpose of celestial coordinates is that they are a function only on the “actual” physical location of the astronomical source in the Universe with respect to some reference location. Obtaining coordinates that only depend on the properties of the source and not the chip in which was observed, was a crucial step for combining the files from individual chips to obtain a single coherent map. Additionally, at the moment of publishing our catalogues, the stellar coordinates need to be accurate, and have a physical meaning, so they can be useful to the astronomical community.

The main software used for doing the astrometry in our images was SCAMP 7 which calculates the astrometrical solution for a set of overlapping images simultaneously. By calculating the solution on multiple images at the same time increases the statistical signiﬁcance of the result, and is one of the main reasons why this software was chosen for determining the astrometry. Every .ﬁts image obtained in a telescope has a preliminary astrometric solution. These solutions, consist of a set of parameters that are used to transform the XY positions in each image to the equatorial coordinates declination (δ) and right ascension (α). This transformation is done according to a

7See http://astromatic.net/software/scamp/

25 Figure 2.1 Scheme of the astrometry process. Solid arrows connect input and output errors of each step. Below each solid arrows is the name of the software used for that step. Finally the dashed arrows point to the description of the ﬁles.

26 specific projection, which is the mathematical approximation of the spherical night sky into a flat bidimensional plane (see Calabretta & Greisen 2002 for details on different projections and transformation between XY and α and δ). The input images that SCAMP currently uses need to have a tangential projection, however, our Mega- Cam/Clay II images have a zenithal polynomial projection, and thus, they cannot be directly used in SCAMP. Therefore, to obtain SCAMP based astrometry on our images, I first transformed our g− band images into a tangential projection using the WCSTools8 task, REMAP. We will call the original images as zpn g1.fits, zpn g2.fits, zpn g3.fits, zpn g4.fits, zpn g5.fits, zpn r1.fits, zpn r2.fits, zpn r3.fits, zpn r4.fits and zpn r5.fits, where the “zpn” word means they have a zenithal polynomial projection, and the “g” or “r” character indicates whether it is a g− band or r− band image. On the other, hand we will call the g− band reprojected images as tan g1.fits, tan g2.fits, tan g3.fits, tan g4.fits, tan g5.fits, where the “tan” prefix indicates they have a tangential projection. Then the reprojected tangential images were run through SEX- TRACTOR,9 which generates the input files necessary for SCAMP, with a list of stars and their XY positions. After this, the five g− tangential images (from tan g1.fits to tan g5.fits) from each chip were processed simultaneously with SCAMP, and with that we obtained a .head file containing the different coefficients that describe the astrometrical solution, for each input image. These files are called tan g1.head, tan g2.head, tan g3.head, tan g4.head, and tan g5.head. The goal is to use these coefficients to transform the XY coordinates in the photometry file combined zpn g1.raw we have for each chip. However, as mentioned in the previous section, these .raw files have XY coordinates corresponding to the ones each source would have had if observed in the image zpn g1.fits, whereas tan g1.head describes the conversion for XY coordinates from the image tan g1 fits to α and δ.

Therefore, to apply the astrometrical solution we have to first transform the XY coordinates in combined zpn g1.raw to coordinates corresponding to the image tan g1.fits. I did this using DAOMATCH, and created the file combined tan g1.raw. Finally, I applied the astrometric solution described in tan g1.head to the catalogue combined tan g1.raw, with which we obtained the final file for the chip called combined.posn. This file has the α and δ equatorial coordinates for each source in the chip along with instrumental g− and r− magnitudes and their associated errors. A schematic chart is shown on figure 2.1, highlighting the steps the description of the files and the softwares used for each step.

2.3.3 Magnitude calibration: Sloan ﬁlters magnitude determination

In this section I explain the process of obtaining calibrated Sloan g− and r− magnitudes for the sources observed in the diﬀerent clusters and dwarf galaxies of this

8see tdc- www.harvard.edu/wcstools 9See http://astromatic.net/software/sextractor/

27 catalogue. The instrumental magnitudes of the sources observed for each system were calibrated be comparing our magnitudes obtained with the MegaCam/Clay imager with the ones obtained in the SDSS survey. This calibration is based on the well known linear transformation approximation that relates magnitudes obtained at pass- band with similar characteristics, as is the case with MegaCam/Clay ﬁlters and SDSS ﬁlters. This transformation has a term that varies linearly with the color of the source observed and another that is independent of the properties of the source. Formally, the transformations between MegaCam/Clay and SDSS g− and r− magnitudes are the following:

gs = gm + ZPg + 2.5 × log(texp[sec]/90) + Eg × X + CTg × (gm − rm) (2.1) and

rs = rm + ZPr + 2.5 × log(texp[sec]/180) + Er × X + CTr × (gm − rm) (2.2)

The “m” underscore indicates the MegaCam observed magnitudes while the “s” underscore depicts the SDSS transformed magnitudes. The “CT” terms are the g− and r− color terms which represent the linear dependence of the transformation on the color of the source. The “X” term is the airmass of the observations which indicates the length of the light-path through the atmospheric layer of the Earth. The “E” terms are the extinction terms and denote how the amount of light received from a source linearly decreases with the airmass of the observations. The term texp refers to the exposure time of the image, and the logarithm dependence comes from the fact that the ﬂux received from a source grows linearly with the exposure time. We have set 90 seconds and 180 seconds as the reference exposure time for g− and r− bands respectively, since those were the standard values used for most of the systems in our catalogue. Finally, the “ZP” are constant additive terms that only depend on the properties of ﬁlters involved in the transformation.

Calibrating a set of magnitudes, hence, consist of deriving the ZP, CT and E terms for each ﬁlter. Since the ZP and E×X terms along with texp are constant for all the sources of a system, we can rearrange equations 2.1 and 2.2 as:

gs − gm = MTg + CTg × (gm − rm) (2.3) and

rs − rm = MTr + CTr × (gm − rm) (2.4)

Where we have deﬁned

28 MTg = ZPg + 2.5 × log(texp[sec]/90) + Eg × X (2.5) and

MTr = ZPr + 2.5 × log(texp[sec]/180) + Er × X (2.6)

as the mixed terms for each band. From the 17 objects in the MegaCam/Clay sample of our catalogue, eight of them overlap the SDSS coverage. For each of these objects we plotted (gm−rm) against (gs− gm) and derived the corresponding color and mixed term, according to equation 2.3 by calculating a linear fit. An analogous procedure was performed to calibrate the r− band magnitude by using equation 2.4. For the objects with more than one field observed, we performed the fit independently for each one, since different fields were observed at different airmasses. The stars considered in each fit came from the Data Release 7 of the SDSS catalogue. Each time we related these stars to our observations based on their equatorial coordinates α and δ. From this sample we used only SDSS stars with 18 < g < 22 and 18 < r < 21.5. The faint limit was set to eliminate stars from SDSS with large photometric uncertainties. The brighter limit was chosen to avoid saturated stars from our data. After the initial fit was calculated for each object, the final solution was obtained by performing a sigma rejection algorithm. This technique consists of eliminating all the points from the data considered in the fitting process, that depart excessively from the fitted function. Then, the fit is done again using the new subsample, and this process is repeated iteratively until no significant outliers remain.

Figure 2.2 Illustrates the magnitude calibration process for the case of the Leo IV ultra-faint dwarf galaxy. In this plot can be seen the rejection of the outliers, and the close distance from the data and the ﬁnal linear ﬁt.

After fitting the MT and CT values from equations 2.3 and 2.4, for the eight objects of our sample that overlay the SDSS footprint, I could calibrate the magnitudes of the stars in these systems. With this, I finally obtained SDSS magnitudes for the photometry of the eight objects that overlay the SDSS footprint. However, there are nine objects in our MegaCam/Clay data that were not observed in the SDSS catalogue. Hence, for these systems we cannot compare our magnitudes with the ones obtained in the SDSS catalogue and fit equations 2.3 and 2.4. For calibrating these objects, we have to determine the three unknowns for each filter presented in equations 2.1 and 2.2: ZP, E and CT. Fortunately all these terms depend on the properties of the instrument and the observing site, and not on the object observed. Therefore, we determine all those coefficients by using the objects from our catalogue that have SDSS observations. Each of these objects were calibrated using coefficients specific for each system, based on their own photometry. And the coefficients used in non-SDSS objects came from a combination of the coefficients determined for the objects with better photometry. In order to do this, I selected the six fields observed in our catalogue that overlaid

29 Figure 2.2 Magnitude Calibration for the LeoIV ultra-faint dwarf galaxy: Linear fitting of the difference between MegaCam/Clay and SDSS magnitudes as a function of MegaCam/Clay color. Fit was performed to obtain the Mixed Term and the Color Term for g− band (left) and r− band (right) according to equations 2.3 and 2.4. The dashed blue lines in the plots indicate the range of data considered for the fit after deleting outliers, while the solid black lines shows the final fit. the SDSS footprint and had high accuracy photometry. Starting from the MT values obtained for these fields, I defined the “corrected mixed terms” as:

CMTg = MTg − 2.5 × log(texp[sec]/90) = ZPg + Eg × X (2.7) and

CMTr = MTg − 2.5 × log(texp[sec]/180) = ZPr + Er × X (2.8)

which correspond to the mixed terms corrected for the different exposure times of the different images. Since CMT grows linearly with the airmass, we plotted CMT against X for each filter, and performed a linear fit to derive the values of the terms E and ZP. The fitting process is shown in figure 2.3, along with the resulting linear function. In this plot the ZP terms corresponds to the y-intercept and the E term correspond to the slope of the fit. The resulting fitted functions are the following:

CMTg = (7.016 ± 0.014) + (−0.222 ± 0.011) × X (2.9) and

CMTr = (7.463 ± 0.011) + (−0.094 ± 0.009) × X (2.10)

30 Figure 2.3 Corrected Mixed Term as a function of Airmass for g− band (left) and r− band (right). Both mixed terms were ﬁtted using equations 2.7 and 2.8. Dashed black lines show the ﬁtted linear functions.

With these same six ﬁelds we then calculated the weighted average of the CT terms, which resulted in

< CTg >= −0.098 ± 0.010 (2.11) and

< CTr >= 0.052 ± 0.005 (2.12)

Finally, we calibrated all the objects in our catalogue that were not observed in SDSS by replacing equations 2.9, 2.10, 2.11, and 2.12 in 2.3 and 2.4, which resulted in:

gs = gm + 7.016 + 2.5 × log(texp[sec]/90) − 0.222 × X − 0.098 × (gm − rm) (2.13) and

rs = rm + 7.463 + 2.5 × log(texp[sec]/180) − 0.094 × X + 0.052 × (gm − rm) (2.14)

These equations describe the ﬁnal magnitude calibration that we applied for all the objects in our catalogue that were not observed in SDSS, in order to transform our MegaCam/Clay magnitudes in SDSS magnitudes.

31 2.3.4 Merging Individual Files into Final Files

Up to this stage, for each system in our catalogue we have a file for each chip of each field observed for the object. This file has calibrated magnitudes and equatorial coordinates in it, therefore both positions and magnitudes of stars have units with physical meaning. We will call each of these files as (object) Nfield Mchip.posn, where “(object)” indicate the name of the object, N the number of the field observed, and M the number of the chip.

To combine the different files into one final file for each object, we splited the process into two stages. First we combined the different files from each chip into one single file per field, called (object) Nfield.posn, and if the object was observed in more than one field, we combined the different files from each field into a final file for the object called (object).posn.

The spatial region covered by files from different chips overlap with each other, because each one is the result of the combination from different slightly shifted images. Thus, to combine these files we used the overlapping regions between different chips. For each source in these regions, we looked for observations in the adjacent chips that had magnitudes and positions that were “similar enough” to the first one to be considered as different observations of the same source. Then, all the different observations of the same sources were merged into one entry in the resulting merged file.

To perform a high accuracy merging process, it is essential to define optimal magnitude and position difference cutoffs that define the “similar enough” criteria. Formally, two different observations “a” and “b” are considered different observations from the same source, if they satisfy that:

ga − gb ≤ g maxdiﬀ

ra − rb ≤ r maxdiﬀ (2.15) p 2 2 [(αa − αb) × cos(δa)] + (δa − δb) ≤ dist maxdiﬀ

where “g” and “r” refer to the g− and r− band calibrated magnitudes and “α” and “δ” to the equatorial coordinates. The general idea is to define the combination of gmaxdiff , rmaxdiff and distmaxdiff that minimizes the sum of the two types of errors associated with the merging process. These errors are: classifying two observations of different sources as coming from the same source (false positives), and classifying two observations of the same source as coming from different sources (false negatives).

Figure 2.4 shows the process of selecting coincident observations for the only ﬁeld of Leo IV. In the left panel, we see all the candidates for coincidences based on the spatial distance between the sources. In this histogram we can see the combination of two diﬀerent distributions, one with very short distance values and a width of the

32 Figure 2.4 Distribution of distance between pair candidates of Leo IV UDFG field. In this plot we show the distribution of distances between sources in the intersection region and their closest source. All coincidences closer than 4 arcseconds are considered as candidates for being different observations of the same source (left). We then checked which candidates also fulfilled the magnitude difference criteria (as described in equation 2.15). The distance distribution of the candidates that also passed this selection criteria are also shown (right panel). Plot shows how the distance distribution gets cleaner after applying magnitude difference selection criteria (see text for more details). Broken y-axis is denoted by the double slash lines. Both the range limits and the zoom change at this point in order to display the high and low part of the histogram in the same plot.

33 order of the astrometrical errors (∼ 0.5 arcseconds), where the vast majority of the cases belong and which most likely correspond to different observations of the same star. The other contribution present in the histogram is a much wider distribution at larger distance values where a small fraction of the cases belong, and that most likely correspond to observations of different stars that lay close to each other. We then filtered all these cases for the ones that had magnitudes that satisfied equations 2.15. Figure 2.4 shows how after filtering for magnitude difference, the distance histogram gets much cleaner (right panel). The distribution that contained most of the false positives significantly decreases while the distribution that contains most of the actual coincidences suffers only a marginal decrease. For each field merged, we chose the optimal combination of g maxdiff r maxdiff and dist maxdiff given the photometrical and astrometrical errors present in that field, and estimated the sum of false negatives and false positives for each case. To estimate the errors associated to the distance selection, we compared the two distributions. The first one was the distribution of distances between positions obtained from different observations of the same source, and the second one was the distribution of distances to the closest star of each star in the intersection region. Then, to estimate the error using the magnitude selection after having selected for distance, we compared the distribution of the difference between the magnitudes obtained from different observations of the same source to the distribution of the difference in magnitude between random stars. By calculating the sum of errors for each field, we obtained that the field merging produces a number of incorrect pair associations that represent in average a 0.2% of the total stars, while the maximum fraction of errors was 0.7%.

After the different chips from each field were combined and we created the files (object) Nfield.posn, we then combined the different fields observed for each object using a procedure analogous to the ones used to combine the chips. With this, we obtained the 17 final (object).posn files, one for each object, and which represent the entire southern MegaCam/Clay catalogue. These files have equatorial coordinates, calibrated g− and r− SDSS bands, magnitude errors, and the photometry quality parameters “chi” and “sharpness” for each source observed.

2.4 Catalogue ﬁnal results example

In these section I illustrate the ﬁnal results of this catalogue of southern overdensities in the Milky Way external halo. The general purpose is to show the high accuracy of the results, check the consistency between our results and the northern counterpart of this catalogue, and prove the signiﬁcant leap these data represent with respect to previous results.

First, I show the final CMDs for all the objects. Dwarf galaxies are displayed in figure 2.5 while globular clusters in figure 2.6. These diagrams include all the sources selected as stars for each system. The criteria to select stars involved magnitude

34 errors in each band smaller than 0.1, a low chi value for the PSF model fitting and a moderate sharpness value mainly to avoid galaxies and cosmic rays. To highlight the main properties of the CMDs of these systems and avoid significant contamination, I include only the central stars in each diagram. Dwarf galaxy CMDs in figure 2.5, include stars within two times the half light radius of each system. The CMDs of the larger galaxies in our sample include only a random subsample of the central stars to avoid saturation in the plots, given that these regions include several tens of thousands stars. For globular clusters in figure 2.6, we display all the stars within 4 times the half light radius of each system. Most of the CMDs displayed in these figures show clear features consisting mainly of main sequence, sub-giant branch and red giant branch stars. In the case of these systems, all these characteristics show stars that were formed in a single star formation episode, but that are now passing through different stellar evolutionary stages. The degree of evolution of the different stars from the population is in turn governed by the mass of each star, in the sense that more massive stars are more evolved. Furthermore, the presence of RR-Lyrae in these systems, along with the dimm main sequence turnoffs, indicate that these populations are in most cases as old as the Milky Way (∼ 13 Gyr). In summary, the CMDs presented here are consistent with systems formed in the early universe in a single episode of star formation, as it has been widely observed in the past for most outer Halo Milky Way satellites. Therefore, these data pass the first consistency check in the sense that display CMD features coincident with the ones expected for this type of systems. The dwarf galaxies, Carina and Fornax, though, represent two clear examples of systems whose CMDs can not be explained as single stellar populations. And for these two systems, we observe complex CMDs consistent with multiple populations as it has been observed in the past for these objects. Our diagrams reproduce the features expected for these systems, and hence, show the reliability of our photometry, but the high accuracy obtained in our study will enable us to study these systems at a higher level of detail than previous works.

Figures 2.7 and 2.8, show the stellar maps of the systems in our catalogue. For each object, we show stars with g− band mag brighter than 23, since these stars are detected across all the spatial extent of our images, which is not the case for dimmer stars. By selecting these stars, we avoid displaying inhomogeneities in the stellar map that are not real, but just product of the different fractions of stars detected in the different regions of the images. For the case of most classical dSph galaxies in figure 2.7, we restricted the sample further by taking a random subsample of stars with g< 23. This new selection was chosen to avoid saturation in the stellar maps of the largest systems of our sample. These stellar maps, involve large spatial extents for all the objects, including all the sources within multiple times the half light radius of each system. These high accuracy, large area maps, represent a great opportunity to develop morphological studies in these systems, that would provide relevant information about the origin and evolution of these objects.

Then, we wanted to check the internal consistency of the survey. For this, we have to compare the northern data taken with CFHT and processed by Ricardo Mu˜noz,with

35 36

Figure 2.5 Dwarf Galaxies CMDs. Apparent SDSS magnitudes and colors are shown for the stars located inside two times the half light radius of each object. Classical dwarf galaxies are shown in top panels, for most of these systems we only show a random subsample of all the central stars to avoid saturation in the diagrams. Ultra-faint dSphs are shown in the bottom panels 37

Figure 2.6 Globular Clusters CMDs. Apparent SDSS magnitudes and colors are shown for stars located inside four times the half light radius of each object. 38

Figure 2.7 Dwarf Galaxies Stellar Maps. Equatorial coordinates are shown for stars with g< 23. Top panels show the classical dSphs. For most of these systems we plotted a subsample of the stars brighter than 23 magnitudes, to avoid saturation in the plot. Ultra-faint dwarf galaxies are shown in the bottom panels. 39

Figure 2.8 Globular Clusters Stellar Maps. Equatorial coordinates are shown for stars with g< 23. the southern data taken with the Clay telescope that I processed using an analogous method to the one used for northern sources. From the entire sample of 17 objects for which I produced the final catalogues, there are three systems (NGC 7492, Palomar 3 and Segue 1) that were also observed in the CFHT telescope. Figure 2.9, shows the comparison between the CMDs obtained from the photometry of the CFHT images (left panels) and Clay images (right panels) for the three systems observed in both telescopes. This figure shows that the main characteristics of the CMDs for these objects are equivalent for the data coming from CFHT and Clay. As seen in this figure, the only difference between both data samples is probably a higher accuracy and completeness for the data coming from the Clay telescope that I processed. This is mainly seen in the higher definition of the main sequence of the Segue 1 dwarf galaxy in the Clay data with respect to the CFHT data. This slight improvement from the northern data is an important evidence of the high quality data processing described in section 2.3. To further check for consistency between northern and southern data from this survey, we compared the magnitudes of the stars obtained in CFHT with the ones obtained with Clay for the three objects observed in both telescopes. Figure 2.10 shows the histogram of the differences in g− band and r− band magnitudes obtained with both telescopes. For each of the three systems, these histograms include all stars with g> 23. For stars dimmer than this limit, photometrical errors increase drastically and hence do not represent the overall photometrical quality of the data. From the information in this figure we claim that there is no significant systematical offset between magnitudes obtained with data from the two telescopes. The average of the magnitude differences are in all cases smaller than 0.04 magnitudes, which is similar to the average magnitude error in these systems, and which is in all cases less than 20% of the width of the magnitude difference distributions.

The last thing I did to test the quality of our data was to compare our catalogues with external independent results obtained for these objects. For this propose we compare our results with public data. The most recent and complete public catalogue available that covers our systems is the SDSS catalogue. There are two systems in our sample — Palomar 3 and Segue 1— that were also observed in the SDSS catalogue. Figure 2.11 shows the positions of the stars obtained by myself, compared to the positions obtained with the CFHT data (left panels) and compared to the positions obtained in the SDSS catalogue (right panels). For each star in the different catalogues, we computed the difference between the positions derived by each catalogue and displayed them in this figure. These plots show that for the case of Palomar 3 the differences between the equatorial coordinates obtained in the different catalogues are in most cases smaller than 0.2 arcsec which indicates a high accuracy astrometry for this system. For the case of Segue 1 the differences between CFHT and Clay are a little higher, nevertheless, when we compare our Clay results with SDSS we see that the differences are as small as the case of Palomar 3, which indicates that the differences between data from both telescopes used in this study is probably due to the astrometric errors in CFHT data.

Finally, we compared the CMDs obtained with our Clay data with the ones obtained with SDSS. Figure 2.12 shows the CMDs for the sources selected as stars in the Clay

40 (left panels) and SDSS (right panels) data for the cases of Palomar 3 and Segue 1. In this plot we can see that our data represents a huge improvement with respect to public data in terms of photometrical errors and completeness. In fact, we reach stars in our data that are 4 magnitudes fainter than the stars observed in SDSS. It is worth noting that SDSS revolutionized the observations of Milky Way satellites, and most of the galaxies presented in our catalogue were discovered in this catalogue. From that moment on, diﬀerent groups working on Galactic astronomy have collected observations that surpass the quality of SDSS. but most of those studies have focused in just a couple of objects from the Milky Way Halo. Therefore, the catalogue we built, and that I have presented throughout this chapter, represents a unique opportunity to study Milky Way satellites at great detail. This survey provides the homogeneity and complete coverage you can ﬁnd in public catalogues like SDSS but with one of the highest photometrical and astrometrical accuracy ever published for these systems, that can only be reached by using last generation telescope data as we have done.

41 Figure 2.9 Comparison between CMDs from Clay and CFHT data. The CMD obtained with our Clay photometry is compared to the one obtained in the northern counterpart of the survey for the three objects that were observed from both CFHT and Clay telescopes. For each system we show all the stars within four times the half light radius of the object. 42 Figure 2.10 Comparison between magnitudes obtained with CFHT and Clay photometry for all stars in the three objects observed with both telescopes. For each object, the histograms show the magnitude diﬀerences for stars brighter than g> 23, where completeness levels are equivalent in the two data samples.

43 Figure 2.11 Comparison of stellar positions from Clay, CFHT and SDSS data for Palo- mar 3 (upper panels) and Segue 1 (lower panels). Histograms show the diﬀerences between the positions of stars obtained with Clay with the ones obtained with CFHT (left panels) and SDSS data (right panels).

44 Figure 2.12 Comparison of the CMDs obtained using Clay data with the ones from the SDSS catalogue for Palomar 3 (upper panels) and Segue 1 (lower panels). All sources selected as stars are shown for each object. This ﬁgure shows great improvement from our photometry with respect to public data, in terms of magnitude errors and completeness.

45 Chapter 3

Blue stragglers in Outer Milky Way Satellites†

3.1 Introduction

Blue stragglers are stars coeval with a given stellar population, but positioned blueward and above its main sequence turnoff, thus mimicking a younger population (see for example Bailyn 1995 for a review on blue stragglers). In the following Chapters I describe how the star formation history of different stellar system is derived from the location of stars in the CMD, though the synthetic CMD method. However, the application of this method has never considered blue stragglers as a stellar population to be fitted in the CMD. Therefore, the application of the CMD method often confuses blue stragglers with young stars, and this can greatly mislead the conclusions derived from the resulting star formation history (Tolstoy et al., 2009). This implies that a proper characterization of the blue straggler population is essential to discriminate these stars from young stars, and avoid biasing the star formation histories. In this Chapter I study the properties of the stars in local dwarf galaxies that populate the region of the CMD that is brighter and bluer than the main sequence turnoff. In principle, these stars could be either blue stragglers or young stars1, but throughout this Chapter I am going to present the properties of these stars and justify why those properties support that they are authentic blue stragglers as opposed to young stars. Then, I analyze how the fraction of blue stragglers present in a stellar system vary with the different structural properties of the system. With this information in hand I present a method to predict the fraction of genuine blue stragglers present in a Milky Way satellite. Then, for applying the method to discriminate blue straggler counts from young star counts, the sum of the two population have to be determined

†Based on the work published in Santana et al. (2013) and reproduced by permission of the AAS. 1In principle, these stars could also be contamination sources. Nevertheless, as shown in ﬁgure 5.2 this location of the CMD has a negligible contribution from contamination foreground/background sources

46 photometrically. This is done by counting all the stars brighter and bluer than the main sequence turnoﬀ. Then, since we are able to predict the number of authentic blue stragglers in a system, and we measure the sum of blue stragglers plus young stars, we are going to be able to derive how many stars (if any) located brighter and bluer than the main sequence turnoﬀ are genuine young stars. This method is then applied in Chapter 5 for the case of the Carina dSph galaxy.

Moreover, the study of blue stragglers has great value beyond its uses for star formation history derivations, since it can provide us important information about the dynamics, and physical conditions of the system in which they were formed. For this reason, we also investigate the properties of blue stragglers in Milky Way satellites in this chapter, to try and elucidate the physical processes by which they were formed, and with that, infer the properties of their surrounding environment.

The ﬁrst time blue stragglers were observed was by the work of Sandage (1953) in the globular cluster M3, who described them as an apparent extension of the classical main sequence. Since globular clusters have traditionally been considered single stellar populations, stars located blueward and above the main sequence turnoﬀ in its CMD should have evolved out of the main sequence into a post hydrogen-burning phase. In this context, the existence of blue straggler stars challenges our current understanding of stellar evolution. To inhabit a hotter and more luminous region in the CMD, these stars must have increased their original masses and, in the process, renewed their fuel for nuclear reactions.

Since the discovery made by Sandage, blue stragglers have been found in practically all Galactic globular clusters, and several formation mechanisms have been proposed. Early on, blue stragglers as single stars were proposed, either massive young stars due to recent star formation, or stars in a post-helium ﬂash evolutionary phase where hydrogen rich material has sunk to the core (Rood, 1970; Conti et al., 1974), but these were later discarded (e.g., Nemec & Harris, 1987; Nemec & Cohen, 1989). At present, the leading blue straggler star formation mechanisms are stellar mergers produced by direct stellar collisions (hereafter collisional blue stragglers, e.g., Hills & Day, 1976; Leonard, 1989) and mass-transfer or mergers in primordial binary or higher order systems (hereafter binary blue stragglers, e.g., McCrea, 1964; Knigge et al., 2009; Perets & Fabrycky, 2009). Several studies have shown that some blue stragglers are indeed binary systems, by measuring photometric variability in these stars (Jorgensen & Hansen, 1984; Mateo et al., 1990, 1995; Nemec et al., 1995). On the other hand, triples have been claimed to be particularly relevant in blue straggler formation in low density environments (Leigh & Sills, 2011). Triples could form blue stragglers through mechanisms like Kozai cycles2 and tidal friction (Perets & Fabrycky, 2009) or triple evolution dynamical instabilities (Perets & Kratter, 2012). The importance of collisions involving triple stars in forming blue stragglers was conﬁrmed by Geller et al. (2013) through N−body modeling of the old open cluster NGC 188. Bailyn (1995) argued that both mechanisms (binary and

2The Kozai cycles are the ﬂuctuations of the eccentricity and inclination of an inner binary system conforming a triple system (Kozai, 1962).

47 direct collisions) are likely to be at work in globular clusters, a view shared by several studies (e.g., Hurley et al., 2001; Mapelli et al., 2006; Dalessandro et al., 2008; Ferraro et al., 2009) with their relative importance being a function of cluster mass (Davies et al., 2004), dynamical evolution and physical conditions of the environment (Piotto et al., 2004; Knigge et al., 2009; Leigh et al., 2011a).

To investigate the relative importance of the two formation mechanisms, several correlations between the observed fraction of blue straggler stars and host properties have been explored. For example, the fraction of blue stragglers can be plotted as a function of density or encounter rate. If the fraction of blue stragglers grows with density, then collisions might be the dominant formation mechanism. If instead we find less blue stragglers in denser systems, collisions between stars might prevent blue straggler formation, either by separating primordial binaries or disrupting multiple star systems. Perhaps the most notable result in this context is the one reported by Piotto et al. (2004), who found that blue straggler specific fraction declines with increasing luminosity or mass. The interpretation of this anti-correlation is that the current fraction of binary stars, from which blue stragglers would form, would be lower for larger and denser systems (Leigh et al., 2011a; Sollima et al., 2008; Davies et al., 2004). Although, in the magnitude range spanned by Piotto’s globular cluster sample, −10 < MV < −6, a significant contribution from collisionally formed blue straggler stars cannot be discarded.

In addition to globular clusters, blue stragglers have been detected in a variety of low density environments such as Galactic dSph galaxies (e.g., Momany et al., 2007), loose stellar clusters (e.g., Geller & Mathieu, 2011; Sollima et al., 2008), the Milky Way’s bulge (Clarkson et al., 2011) and even the Galactic ﬁeld (e.g., Stetson, 1991; Glaspey et al., 1994; Preston & Sneden, 2000; Carney et al., 2001, 2005). Currently, the number of studies in dSph galaxies is rather limited, and in most cases they cover one or two galaxies (see Mapelli et al. 2007 for Draco and Ursa Minor, Hurley-Keller et al. 1999 and Monkiewicz et al. 1999 for Sculptor, and Mateo et al. 1995 for Sextans). Only one study to date presents a systematic study among classical dSph galaxies (Momany et al., 2007).

On the other hand, information regarding blue straggler stars in ultra-faint dwarf galaxies is extremely scarce. These extreme low luminosity, low surface brightness systems represent a new opportunity to study blue straggler stars in extremely low stellar density environments.

In this work, we use data from the survey presented in Chapter 2, and from that catalogue we use the objects from the northern sky observed with the CFHT telescope and which were processed by Ricardo Mu˜noz3. Here we present a homogeneous analysis of the blue straggler star populations of most of these Milky Way halo satellites, to study the characteristics of blue straggler stars in the lowest stellar density systems.

3The southern counterpart of this catalogue was not yet processes by the moment this work was developed, and therefore those sources are not included in this analysis

48 The analysis includes globular clusters, dSphs, as well as the ﬁrst systematic study of blue stragglers in the ultra-faint dwarfs.

Collisions involving single, binary or triple stars in our systems show typical times of occurrence that are large compared to the blue straggler lifetime. Therefore, except for a few of our highest density systems, the blue stragglers in our Galactic satellites might not be explained by collisions of any type. Moreover, the densest objects in our sample, the outer halo clusters, are as a group, fainter and on average ten times bigger than their inner halo counterparts. Our goal is to investigate the characteristics of blue straggler stars in the most diﬀuse stellar systems and study the inﬂuence of the environment on their blue straggler star populations. With this we can unravel the physical processes behind their formation and, as described earlier, derive a method to discriminate blues straggler counts and young star counts in this low density stellar systems. The Chapter is organized as follows: In Section 3.2 we describe the photometric catalog and the sample of satellites used in this study. We also detail how blue straggler stars are selected and their counts normalized to red giant branch stars. In Section 3.3 we present our results, including modeling of young populations that could be mimicking blue straggler stars in dwarf galaxies. In Section 3.4 we discuss our results for each type of satellite. Finally, a brief summary is presented in Section 3.5.

3.2 Data and Blue Straggler Selection

We analyzed the blue straggler star population of 22 outer halo (beyond RG = 25 kpc) satellites: ten globular clusters, three of the classical dSph galaxies (Draco, Ursa Minor and Sextans) and nine ultra-faint dwarfs. Data for all objects were obtained using the CFHT MegaCam imager, and it represents a subsample of the catalogue presented in Chapter 2. From the entire sample of objects in the northern part of our catalogue, the 22 systems considered here correspond to the ones where blue straggler stars could be reliably selected in the CMD. In the excluded systems, extreme low star counts, severe overcrowding or photometry issues prevented a robust blue straggler discrimination, and would have added large systematic errors to the analysis. We note that overcrowding aﬀects only a few objects, since most outer halo clusters are, on average, more extended and less luminous than their inner halo counterparts, and therefore have lower stellar densities. In the cases where overcrowding could have been a problem, the regions where completeness was below 50%, for magnitudes brighter than that of the MSTO, were always more than a factor of 50 smaller than the total region studied. Therefore, even for these systems overcrowding produces a negligible eﬀect on our results.

In figure 3.1 we show the CMDs of all the sources selected as stars for different globular clusters, classical dwarf galaxies and ultra-faint dwarf galaxies in our sample. In this figure we also show the boxes used for selecting blue stragglers and red giant branch stars from each CMD.

49 Figure 3.1 (g − r) vs Mg extinction corrected CMDs of stars in 9 diﬀerent satellites from our sample, obtained at the CFHT. Classical dwarf galaxies are shown in the top panels, ultra-faint dwarf galaxies are shown in the middle panels and three of the globular clusters in our sample are shown in the bottom panels. Boxes where blue straggler and red giant branch stars were counted are shown for each case.

50 Figure 3.2 Stars in the BoötesI field. Left: BoötesI (g −r) vs Mg, extinction corrected CMD. Blue and red lines show, respectively, Padova isochrones of 1 and 12 Gyr, with a metallicity of [Fe/H]=−2.1. The blue box shows the CMD region were blue straggler stars were counted, while the red giant branch region is delimited by the red box. Stars considered as blue stragglers are shown as blue squares. Right: Star map of the Boötes I ultra-faint dwarf. The blue curve shows the limit for the system region at twice the half light radius, while the red annuli show the limits for the contamination region, at 5 and 6 times the half light radius respectively.

To select blue straggler stars, we defined a box in the dereddened (g − r) vs Mg diagram of each object. The size and shape of this box was chosen to maximize the blue straggler counts while at the same time minimizing contaminants from other stellar populations. We determined the distance from the blue straggler box to the main sequence turnoff position based on typical main-sequence widths and photometric uncertainties. Likewise, the bright side of the blue straggler box was chosen based on typical blue horizontal branch extensions. In figure 3.2 we show our blue straggler star selection criteria for the ultra faint dwarf galaxy BoötesI as an illustration. The coordinates of the four points in the CMD that define the blue straggler star4 box for each object, relative to the main sequence turnoff position had typical [∆(g − r), ∆g] values of (−0.245,−0.405), (−0.554,−1.969), (−0.362,−3.012) and (−0.010, −1.513). Small shifts, with typical values of 0.015 in color and 0.09 in magnitude, were applied in some objects to move the entire box. These shifts are mainly caused by uncertainties in the position of the main sequence turnoff and the intention of avoiding regions significantly contaminated in certain objects. Varying the bright and dim side of our blue straggler box, along with applying the shifts, does not significantly change our results since these variations were proven to be small compared to our random errors.

The data were dereddened for all satellites using values for E(B − V ) taken from

4It is worth noting that we denominate blue stragglers what in principle can also be young stars. However, in Section 3.3 we show that the vast majority of our blue straggler “candidates” are unlikely to be young stars. We therefore, use the term blue straggler for every star that falls inside the box in the CMD described above.

51 Schlegel et al. (1998). These values were translated into Sloan filter extinctions Ag and Ar, using the transformations of Schlafly & Finkbeiner (2011). Absolute magnitudes were derived using distance values from the literature5. We derived metallicities by fitting Padova isochrones to the main old population.

To compare the number of blue straggler stars among systems with different absolute magnitude, a common practice (e.g., Piotto et al., 2004; Leigh et al., 2007) is to normal- ize the blue straggler star counts to those of another sub-population, typically red giant branch or blue horizontal branch stars. For this study, we chose red giant branch stars since they are more numerous than blue horizontal branch stars. This choice reduces shot noise due to low number of stars, a problem especially critical for the ultra-faint dwarfs (Martin et al., 2008a; Muñozet al., 2012b). To avoid introducing significant bias when using red giant branch stars as a normalization population, we checked that the number of these stars grows linearly with luminosity. Figure 3.3 shows that red giant branch star counts are indeed proportional to the flux of the systems, with a correlation factor of r2 = 0.91, confirming that red giant branch stars are good tracers of total stellar luminosity or mass. To select red giant branch stars, we defined a box centered on a 12 Gyr old Padova isochrone (with the appropriate metallicity for each system) 0.19 mag wide and located between 2.4 and 4.9 mag below the red giant branch tip. To count both blue straggler and red giant branch stars we used an elliptical region within 2 times the half light radius of the system. For each case, this region was defined by the ellipticity and position angle derived in R. R. Muñozet al. (in preparation), based on the same CFHT data. To account for background/foreground contaminants, we counted sources in both the blue straggler and red giant branch boxes, but in annuli at distances greater than 4 times the half light radius from the center of the object. An example is shown in the right panel of figure 3.2 and the values of the inner and outer annuli of the contamination region, normalized to the half light radius, are shown in Table 3.1 for each object.

Once blue stragglers, red giant branch stars and contamination objects were selected, we deﬁned the speciﬁc fraction of blue stragglers as:

BSS BSSs − BSSc FRGB = (3.1) RGBs − RGBc where BSS and RGB mean ‘blue straggler star’ and ‘red giant branch star’ and s and c mean, respectively, ‘system’ and ‘contamination’.

5Distances to all our clusters were taken from Harris (2010), for dwarf galaxies the following references were used: Dall’Ora et al. (2006) for Bo¨otesI; Walsh et al. (2008) for Bo¨otesII; Musella et al. (2009) for Coma Berenices; Kuehn et al. (2008) for Canes Venatici I; Bonanos et al. (2004) for Draco; Musella et al. (2012) for Hercules; Belokurov et al. (2009) for Segue 2; Lee et al. (2003) for Sextans; Garofalo et al. (2013) for Ursa Major I; Dall’Ora et al. (2012) for Ursa Major II; Carrera et al. (2002) for Ursa Minor and Willman et al. (2005) for Willman 1.

52 Figure 3.3 Red giant branch star numbers are plotted against the absolute magnitude of each system. The ﬁgure shows that our normalization population numbers, are directly proportional to the total luminosity of the system. Dashed black line shows a linear relation between luminosity and red giant branch star counts, which has a correlation factor of 0.91 with our data. 3.3 Results

3.3.1 Blue Straggler Speciﬁc Fractions

In Table 3.1 we list the resulting blue straggler and red giant branch star counts, along with the corresponding blue straggler speciﬁc fractions, limits for the contamination region and the density of our clusters and galaxies.

A surprising first result is that blue stragglers seem to be ubiquitous among dwarf galaxies, being present even in the most diffuse and least luminous systems. In fig- BSS ure 3.4, we plot FRGB against absolute magnitude, MV. This figure shows that the blue straggler star fraction distribution for galaxies is statistically consistent with being flat over a six magnitude range, with a weighted mean value of:

BSS FRGB dwarfs = 0.29 ± 0.01 (3.2) and a standard deviation of 0.17. In contrast, for globular clusters we see a well- BSS deﬁned anti-correlation between log(FRGB) and the absolute magnitude of the objects.

53 Table 3.1. Blue Straggler Stars and Red Giant Branch Counts for All Satellites

1 2 3 4 BSS 5 −3 6 rcont,inn 7 rcont,out 8 Object Type BSST Norm BSSC RGBT Norm RGBC F nrh[stars pc ] RGB rhalf rhalf Bo¨otesI UFDG9 71 16.19 230 77.73 0.36 ± 0.07 2.1×10−3 5.0 6.0 Bo¨otesII UFDG 4 1.20 18 5.00 0.26 ± 0.17 5.9×10−3 4.0 6.0 Coma Berenices UFDG 10 4.00 21 8.83 0.49 ± 0.33 4.6×10−3 5.0 7.0 Canes Venatici I UFDG 343 91.98 842 40.37 0.31 ± 0.03 6.0×10−4 4.8 5.8 Hercules UFDG 35 9.43 127 27.57 0.26 ± 0.07 4.1×10−4 6.0 8.0 Segue 2 UFDG 3 0.60 13 3.57 0.26 ± 0.22 7.4×10−3 5.0 6.2 Ursa Major I UFDG 19 6.36 56 11.59 0.28 ± 0.12 3.8×10−4 5.5 7.0 Ursa Major II UFDG 23 3.85 79 16.42 0.31 ± 0.09 1.2×10−3 5.0 7.0 Willman 1 UFDG 3 0.21 4 0.72 0.85 ± 0.75 2.2×10−2 5.0 8.0 Draco dSph 422 17.82 1600 76.73 0.27 ± 0.02 1.2×10−2 5.0 6.0 Sextans dSph 521 65.49 1822 260.35 0.29 ± 0.02 1.3×10−3 4.0 5.0

54 Ursa Minor dSph 514 14.00 1743 47.36 0.30 ± 0.02 3.3×10−3 5.0 6.5 NGC 5694 GC 11 0.15 247 6.97 0.05 ± 0.01 7.5×100 15.0 19.0 NGC 6229 GC 11 0.19 318 1.66 0.03 ± 0.01 1.5×101 14.0 18.0 NGC 7006 GC 19 0.12 263 9.06 0.07 ± 0.02 6.1×100 14.0 18.0 NGC 7492 GC 8 0.08 83 0.75 0.10 ± 0.04 1.4×101 16.0 20.0 Eridanus GC 12 0.25 45 0.39 0.26 ± 0.09 2.7×10−1 20.0 28.0 Palomar 3 GC 11 0.15 65 0.44 0.17 ± 0.06 8.5×10−1 20.0 30.0 Palomar 4 GC 10 0.12 102 0.44 0.10 ± 0.03 1.5×100 20.0 30.0 Palomar 13 GC 8 0.12 20 0.75 0.41 ± 0.18 1.4×100 16.0 22.0 Palomar 14 GC 15 0.75 91 5.14 0.17 ± 0.05 1.6×10−1 12.0 16.0 Palomar 15 GC 20 1.32 150 18.25 0.14 ± 0.04 6.5×10−1 12.0 16.0

1Blue straggler stars measured in the system region 2Blue straggler stars measured in the contamination region normalized by area 3red giant branch stars measured in the system region 4red giant branch stars measured in the contamination region normalized by area 5Specific fraction of blue stragglers as measured from equation (1) 6Stellar density measured within the half light radius of the system 7Inner radius of contamination region normalized to the half light radius 8Outer radius of contamination region normalized to the half light radius 9ultra-faint dwarf galaxy BSS Figure 3.4 Specific fraction of blue stragglers FRGB is plotted against absolute magnitude. A clear anti-correlation can be seen for clusters, while dwarf galaxies show a high BSS and flat distribution. The logarithm of the weighted mean of FRGB is shown as a solid blue line while dashed blue lines show the standard deviation around this value. Red BSS solid line shows the fit for clusters corresponding to log(FRGB) ∝ (0.28 ± 0.04) MV

The linear function ﬁtted has the form:

BSS log(FRGB) clusters = (0.28 ± 0.04)MV + (0.50 ± 0.22). (3.3)

The uncertainties in the fitting parameters of this and all the forthcoming equations were estimated using Monte Carlo simulations. Each time we ran a simulation, we shifted the data by values consistent with the uncertainties in the measured frequencies and then calculated the set of fitting parameters that corresponded to that shifted data sample. Then, for each fitting parameter, the uncertainty was determined as the standard deviation of the values obtained in the different runs.

This result is consistent with a similar anti-correlation found by Piotto et al. (2004) for a group of 56 globular clusters, most of them in the inner halo, but in our study we have expanded the anti-correlation to clusters that are three magnitudes fainter. Even though we used red giant branch stars as a normalization population and horizontal branch stars were used in Piotto et al. (2004), the slopes of the anti-correlations found in both studies are consistent within the errors.

A different normalization method (first outlined in Knigge et al. 2009) was also used to illustrate the dependence of blue straggler population sizes on the total population size of their hosts. As seen in figure 3.5, we correlated the number of blue stragglers observed with the total stellar mass of our systems. The linear fitting functions we

55 Figure 3.5 The number of blue stragglers is plotted against the total stellar mass of each system. Fitting function for clusters is log (NBSS) ∝ (0.06 ± 0.07) log (M) and is shown as a red line. Fitting function for galaxies is log (NBSS) ∝ (0.90 ± 0.04) log (M) and is shown as a blue line. obtained using this normalization were:

log (NBSS) = (0.06 ± 0.07) log (Mass) + (0.8 ± 0.3) (for globular clusters) (3.4)

log (NBSS) = (0.90 ± 0.04) log (Mass) + (−2.4 ± 0.2) (for dwarf galaxies) (3.5)

Both correlations found here are equivalent to the results found before using the speciﬁc frequency of blue stragglers. Blue straggler numbers in clusters increasing slowly with BSS mass is equivalent to an anti-correlation of FRGB and MV like the one in equation 3.3. On the other hand, blue straggler numbers in dwarf galaxies growing almost linearly BSS with mass is equivalent to a nearly constant distribution of FRGB. Within the errors, equations 3.2 and 3.5 point to speciﬁc blue straggler fractions in dwarfs that are either independent of the absolute magnitude or that follow a shallow anti-correlation with absolute magnitude like the one found by Momany et al. (2007).

Finally, we plot blue straggler specific frequencies against both the density within the half light radius (see figure 3.6) and the encounter rate between single-single stars (see figure 3.7), as calculated in Leigh & Sills (2011). Figure 3.6 shows that blue straggler frequencies of all our systems follow a single exponential trend with density, displaying a smooth transition between clusters and galaxies. Figure 3.7, on the other hand, shows that the same behavior is followed by the frequency of blue stragglers versus the encounter rate. The fraction of blue stragglers stays constant and high in the low density/low encounter rate regime spanned by our dwarf galaxies, and decreases

56 BSS Figure 3.6 Specific fraction of blue stragglers FRGB is plotted against density, calculated inside one half light radius of each system. While dwarf galaxies show a flat distribution on the low density regime, clusters show an anti-correlation in the high density regime. BSS The function fitted is shown as a solid black line, which corresponds to log(FRGB) ∝ (−0.063 ± 0.007) nrh. with density and encounter rate in the range spanned by our globular clusters. The fitting functions that describe the blue straggler specific frequency against these two parameters are:

log (F BSS ) = (−0.063 ± 0.007) n[stars pc−3] RGB (3.6) +(−0.55 ± 0.01)

BSS 9 log (FRGB) = (−1.9 ± 0.2) × 10 Γ + (−0.56 ± 0.01) (3.7)

3.3.2 Blue straggler/Young Stars Discrimination

By definition, blue straggler stars live in a region of the CMD that could also be inhabited by young stars. In old systems without recent episodes of star formation, like globular clusters have traditionally been considered, the identification of blue straggler stars in the CMD is straightforward. However, for satellites where recent episodes of star formation cannot be ruled out a priori, it is not immediately clear whether an observed extension of the main sequence beyond the older turnoff is due to blue straggler or young stars.

57 BSS Figure 3.7 Speciﬁc fraction of blue stragglers FRGB against the rate for single-single BSS star encounters, as calculated in Leigh & Sills (2011). The ﬁtted function is log(FRGB) ∝ (−1.9 ± 0.2)×109 Γ and is illustrated as a solid black line.

We studied the numbers and magnitude distributions of stars inhabiting the region of the CMD occupied by blue stragglers. Based on these values, we estimate the ages and fractions of young stars that would reproduce our observations in the absence of genuine blue stragglers. In this way, we can assess the likelihood that recent bursts of star formation could be responsible for the stars observed beyond the main-sequence turnoﬀ.

Given the extremely low number of stars present in our dwarf galaxies, the only region of the CMD that we can use to compare blue stragglers and young stars is the region previously defined as our blue straggler box, since all the other regions of the CMD would show negligible number of blue stragglers and/or young stars compared to the main old population or contamination stars (see figure 3.8 for an example of the expected appearance of the CMD of an object where young stars could reproduce the number and distribution of stars observed beyond the main-sequence turnoff in our dwarf galaxies).

To estimate the properties of the young stars that could reproduce the blue straggler frequencies observed in our galaxies, we ran simulations where we generated both a young and an old single stellar population. To generate them, we used two Padova isochrones: a young one with an age varying from 1 to 3 Gyr and an old one of 12 Gyr, both with an abundance of [Fe/H]= −2.0 which represents the average among the galaxy sample6. We also used the corresponding theoretical luminosity functions, based

6Varying the metallicity of the isochrone introduces only minor changes in our results, and therefore,

58 Figure 3.8 Simulated CMD for fake stars with a fraction of young stars of 0.02. Red and blue points represent, respectively, 2 Gyr and 12 Gyr stars. Solid and dashed black lines show the theoretical isochrones used, while blue and red boxes show the regions where blue straggler stars and red giant branch stars were counted. on a Chabrier (2003) IMF, incorporating magnitude uncertainties consistent with our photometric data. Once we populated the fake CMDs, we counted blue straggler- like and red giant branch stars in the same way we did for the real data. Thus, for a given age a and fraction of young stars f we obtained a simulated blue straggler fraction F (a, f) corresponding to the one that a given system with no genuine blue stragglers would show. By comparing the frequencies measured in the real data with the simulated data, we obtained the fraction of young stars that would be needed to mimic the observed population of blue stragglers. In figure 3.8 a simulated CMD is shown for illustration, with young and old populations of 2 and 12 Gyr respectively, and a fraction of young over total stars of 0.02. The results of the simulations are shown in figure 3.9. This plot shows the fake blue straggler frequencies corresponding to different fractions of young stars, for ages ranging from 1 to 3 Gyr. Also shown here are the ranges of blue straggler fractions actually observed: one including all the objects and the other excluding the four (out of twelve) galaxies with the largest frequency uncertainties.

Additionally, we constrained the age of young stars that could mimic blue stragglers by comparing the magnitude distribution of each set of young stars with those of the observed blue stragglers. We also used the globular clusters as a “control sample”. Given that the number of stars in the ultra-faint dwarfs are extremely low, to make the comparison statistically meaningful, we analyzed the magnitude distribution of the for simplicity, we chose to keep the metallicity constant.

59 Figure 3.9 Simulated fraction of blue straggler stars corresponding to each young star fraction, for different ages of the generated stars. The black dashed lines show the blue straggler fraction range observed in our complete galaxy sample. The black solid lines show the blue straggler fraction range observed in the galaxies excluding the four systems with the highest frequency uncertainties. ultra-faint dwarfs as a single group. The three classical dSphs in our sample were studied individually. We carried out a Kolmogorov-Smirnov (KS) test to compare the different sets of stars, and found that stars with ages of 2.5 ± 0.5 Gyr were the only ones even marginally consistent with the magnitude distribution of the observed blue stragglers in our dwarf galaxies. The magnitude distributions of blue stragglers in globular clusters are also consistent with the one of 2.5 Gyr old stars and blue stragglers from dwarf galaxies. This result comes as no surprise. For populations older than ∼ 2.5 Gyr, turnoff stars leave what we defined as our blue straggler box progressively closer to its faint end while younger populations will extend beyond the upper luminosity limit observed for blue straggler, both in clusters and in galaxies.

What is left to determine is the fraction of stars with ages in the range of 2–3 Gyr that would reproduce the speciﬁc fractions of blue stragglers observed in our dwarf galaxies. Figure 3.9 shows that to reproduce the lowest observed fraction of blue straggler stars in all dwarf galaxies, a minimum young star fraction of ∼ 1–2% is needed. For the upper limit, the fractions needed are ∼ 4–7% for 2.5 ± 0.5 Gyr old stars.

In summary, the stars in the region of the CMD occupied by blue stragglers in our dwarf galaxies can be attributed to recent bursts of star formation only if all the dwarf galaxies in our sample formed stars 2.5 ± 0.5 Gyr ago, and these stars account for ∼ 1–7% of the total number of stars, or ∼ 1–9% in mass fraction. Furthermore, if we exclude the four systems with the highest blue straggler frequency uncertainties, the

60 Figure 3.10 Radial distribution of blue stragglers with respect to the one of red giant branch stars. For each system, the curve shows the specific frequency of blue stragglers at different radii normalized to the total specific frequency of blue stragglers. All the objects where the radial distribution of blue stragglers were not statistically consistent with the one of red giant branch stars are plotted. Top: Two dwarf galaxies where blue stragglers are located preferentially on the outskirts of the system. Middle: Two globular clusters where blue stragglers are centrally concentrated. Bottom: Globular cluster Palomar 13 (left panel) with a central concentration of blue stragglers and Eridanus globular cluster (right panel) where a bimodal distribution can be present.

61 ﬁne-tuning of the star formation history of galaxies would have to be even greater to explain blue stragglers, since the young star fraction needed would have to be in the narrow range of 1 to 3 %, which (as explained in the discussion section) we deem highly unlikely.

3.3.3 Radial Distribution Analysis

We explored an additional line of evidence to help elucidate the nature of the blue straggler candidates in our Galactic satellites: we compared their radial distributions to those of red giant branch and main sequence stars. How blue stragglers are distributed throughout a system can be the result of a complex interplay between dynamical history and the dominant blue straggler formation mechanism. In our dwarf galaxy sample collisions are negligible and 2-body relaxation times are longer than the age of the universe and therefore dynamical evolution (mass segregation) is not expected. In this scenario, there is no reason to presume central concentration of blue stragglers. Young stars, on the other hand tend to be centrally concentrated with respect to other stellar populations in dwarf galaxies (e.g. Harbeck et al., 2001; Grebel, 2001), and thus the radial distribution of the stars we classiﬁed as blue stragglers can help us distinguish genuine blue stragglers from young stars. In the case of the globular clusters in our sample, where we can assume a priori that blue stragglers are genuine, eventual central concentration could shed some light into the relevance of collisions as a formation mechanism.

For most satellites in our sample, we found that the radial distribution of blue stragglers is nearly indistinguishable from that of red giant branch stars. Significant differences are seen only in 6/22 = 27% of our systems. These objects are: the ultra- faint dwarfs Canes Venatici I and Ursa Major II and the globular clusters Palomar 4, Palomar 13, Palomar 15 and Eridanus. Figure 3.10 shows the blue straggler fraction versus radius, normalized by the overall fraction of blue stragglers, for these six objects. It is interesting that for Canes Venatici I and Ursa Major II, blue straggler stars are located preferentially in the outer regions. This behavior is also observed in galaxies like Draco, although for this galaxy the difference is too small for the KS test to differentiate both radial distributions. For the globular clusters in the figure, a clear radial concentration is observed (except for Eridanus, where a bimodal distribution might be present).

It is worth reminding the reader that the area we used to select blue stragglers corresponds to twice the half light radii of the systems, and therefore features in the radial distribution present at larger distances will not be observed if the objects extend much further than this. However, we do not anticipate this to be a problem given the extremely low densities at radii larger than 2 times the half light radius.

62 3.4 Discussion

3.4.1 Dwarf Galaxies

In both classical and ultra-faint dwarf galaxies, blue stragglers are ubiquitous, regardless of how low the stellar densities or encounter rates are. Their speciﬁc frequencies are high compared to those observed in globular clusters and are found to be statistically consistent with being constant over a six magnitude range. Given that we found the red giant branch populations to scale linearly with luminous mass, this is equivalent to saying that the number of blue stragglers grows almost linearly with the total stellar mass of the system.

We used simulations of young populations to compare their photometric properties with those of the blue stragglers observed in dwarf galaxies and conclude that the latter are genuine, as opposed to young stars. A number of facts support this conclusion: (1) for young stars to have magnitude distributions statistically consistent with the blue stragglers observed in dwarf galaxies, their ages need to be closely clumped around 2.5 Gyr. This result can be readily understood when we consider that our brightest blue stragglers have an absolute magnitude of Mg ∼ 1.9, coincident with the magnitude at which a 2.5 Gyr old star evolves out of our blue straggler star box. This is also the magnitude corresponding to a star with twice the mass of a turnoff star 12 to 13 Gyr old, an expected result if we are seeing blue stragglers formed by collisions (either single-single or in binaries) or mass-transfer in binary systems. (2) The magnitude distributions of blue stragglers in both dwarf galaxies and globular clusters (where they can be reliably classified as blue stragglers) are completely consistent. (3) The lack of central concentration of blue stragglers in dwarf galaxies is consistent with the scenario wherein these stars form from mass-transfer or mergers in primordial binaries or multiple systems, rather than being the result of a recent star formation episode. In the latter case the young stars would be expected to be located preferentially near the central regions. (4) From the simulations we also determined that to reproduce the range of observed blue straggler frequencies, the 2.5 ± 0.5 Gyr old stars should constitute 1–7% of the total number of stars in all the dwarf galaxies in our sample, an unlikely fine-tuned common star formation history (Mateo, 1998; Grebel, 1999; Tolstoy et al., 2009).

Most galaxies in our sample have half light densities of 10−2–10−3 stars pc−3, i.e., at least 10 times less dense than the solar neighborhood (Latyshev, 1978). Given these extremely low stellar densities, blue stragglers formed by collisions between stars can be safely ruled out. When considering collisions between single, binaries or triple stars, the collision times (calculated as in Leigh & Sills 2011) are orders of magnitude higher than the age of the universe. Even though some physical processes have been particularly successful in explaining collisions in low density environments, they might not explain the blue stragglers observed in systems like our dwarf galaxies. For instance, the triple evolution dynamical instability proposed by Perets & Kratter (2012) produces

63 encounter rates which are too low in systems with low numbers of stars to explain our dwarf galaxy blue stragglers.

If collisions of any kind cannot account for blue stragglers in our systems, their presence can only be explained if they formed via mass-transfer and/or mergers in primordial binaries, whether or not they have more companion stars that are members of the system. Two powerful correlations were found to support this claim. When plotting blue straggler fractions against both density and encounter rate, we found that a single exponential function could reproduce the behavior of all satellites in our sample. In this context, dwarf galaxies live in the lower density/encounter rate regime, displaying high and similar values of blue straggler fractions. This points to the fact that collisions neither signiﬁcantly create nor prevent the formation of their blue stragglers. On the other hand, as we explain below, close encounters in higher density environments prevent blue straggler formation by altering the conﬁguration of the binary or multiple systems. Finally, the lack of central concentration of blue stragglers in all our dwarf galaxies is also consistent with the binary/multiple system scenario, implying that these stars can be formed in all regions of our galaxies and not just their slightly higher density central regions.

The similarity in the blue straggler fractions observed in galaxies can be explained if the primordial binary star fractions are also similar. While this should be further conﬁrmed by observations, hints that this is in fact the case already exist (Geha et al. 2013 measured the binary fraction of two ultra-faint galaxies, ﬁnding identical binary fraction values of 47%).

3.4.2 Globular Clusters

Our observations show that, for the globular clusters in our survey, the specific frequency of blue stragglers decreases when there is an increase in a particular physical parameter of the host system, such as luminosity, stellar densities, encounter rate and BSS total stellar mass. The anti-correlation between FRGB and the luminosity of the systems is similar to the one observed for inner halo clusters, even though our globular clusters are on average less luminous and larger (5–10×). The slope of our anti-correlation is consistent within the uncertainties with the one found by Piotto et al. (2004) using a sample of 56 globular clusters with −6 > MV > −10, and by Sandquist (2005), which extends the results of Piotto et al. results with lower luminosity clusters down to MV ∼ −4. The anti-correlation derived in our study extends the existing ones to absolute magnitudes as faint as MV ∼ −2.5. At this faint end, the fraction of blue straggler stars in our globular clusters is comparable to that of dwarf galaxies. Despite the consistency between our correlations, there is a key difference between our results and the one by Piotto et al. (2004): we study blue stragglers within 2 times the half light radius of our clusters, which represents a significant fraction of the total cluster area, whereas Piotto’s work focused on the cluster’s cores. This difference is important since, as proposed by Leigh et al. (2011b), the systems with the higher relaxation

64 times/higher mass would not have had time to sunk their blue stragglers to the inner- most regions by two-body relaxation. This would reduce the number of blue stragglers, NBSS, found in the high mass clusters when counting them in the most central regions, but that would not aﬀect the trend of NBSS when the region of the cluster considered represents a considerable fraction of the total cluster area. Thus, if dynamics in the central regions do not destroy the progenitors of blue stragglers and these stars are homogeneously formed within the clusters, mass segregation would translate to a sub- linear dependence of NBSS with cluster mass enclosed when studying only the central regions whereas a linear dependence would be expected when considering larger areas. Our ﬁgure 3.5 then argues against mass segregation playing an important role in the blue straggler counts found in our globular clusters.

As was the case for dwarf galaxies, collisions alone are unable to explain the fraction of blue stragglers observed in our globular clusters. Based on the collision times, calculated as in Leigh & Sills (2011), collisions between single, binary or triple systems can account only for a small contribution to the blue straggler numbers observed in our highest density clusters. Thus, there should be another dominant blue straggler formation mechanism at work. Figures 3.6 and 3.7 show that globular clusters inhabit our higher density, higher encounter rate regime, showing a systematic decrease with both physical parameters. We interpret these trends as supporting a scenario where mass-transfer or mergers in binary or multiple star systems are the dominant blue straggler formation mechanism in the outer halo globular clusters. The following pieces of evidence support this scenario: (1) The behavior of the frequency of blue stragglers is well fitted by single trends with smooth transitions between dwarf galaxies and clusters, which points to a common origin for their blue stragglers. (2) Systematic decrease of blue straggler fraction with encounter rate and density is inconsistent with the collision scenario. Instead, this points to encounters preventing blue straggler formation in our globular clusters. (3) The expressions shown in Equations 3.6 and 3.7 describing the exponential decay of the frequency of blue stragglers with both density and encounter rate arises naturally if the relative decrease in the fraction of blue stragglers goes as the ratio between the age of the system and the collision time. It is worth pointing out that collisional blue stragglers might have shorter lifetimes than systems formed through mass-transfer (Chatterjee et al., 2013). This means that we cannot rule out the possibility that a fraction of blue stragglers in denser systems still formed through collisions involving binaries (that would have otherwise undergone mass-transfer to form a blue straggler) and that they quickly evolved away from the blue straggler region. We argue that this would be only a second order effect, because the differences in the lifetimes of blue stragglers produced by the different mechanisms is much less than the one needed to explain the decline in the frequency of blue stragglers observed for our clusters.

Aside from our study, there is mounting evidence favoring a binary origin for blue stragglers. A direct link between blue straggler stars and binaries has been determined by Preston & Sneden (2000), who derived a binary fraction of 68% among their metal- poor ﬁeld blue straggler stars, and Mathieu & Geller (2009) who estimated a binary

65 fraction of 76% among blue straggler stars in NGC 188. Palomar 13, one of the clusters with the highest blue straggler frequencies is known to have a relatively high fraction of binary stars, 30±4% (Clark et al., 2004), and many of their blue straggler stars were proved to show signiﬁcant velocity variations, suggesting these are unresolved binary systems (Bradford et al., 2011).

In our globular cluster sample, the radial distributions of blue stragglers are in most cases indistinguishable from those of red giant branch or main sequence stars, consistent in principle with the binary scenario. However, a clear central concentration of blue stragglers is observed in a few clusters: Palomar 4, Palomar 13 and Palomar 15, while a bimodal distribution might be present in Eridanus. At first glance, this may seem contradictory with our interpretation of figure 3.6 that higher density environments favor the destruction or separation of binary or multiple systems progenitors of blue stragglers, but the trend of frequency with density followed by different objects should not necessarily be expected to hold within a single system. Fregeau et al. (2009) studied the evolution of binaries in dense stellar systems and found an increase with time of the core binary fraction, which could be understood as a consequence of a complex interaction between mass segregation of binaries into the core, and their subsequent destruction there. In addition, once formed, blue stragglers can also migrate toward the central regions through mass segregation. In summary, dynamical processes likely to occur in globular clusters severely complicate the interpretation of the trends observed within an individual object.

3.4.3 Discriminating between Blue Straggler Counts and Young Star Counts

Since globular clusters have been historically classiﬁed as old single stellar populations with no young stars, confusion between blue stragglers and young stars is not a problem in these systems. Thus, the blue plume observed in these systems can be immediately attributed to blue stragglers

In section 3.4.1 we have extensively shown why the population of stars located bluer and brighter than the old main sequence turnoff are genuine blue stragglers as opposed to young stars. Therefore, we can use the blue straggler population of these galaxies as a control sample, to which other population of blue stragglers found in different stellar systems can be compared. Therefore, if a stellar system (that we will denominate sys1) has similar densities and luminosities than the galaxies in this sample, we can assume that the relations found genuine blue stragglers hold also for sys1. However, in principle, we do not know if the stars of sys1, located bluer and brighter than its main sequence turnoff (or blue plume region), are blue stragglers or young stars. But, as described 3.3.1, systems with that properties have a genuine blue straggler 7 over BSS red giant branch fraction of FRGB = 0.29 ± 0.01. Therefore, if we measure in sys1 the 7meaning that these stars have been proven to be blue stragglers as opposed to young stars

66 amount of stars in the blue plume region in the same way we have done it in this work, we will obtain the number of blue straggler plus the number of young stars locating this region. Then, we can use equation 3.2 to discriminate what fraction of the stars in the blue plume of sys1 are genuine blue stragglers and what fraction of stars are young stars. For example, if the number of stars in the blue plume over the number of red giant branch stars in sys1 is close to 0.29%, then this probably means that there are no young stars in sys1, and the stars in the blue plume are only due to blue stragglers. On the other hand, if the number of stars in the blue plume over the number of red giant branch is N and this number is signiﬁcantly larger than 0.29%, this probably means that the young stars in the blue plume over the number of red giant branch stars is close to N−0.29. However we remind the reader that to increase the odds that this technique is successful the method to derive the blue straggler fractions has to follow strictly the method described in this chapter.

An example of how this technique is applied for the case of the Carina dSph galaxy is shown on Chapter 5, with small adaptations that had to be implemented due to the presence of intermediate age populations present in Carina.

3.5 Conclusions

We have presented a comprehensive analysis of the blue straggler star population in a representative sub-sample of Galactic outer halo satellites. This photometrically homogeneous sample includes ten low density globular clusters, three classical dSph galaxies and nine of the recently discovered ultra-faint dwarf galaxies. Despite their diverse physical properties, all these satellites are relatively loose and scarcely populated when compared to inner halo globular clusters, where most blue straggler star studies have been carried out. Given the extremely long collision times of our systems, collisions involving single, binary or triple stars can only account for a small fraction of the blue stragglers of our highest density clusters, while their inﬂuence on dwarf galaxies should be negligible. Our sample provided an opportunity to study blue straggler populations in a new density/luminosity regime. We claim that the dominant blue straggler formation mechanism in these type of systems is mass-transfer or mergers in binary or multiple star systems. In the higher encounter rate regime spanned by our globular clusters, encounters prevent blue straggler formation by altering the conﬁguration of the star systems that would otherwise produce blue straggler stars.

Our results can be summarized as follows:

1. We found blue stragglers to be ubiquitous among globular clusters and dwarf galaxies, including the ultra-faint dwarfs.

2. The blue straggler populations in both classical dSphs and ultra-faint dwarfs show a remarkably high and constant distribution of their fractions over an absolute magnitude range of more than six magnitudes, and a density range of two orders of

67 magnitude.

3. The behavior of the frequency of blue stragglers is well ﬁtted by single trends with smooth transitions between dwarf galaxies and clusters, which points to a common origin for their blue stragglers.

4. The fraction of blue straggler stars is high and ﬂat in the extremely low encounter rate regime spanned by dwarf galaxies, while it decreases exponentially with increasing stellar density or encounter rate for the regime spanned by our outer halo globular clusters.

5. There is a well-deﬁned anti-correlation between the fraction of blue straggler stars and absolute magnitude for the outer halo clusters in our sample. This trend has already been observed in inner halo clusters and it is also interpreted as a consequence of the binary origin of the blue straggler population.

6. Comparing the magnitude distribution of the observed blue stragglers in dwarf galaxies with those of simulated single stellar populations, we ﬁnd that for blue stragglers in dwarf galaxies to be young stars, they would have to correspond to a 2.5 ± 0.5 Gyr old population. In addition, to match the observed blue straggler fractions seen in galaxies, young stars would have to comprise between ∼ 1–7% of the total number of stars. Such ﬁne-tuned requirements make it unlikely that we are mistakenly classifying young stars as blue stragglers.

7. The radial distribution of blue stragglers in most objects is statistically consistent with the ones found for their red giant branch and main sequence stars. Only a few exceptions are found, notably the central concentration seen in Palomar 4, Palomar 13, Palomar 14 and the bimodal distribution in Eridanus. In all these cases, dynamical processes, like mass segregation, are likely to alter the primordial binary population and therefore the interpretation of the trends observed within individual objects is not straightforward.

8. Based on the fact that we prove that the blue straggler stars present in this sample of young stars are not young stars, we have developed a method to discriminate blue straggler fractions from young star fractions in systems with luminosities and densities similar to the ones of our Milky Way satellite galaxies.

68 Chapter 4

Star Formation History of Milky Way Satellites

4.1 Deﬁnitions and Generalities of the Star Forma- tion History

The star formation history of a stellar system is the mathematical description of, at minimum, when a galaxy formed its stars. It can also describe the composition of those stars, usually computed as the metallicity or iron abundance, and the regions of the system in which the diﬀerent populations of stars were formed.

In practice, the star formation history is usually described as follows:

SFHi,j = S(agei → agei + ∆agei; metj → metj + ∆metj) (4.1) where, as shown on equation 4.1, SFHi,j, is the stellar mass per unit of time formed at ages between agei and agei+∆agei and with metallicity between metj and metj+∆metj. As we see in this formula, from the observational point of view, the star formation history is always a discrete function, since it can not be measured at every single value of age or metallicity, and instead we can only measure it at given intervals of age (from 1 to nage) and metallicity (from 1 to nmet). In principle, the star formation history can be measured at different resolutions for different ages, which is why instead of one single value of ∆age we have an array of values, as denoted by the ∆agei term in equation 4.1. Likewise, the different resolutions in metallicity are denoted by the term ∆ metj. The star formation history is usually calculated in units of stellar mass per unit of time. Therefore, the amount of stellar mass formed per unit time, within a given interval of age, can be described as:

69 nmet X SFRi = ψ(agei → agei + ∆agei) = SFHi,j (4.2) j=1

This term is by deﬁnition the Star Formation Rate, and SFRi corresponds to the amount of stellar mass formed per unit time of stars with ages between agei and agei+∆agei. The sum in equation 4.2 goes from the lowest metallicity we can measure (met1) until the largest (metnmet), and hence, SFRi, includes the stars formed at the corresponding age interval, at any metallicity.

Similarly, we can deﬁne the amount of stellar mass formed per unit of metallicity interval (dex), at a given range of metallicity as:

nage 1 X MDF = φ(met → met + ∆met ) = × SFH × ∆age (4.3) j j j j ∆met i,j i j i=1

Where the term described corresponds to the Metallicity Distribution Function and corresponds to the amount of stellar mass formed per unit of metallicity interval, of stars with metallicities between metj and metj+∆metj. The sum in equation 4.3 goes from age1 to agenage, and hence, MDFj, includes the stars formed at the corresponding metallicity interval, at any age.

Given that both the star formation rate and the metallicity distribution function are normalized to the length of the intervals, in which each value is deﬁned, we can recover the total stellar mass formed by the system by doing:

nage nmet X X Mstars = SFRi × ∆agei = MDFj × ∆metj (4.4) i=1 j=1

All these deﬁnitions are used to describe the star formation history of the Carina dSph galaxy in Chapter 5, and the form and units of measurement used in that Chapter correspond always to the ones described in equations 4.1 to 4.4

The description of the star formation history of a stellar system is one of the most important physical characterizations of an object, given that it describes quantitatively all its evolution from the moment it was formed to the current epoch. It is related to various physical processes that govern the formation of stars, such as gas cooling, SN feedback, chemical enrichment of the interstellar medium, interaction with external stellar systems (such as mergers or tidal forces), among many others. For this reason, the star formation history can tell us key information about the conditions of the

70 environment in which a certain system was formed. Moreover, the star formation history is related to practically all the observables of a stellar system, such as luminosity, metal content, surface brightness, color, etc, and therefore, it is extremely important to interpret the observed properties of an object.

4.2 The Synthetic CMD Method

The star formation history of stellar systems can be recovered through a wide variety of techniques. The age and metallicity of the star leave imprints on the observables of stellar systems and these observables are then used to try to recover the star formation history of a particular object.

The current levels of star formation can be recovered though a large variety of methods, that include measuring the emission in H-alpha, radio wavelengths, far infrared, ultraviolet and recombination lines. Nevertheless, these calculations are based on the eﬀect that very young and massive stars produce on the observed emission from the object. Therefore, once the stellar population ages, and their youngest stars have already evolved, most of those eﬀects disappear, and the stellar population has to be studied in some other way.

Another possibility to recover the star formation history of a system, is by measuring the integrated light1 received at different wavelengths, and compare the magnitudes and colors of the object with simulated systems that represent different star formation histories (i.e., different combinations of single stellar populations). By these means we can recover the combination of stellar populations that best matches the photometric properties of the data (e.g. James et al., 2006), and obtain the star formation history. Analogously, integrated spectra is also widely used to recover the star formation history (see for example Tojeiro et al., 2007; Dressler et al., 2004; Baldry et al., 2002). Even though this type of studies have the advantage that can be applied for very distant objects, the results suffer from substantial degeneracy, and they sharply loose age resolution for populations older than 1 Gigayear (Mateo, 1998).

For close-by stellar systems, the properties of individually resolved stars have to be studied in order to fully exploit the information provided by them, and avoid a sig- niﬁcant source of degeneracy. Resolved stellar population studies started with Walter Baade in the satellites of M31. He realized that their stellar populations were diﬀerent from the stars typically observed in the Milky Way (Baade, 1944a,b), based on their color distribution. For this reason these stars were denominated Population II stars, whereas young stars were denominated as Population I, and these terms are used up to these days. Based on the discovery that agglomerations of stars in a CMD corresponded to evolutionary tracks of stars, various CMDs started to get derived for a number of star clusters and galaxies. The purpose was to derive, among other things, their star

1The sum of the light from all the stars and components of the system.

71 formation history. Early attempts were based on the isochrone fitting method, to determine at the same time the distance and age of given stellar population (e.g. Sandage, 1953, 1958). The general scheme was fitting the region spanned by individual stars in a CMD with an isochrone, which is a theoretical curve, that represented the color and magnitudes of stars from a single stellar population. Each of these theoretical populations were in tun defined by a specific age and metallicity. Even though the isochrone fitting method can be very useful for deriving the age and composition of simple stellar populations, like globular clusters, it looses meaning when multiple stellar populations overlay in a CMD, as the case of complex systems like dwarf galaxies. In this case, the best way to determine the full star formation history, is by using the synthetic CMD method (Tosi et al., 1991; Dolphin, 1997; Harris & Zaritsky, 2001). This technique is based on recovering the history of an object, based on the photometrical properties of individually resolved stars. The general method is that using models of stellar evolution (e.g. Dotter et al., 2008; Bressan et al., 2012), synthetic populations of stars are constructed. Then the properties of these synthetic populations are compared to the ones of the stars observed, to chose for the stellar population that best describes the data.

The process starts with a set of isochrones and a CMD of the stars observed in the system. Each isochrone is associated with a given age and metallicity, and is conformed by a list of stars with different masses along with their brightness at different photometric bands. The first step is to construct a single synthetic stellar population CMD from each isochrone. For this, a set of stars must be generated to build the artificial CMD. First, an array of stellar masses is extracted from an initial mass function (IMF), which describes the theoretical mass distribution at which stars are formed, and whose most famous examples are the Salpeter IMF (Salpeter, 1955), the Miller-Scalo IMF (Miller & Scalo, 1979), the Kroupa IMF (Kroupa, 2001), and the Chabrier IMF (Chabrier, 2003). Then, colors and magnitudes are derived for each stellar mass generated. These departures are chosen according to the values in the isochrone. After this, the routines add small departures from the magnitudes predicted by the isochrones to the mass generated, according to the photometrical errors present in the real data CMD, and their purpose is to mimic the observational errors in the synthetic data. Then, with the final colors and magnuitudes of stars generated, a synthetic CMD is created for each isochrone. Figure 4.1, shows a sketch of this process. Three different isochrones (top panels) represent different ages and metallicities, and from each one a synthetic single stellar population CMD is created.

After this, a synthetic Hess diagram is built from each synthetic CMD. These diagrams show the number of stars at each region of the CMD, defined by a small interval in color and in magnitude. Hess diagrams are useful to analyze the characteristics of a CMD in a quantifiable manner, rather to study it just “by eye”. Figure 4.2 shows an example of a transformation from the synthetic CMDs created in fig. 4.1 (top panel), to their corresponding Hess diagrams (bottom panels). Each Hess diagram, represents a single stellar population model, corresponding to a given age and metallicity. These

72 73

Figure 4.1 Constructing synthetic CMDs from Isochrones. Each artificial CMD (bottom panels) is built from its corresponding isochrone (top panels), according to a Kroupa IMF (Kroupa, 2001), and magnitude errors that grow quadratically for dimmer stars. Each isochrone corresponds to a specific age and metallicity, which is shown in the text of each panel. For each isochrone 10000 stars were generated for the corresponding synthetic CMD. models are denoted as Single 1, Single 2, and Single 3 (abbreviated as S 1, S 2, and S 3, respectively), and represent the eigenvectors from which the final solution will be constructed. The population Single 1 represents an age of 12 Gyr and an abundance of [Z/H]=-1.88, population Single 2 represents an age of 6 Gyr and an abundance of [Z/H]=-1.4, and population Single 3 represents an age of 1 Gyr and an abundance of [Z/H]=-1.27.

After the single population Hess diagrams are constructed, these are combined to build the composite stellar population Hess diagrams. Each combination is composed by different relative fractions of each single stellar population model. As shown in figure 4.3, the three single stellar populations Hess diagrams of our example (left panels), are combined using different relative fractions to create the three composed stellar population Hess diagrams (middle panels), Composed 1, Composed 2, and Composed 3, respectively. Each composed stellar population model has a set of coefficients associated, that represent the relative fraction of each single stellar population conforming them. These coefficients, describe the star formation history associated with each model, and it is composed of a set of values, each one indicating the number of stars (or alternatively stellar mass) that were formed at each age and metallicity

Finally, all the composed stellar population model Hess diagrams are compared to the Hess diagram of the observed data, to calculate the differences between both of them. Then, the composed population with less differences with the data is chosen as the best representation of the observations, and hence, its associated star formation history is chosen as the star formation history derived for the data. As shown on figure 4.4, the three synthetic composed stellar population Hess diagrams of our example are compared to the Hess diagram of the data. For each region of the CMD, a difference between data and model is calculated, and with all these differences a χ2 is calculates for each model. In the case shown in figure 4.4, the model Composed 2 displays the lowest χ2 (1224 against 4053 and 2297 for the other models), and therefore, the star formation history of this model is chosen as the one derived for the data. In this case, the chosen star formation history consist of 8000 stars from the population Single 1, 12000 stars from the population Single 2, and 6000 stars from the population Single 3. The middle panel of the first column of figure 4.4, shows the relative fraction of stars corresponding to each single stellar population.

The general scheme of a typical synthetic CMD method, was explained above step by step until the final star formation history is derived. Different applications of this method differ mainly on the way the goodness of the fit is calculated (e.g. chi-squared, poissonian chi-squared), and how the best solution is chosen among the entire parameter space covered. The number of stars that belong to each single stellar population, is a free parameter that has to be calculated to determine the final star formation history. Usually a large number of metallicities and ages have to be included to reproduce realistic star formation histories. Therefore, the final number of free parameters is very large and computationally expensive. This is one of the largest complications of synthetic CMD studies, because it implies that most times, the entire parameter space

74 75

Figure 4.2 Constructing Hess diagrams from artiﬁcial CMDs. A Hess diagram (bottom panels) is constructed from each artiﬁcial CMD (top panels), and shows the number of stars in each region of the CMD. Each of these Hess diagrams represents a synthetic single stellar population model, denominated Single 1, Single 2, and Single 3, respectively. 76

Figure 4.3 Constructing composed synthetic stellar populations from single stellar populations. The different Hess diagrams of the synthetic single stellar populations (first column) are combined using different relative fractions from each population to create three different composed stellar populations (second column). The relative fraction of each single stellar population for each composed population is shown in the last column, and represents the associated star formation history for each composed model. 77

Figure 4.4 Comparing composed models with data. Composed synthetic stellar population Hess diagrams (second column) are compared to the Hess diagram of the data (third column) to calculate the residuals between them (last column). The χ2 calculated for each model is shown in the text of the plots in the last column. The first column shows the associated star formation history for each composed model, or equivalently, the relative fraction of stars in each model that belong to each single stellar population. In this example, Composed 2 displays the lowest χ2, and thus the star formation history in the middle panel of the first column is chosen as the better representation of the data. of possible solutions can not be completely navigated in the search of the best possible solution. Other problem present in this type of studies, is that different combinations of age and metallicity in the stellar populations produce sometimes very similar results, in most regions of the CMD. This is known as the “age-metallicity” degeneracy, and to try to break it, some authors assume an age-metallicity relationship for the stellar populations. This relation is obtained by setting some level of enrichment of the interstellar medium as a function of age. Another possibility to break the age-metallicity degeneracy is to obtain independent measurements for the metallicity of the stars in the object, for example from spectroscopy.

Despite these diﬃculties associated with synthetic CMD analysis studies, this technique keeps being the most eﬃcient for determining star formation history of resolved stellar populations (Tolstoy et al., 2009). Moreover, forthcoming advances in the stellar evolution models, and minimization algorithms, along with better observations, will certainly improve further the reliability of this method.

4.2.1 Our Application of the Synthetic CMD Method: Talos

In this section I am going to explain the speciﬁc routine I used, for the case of the Carina dSph galaxy (Chapter 5), and the one that I plan to apply in the near future for a large fraction of the objects in the catalogue presented in Chapter 2. This routine is called Talos and it was presented in the article of de Boer et al. (2012).

The Talos routine is a set of Fortran scripts developed to derive the star formation history. It uses a set of isochrones to create synthetic populations that are then contrasted to observations, as generally do all the synthetic CMD methods, and as it was described in the previous section. The main diﬀerence between Talos and most routines used for deriving the star formation history, is that along with the CMD it can also use the metallicity distribution function form the object as input information. This implies that it does not require assumptions on the age-metallicity relationship as long as there are independent measurement for the metallicity of the stars available, for example from spectroscopy.

A synthetic single stellar population of age “a” and metallicity “met” in Talos, is created extracting masses of stars according to a Kroupa IMF (Kroupa, 2001). Then the magnitudes in diﬀerent passbands of each star are calculated based on an isochrone of age “a” and metallicity “met”. Starting from the magnitude values described in the isochrone for each mass, the ﬁnal values at each band are calculated in Talos, considering the photometric error, completeness fractions2, reddening and binary fraction of the real (observed) data. Therefore, Talos considers quantitatively all these characteristics of the observations when generating the models, and thus, the models are

2These factors correspond to the number of stars that are recovered in the photometry at each range of magnitude, over the total number of stars present in the system at that same magnitude range

78 directly comparable to the observations

Once all the Hess diagrams corresponding to each single stellar population are created, as described in the previous section, composed models are created by combining the different single stellar populations models using different relative fractions. Then, after the Hess diagram of a combined stellar population is created, Talos selects the region of the CMD where there exist independent metallicity measurements, and calculates the metallicity distribution model of the combined stellar population in that CMD region. As shown in figure 4.5, there is a region of the CMD of the data (black box in upper left panel) where a significant fraction of the stars have metallicity measurements, and a metallicity distribution function is calculated for those stars (upper right panel). Then the same region of the CMD is used to select a subsample of the stars in the combined stellar population models, as shown in the lower left panel of figure 4.5. From those stars a metallicity distribution function is constructed for the combined stellar population model, which is then compared to the metallicity distribution function of the data. Therefore, each combined stellar population model is defined by a matrix Hi,j denoting the number of stars in each CMD region, and an array Mk denoting the number of stars from the region with metallicity measurements in each metallicity bin.

To calculate the Hess diagram and the metallicity distribution function of the data, Talos ﬁrst decontaminates the sample for sources that lay in the same region as the cluster or galaxy, but that do not belong to the system. To do this, Talos calculates the Hess diagram of a region outside the extent of the object being studied. Then, it normalizes that Hess diagram for the spatial extent of the “contamination” region, so that the normalized contamination Hess diagram correspond to the ones that would be measured if the contamination region area was equal to the area of the “system” region, where the Hess diagram of the data is calculated. After this, the ﬁnal decontaminated Hess diagram of the data is created by subtracting the normalized contamination Hess diagram to the Hess diagram of all the sources in the “system” region. On the other hand, the metallicity distribution function provided by the user to Talos, has to be decontaminated in advance. To do this the user can select the stars that belong to the system from the metallicity sample, for example, using kinematic criteria, such as selecting the ones with velocity measurement coherent with the velocity of the system.

After the Hess diagrams and metallicity distribution functions of the data and the models are constructed, the goodness of the ﬁt for each model is calculated as:

"ncol nmag nmet # 2 X X hi,j X mk χPoisson = 2 Hi,j − hi,j + hi,j ln + Mk − mk + mk ln Hi,j Mk i j k (4.5)

where Hi,j and hi,j, indicate the number of stars in the Hess diagram of the model

79 Figure 4.5 Constructing the metallicity distribution function of the data and the model. Upper left panel shows the decontaminated CMD of the data, where the stars with metallicity measurements are color coded by metallicity range. The region of the CMD where signiﬁcant metallicity measurements exist is denoted by a black box. Then a metallicity distribution function is calculated for the stars in this box (lower right panel). The box denoting the CMD region with metallicity measurements, is then used to select stars from the combined stellar population model (lower left panel) and construct the metallicity distribution function of the model (lower right panel), which is then compared to the one of the data.

80 and the data, respectively, in a specific CMD region. The index “i” goes over all the color bins in the Hess diagram, and the index “j” goes over all the magnitude bins in the Hess diagram. The terms Mk and mk denote the number of stars in the metallicity distribution function of the model and the data, respectively, in a specific 2 metallicity bin. The index “k”, goes over all the bins of metallicity. The term χPoisson, in equation 4.5, indicates the Poissonian Chi Squared. As demonstrated in Dolphin (2002) the chi-squared minimization equals maximizing the likelihood ratio in the case that the errors are normally distributed, and equal in magnitude for the different models fitted. However, neither these assumptions are true in synthetic CMD fitting, because the data follow a Poisson distribution and the error grows linearly with the value obtained in each model. Therefore, Dolphin (2002) defined a poissonian chi squared, whose minimization is equivalent to maximizing the likelihood ratio, but this time for poissonian distributed data.

The only thing left, to select the model that best reproduces the star formation history of the system, is to find the composed stellar population model that minimizes the poissonian chi squared. The number of free parameters defining the solution correspond to the number of isochrones used in the fitting, niso. Therefore, the entire parameter space, where the final solution is searched for, has niso orhotogonal axes. Now, I will define Smax, as the maximum number of stars, allowed by Talos, from each stellar population. Analogously, I will define ∆S as the difference in stars from a given population, of 2 neighbor solutions. The possible number of stars from each population in a given solution are then, 0, ∆S, 2×∆S, 3×∆S, ... , Smax. Therefore, the total niso numer of solutions contained in the entire parameter space is (Smax/∆S) . Since this number is extremely large, sampling the entire parameters space looking for the best solution would be extremely computer-time consuming, and unable to do in practice. For this reason, Talos uses a Downhill Simplex Algorithm, as described in Numerical Recipes (Press et al., 1992). This algorithm is based on moving uniderectionaly in the parameter space, each time looking for regions with lower chi squared until the global minimum is found. Talos starts by calculating the poissonian chi squared in a random location. Then, it evaluates the goodness of fit in all neighbor locations of the parameter space. These neighbors are ∆S away from the original location, in any of the niso directions that define the parameter space. In practice, this means that the neighbor solutions evaluated, have ∆S stars more or ∆S stars less from a given single stellar population than the original location, and the numbers of stars from all the other stellar populations are the same than the ones in the original location. Then, after it has evaluated all the neighbor locations in the parameter space, it moves ∆S in the direction that decreases the poissonian chi squared the most. After this, it repeats the process starting from the new location, and keeps repeating until moving in any direction increases the poissonian chi squared, and therefore, has reached a local minimum of the poissonian chi squared. Finally, to check if the local minimum is a global minimum, it starts the entire process again starting from various different random locations. Then, if the vast majority of trials converge to the same solution, and this solution corresponds to the lowest local minimum, the solution is picked as the model that best reproduces the data, i.e., the final star formation history of the object.

81 All the characteristics of Talos described above, were the main reasons why I chose this routine for the work of this thesis. It has significant advantages with respect to other procedures mainly for the following three reasons: (1) it uses all the information from the CMD to fit the star formation history and not just key fingerprints of it. (2) Uses as an input the metallicity distribution function, and therefore, no assumptions on the enrichment of the system have to be made. (3) It considers many characteristics from the photometry when constructing the models, which makes this models directly comparable to the data.

4.3 Star Formation History Measurements on Milky Way Satellites

In this section I will brieﬂy summarize the current state of studies regarding the star formation histories in local dwarf galaxies.

One of the first approaches about the star formation histories of galaxies was the one made by Searle et al. (1973), who derived theoretical U,B,V colors for different type of galaxies, based on their star formation histories and IMFs. About a decade later, different papers started to analyze different properties of galaxies as indicators of the star formation rate at different epochs (Gallagher et al., 1984; Hunter & Gallagher, 1986). These indicators were colors, H-alpha luminosity, and emission line ratios.

A couple of years later, Hodge (1989), proposed a type of diagram to visualize the star formation history, which are denominated Hodge Boxes. These figures are 3D plots showing the star formation rate in the vertical axis, and the metallicity and age of each stellar population in the horizontal axes. Despite the simplicity in the idea of Hodge, it revealed explicitly for the first time that the star formation history and the chemical enrichment history, cannot be interpreted independently. These diagrams are used until the present days to represent star formation histories, although by the moment it was defined by Hodge, the observations were not powerful enough to enable using this representation with real data.

Shortly after this, the launch of the Hubble Space Telescope in 1990, the advent of large new generation ground based telescopes, and the advantages of CCD cameras, revolutionized the astronomical observations.

The improved quality of the new observations allowed the first studies of star formation histories through the synthetic CMD analysis (e.g. Tosi et al., 1991; Dolphin, 1997). As mentioned in the previous section, this technique has proven to be the most powerful tool to derive the star formation histories of resolved stellar populations, like the local dwarf galaxies. It takes full advantage of the information from different regions of the CMD simultaneously, and quantitatively compares these properties with simulated data generated based on different stellar evolution models.

82 Presenting the work done using this and other techniques, Grebel (1999), was one of the first ones to compile comprehensively the different measurements on star formation histories of local dwarf galaxies. One of the most important conclusion in this work, and one that is thoroughly repeated in similar studies, is that no two galaxies have the same star formation history. Moreover, the formation time, duration of episodes of star formation, and relative fraction of the different stellar population in local dwarf galaxies, varied widely from system to system. Despite these large differences, Grebel (1999) classifies the star formation histories detected in the local dwarf galaxies, in “continuous” and “episodic” star formation. The author also found that many of these galaxies display age and metallicity gradients, mainly in the sense that the inner regions were in average younger and more metal rich than the outskirts. This implied that the star formation lasted longer in the central regions of these systems while stars were formed only in the relatively early epochs of the galaxy in the outer regions. Other very important result present in this study, is the apparent common epoch of formation for the different local dwarf galaxies. Even though all these galaxies share an episode of star formation at ages older than 10 Gyr, the relative fraction this population represents varies widely from system to system. Moreover, the exact age was difficult to determine at the moment, because it depends largely on the position of the oldest main-sequence turnoff, which requires a proper photometric characterization of stars that are sometimes too faint to be detected.

One of the most important uses of studying the star formation histories of local dwarf galaxies (and of stellar systems in general) is to elucidate physical processes and conditions that governed the evolution of these systems. For the case of the local dwarf galaxies, one of the main specific goals in this sense is to analyze the differences between dIrr galaxies and early-type dwarf galaxies (dSphs and dEs). The connection between formation scenarios and star formation histories for dwarf galaxies is largely motivated by the aforementioned position-morphology relation, first noted by Einasto et al. (1974). This relation states that the dIrr galaxies are located farther from the Milky Way center and have higher gas contents than dSph galaxies. Potential differences found between dIrr and dSph in terms of the star formation history could hint for intrinsic differences in the overall evolution of these systems, and could help justify the position-morphology relation. The main purpose is to check whether the different morphological types correspond to qualitatively different formation and evolution scenarios, or whether there is a smooth transition between the different type of dwarf galaxies. In the work of Grebel (1999), the author interprets the results as hinting for the second option. The young populations recently discovered in some local dSph galaxies (e.g Stetson et al., 1998; Gallart et al., 1999), along with the transition phase found in between dIrr and dSph, were used to justify that hypothesis in that work. Grebel (1999) also argues that the lack of gas in dSph and some dE seems inconsistent with their star formation histories, and claims that photoionization might be causing that, even though gas is indeed present, it cannot be observed as HI emission. The author also argues that tidal compression from the Milky Way at apogalacticon could be responsible for the episodic star formation observed in some of these systems, which would act cooling this hot gas.

83 Anther important work that reviews the star formation history of local dwarf galaxies in these years is the one of Mateo (1998). In this study, the author highlights the importance of using deep CCD photometry of individual stars to derive the star formation history of local dwarf galaxies, since both young star formation indicators and integrated color analysis loose age resolution for populations older than 1 Gyr. He also emphasizes that the observation of local dwarf galaxies led the community for the first time to low age, low metallicities studies, which up to that point had never been covered by observations. The first conclusion in Mateo (1998) is that the are no 2 local dwarf galaxies that share the same star formation history, confirming what was pointed out by Grebel (1999). He also observed that, despite they all have recent star formation, many dIrr have also significant old populations. Mateo (1998) concluded that these recent star formation episodes found in different types of local dwarf galaxies have short durations (10–500 Myr). The author highlighted that age resolution for populations older than 1 Gyr decreases significantly. Therefore, since short duration episodes were observed in the only age range with high age resolution, the author inferred that all the star formation episodes are short duration, but can we only discriminate the most recent ones. In this work, the author observes that local dwarf galaxies tend to have redder horizontal branch morphologies3 than globular clusters, a property that he interpreted as local dwarf galaxies being younger than the oldest globular cluster. Mateo (1998) highlights that no galaxy, excepting Ursa Minor, is conformed only by old (> 10 Gyr) stars4. Moreover, the author claims that some galaxies like M 32 and possibly Leo I have very few or no stars older than 10 Gyr. This might stand opposite to the common old formation episode claimed by Grebel (1999), although, the sample of galaxies without old stars found by Mateo (1998), is still far from representative of local dwarf galaxies. Mateo (1998) also concludes that there is no trend in average stellar ages with distance to the Milky Way. This observation is contrary to what had been claimed by van den Bergh (1994), who found that galaxies closer to the Milky Way (or M31), were in average older, as expected from the position-morphology relation observed for local dwarf galaxies. Finally, just as the work of Grebel (1999), Mateo (1998) highlights the different discoveries of spatial gradients in the star formation of different early-type dwarf galaxies, like And I, Leo II, NGC 205, or Antila.

A couple of year after the reviews of Mateo (1998) and Grebel (1999) were published, the first results of the Coimbra Experiment (Skillman & Gallart, 2002), were presented. The main purpose of this work was to check the consistency of the method of converting color-magnitude diagrams of galaxies into star formation histories. To do this, they gathered 10 research groups, and they analyzed a single set of HST data on the bar of the Large Magellanic Cloud. Each group used different assumptions, stellar evolution models, and approaches. However, despite the large differences between the results of different groups, most of the differences were consistent within the errors,

3These observations are known as second parameter effect because they are deviations from the main effect which is redder horizontal branch morphologies for higher metallicity stellar systems. Therefore, metallicity is often considered as the main parameter driving the horizontal branch morphology, while second parameter effects are often attributed to age or abundance patterns 4Something that would significantly change with the discovery of the ultra-faint dwarf galaxies

84 and relatively small for ages older than 1 Gyr. The reliability of synthetic CMD analysis was further confirmed in the work of Skillman et al. (2003), where the authors obtained consistent star formation histories of the galaxy IC 1613, using three independent methods. Nevertheless, the authors got some discrepancy for the older stellar population, since they did not reach the oldest main sequence turnoff. On the other hand, in the study of Gallart & Lcid Team (2007), the authors reach for the oldest turnoff but derived a formation histories for IC 1613 that was considerably different than the one in Skillman et al. (2003). The differences were attributed to the fact that both studies analized different regions of the galaxy, and even though both regions were located at similar galactocentric distances, inhomogeneities in dIrr galaxies might produce that locations like these display significantly different star formation histories. All these studies remind us that the consistency in the derivation of star formation histories in local dwarf galaxies through the synthetic color-magnitude analysis, depends on a proper photometric characterization of the oldest turnoffs, as well as a spatial coverage that includes a significant region of the galaxy.

The most comprehensive work about the current state of star formation history measurements on local dwarf galaxies is the one provided by Tolstoy et al. (2009) In this work the authors highlighted that a large fraction of the galaxies in the Local Group had been investigated by that moment in terms of their star formation history, and that many of them had been homogeneously derived (e.g. Dolphin et al., 2005). In the review article of Tolstoy et al. (2009), the authors claim that a significant fraction of the advances made in star formation histories of local dwarf galaxies is given by the arrival of the HST cameras WFPC2 and the Advanced Camera for Surveys. Even though excellent quality photometry can be performed with the space telescope, the largest disadvantage of these cameras is the relatively short spatial coverage. The . 200 arcseconds FOV diameter of them is too small to cover local dwarf galaxies, whose half light radius are most times tens of arc minutes. Therefore, only the most central parts of these systems can be examined in a significant fraction of these works. This is specially critical if we consider all the evidence supporting the presence of age and metallicity gradients in local dwarf galaxies (e.g. Mart´ınez-Delgadoet al., 1999; Rizzi et al., 2004; Harbeck et al., 2001). On the other hand, the tables in Tolstoy et al. (2009) summarizing the star formation histories of local dwarf galaxies show that, for most of the objects where the star formation history has been derived using wide field instruments, the photometry is not deep enough to characterize the oldest turnoffs, and therefore, largest uncertainties remain in the oldest star formation episode (Gallart et al. 1996 in NGC 6822, McConnachie et al. 2006 in DDO 210, de Jong et al. 2008b in Leo T, Gullieuszik et al. (2008) in Leo II, and de Jong et al. 2008a in Böotes I, Canes Venatici I and Ursa Major II).

A general trend mentioned in Tolstoy et al. (2009) is that galaxies that are currently forming stars in the Local Group, are located more than 300 kpc away, conﬁrming the long standing morphological transformation possibility.

The authors claimed that dSph galaxies are usually associated with large galaxies like

85 our own, and the local dSphs are preferentially located close to the Milky Way (. 130 kpc). They also stated that arguably the ultra-faint dwarf galaxies are an extension of the dSph to lower luminosities. They concluded that the biggest difference with dIrr galaxies is their lack of gas and recent star formation. According to Tolstoy et al. (2009), most dSph do not show star formation in the last hundred million years, and in various cases the star formation is mainly formed by an episode of star formation which is older than 10 Gyr (e.g. the Sculptor dSph). According to the authors, the hypothesis of the recent activity as the main differentiating property for dSph and dIrr is supported by their overlapping structural properties, such as their position in the magnitude v/s size diagram, or in the magnitude v/s surface brightness diagram. Tolstoy et al. (2009) indicates that dSph galaxies are very similar to the old extended components of late-type galaxies, although as stated above, a proper characterization of their stellar population requires wide field surveys, to span the large spatial extents that some of these galaxies cover.

A special case mentioned in Tolstoy et al. (2009), in terms of its star formation history, is the Carina dSph galaxy, which has been proven to have formed its stars episodically (Hurley-Keller et al., 1998; Mateo et al., 1998). This episodic star formation history is composed of 3 different episodes of star formation ,separated by periods consistent with no star formation at all. These features were confirmed quantitatively from synthetic CMD analysis, although they were already observed in their stellar populations (Saha et al., 1986; Smecker-Hane et al., 1994). This shows the great attention that has been paid to Carina’s star formation history, mainly since its unique episodic episodes among Milky Way satellites can hint us important information about its evolution. Nevertheless, until now, no conclusive scenario has been able to explain the physical processes governing its particular star formation history. In the next and final chapter of this thesis we derive important conclusions about this relevant topic.

In the work of Tolstoy et al. (2009), the authors also highlight the presence of dSph galaxies that share all the main characteristics of this type of galaxies, but that are located significantly farther from the Milky Way, and hence, represent exceptions to the well justified position morphology relation. This is the case of Tucana (880 kpc away) and Cetus (775 kpc away). Moreover, the case of Cetus displays properties that are opposite to the ones of dIrr, since it shows no star formation in the past 8 Gyr and its blue plume is believed to be due only to blue stragglers. In this review is also highlighted the importance of not confusing blue stragglers with young stars when analyzing CMDs for deriving star formation histories. The work we have presented in Chapter 3 provides a powerful tool to discriminate between young star and blue straggler, an issue that has been already noted as crucial for deriving accurate star formation histories, and whose implementation can significantly change the conclusions about the derivation of star formation histories in local dwarf galaxies.

For the case of dIrr galaxies, Tolstoy et al. (2009), indicated that these systems have been historically used to prove metal poor star formation both in the young and old regime. The early studies of star formation histories in dIrr galaxies (e.g. Tosi et al.,

86 1991; Marconi et al., 1995; Gallart et al., 1996; Tolstoy, 1996) showed that the star formation in these galaxies varied widely from system to system and even for diﬀerent regions of each galaxy. It was also shown by these studies that the star formation in dIrr occurred in long moderate intensity episodes that are separated by short stages with no activity, described as a gasping regime by Marconi et al. (1995).

As mentioned earlier, dIrr galaxies are characterized by displaying considerable HI mass fractions and current star formation. Tolstoy et al. (2009) also indicated that these galaxies have probably been forming stars throughout all their lives, although they display a great variety of rates in different epochs. These galaxies are generally far from their hosts (Milky Way or M31) at galactocentric distances > 400 kpc, with the noticeable exception of the SMC (and LMC if we consider it as a dwarf). It is very interesting to highlight at this point that in the most recent years, the Magellanic system has been proposed to be falling for the first time to the Milky Way Halo (e,g. Besla, 2015). This finding can be very helpful to explain why the Magellanic clouds are clear outliers of the position morphology relation found in the Local Group.

In the review of Tolstoy et al. (2009), the authors highlight the importance of the Hubble Space Telescope for deriving the star formation histories of dIrr galaxies. The main reason why this telescope is particularly helpful for studying this type of galaxies, is the relatively large galactocentric distances of dIrrs compared to dSphs. These distances imply that great photometric sensitivity is needed to characterize their dimmest stars, and their angular extent is significantly smaller (excepting the Magel- lanic Clouds), than the one of dSph. Therefore, HST appears as the perfect telescope to study these galaxies, and it has made possible, for example, the distinction between main sequence stars and blue loop stars in dIrr galaxies. These stars have proved to map the spatial variation of the most recent (< 800 Myr) star formation (Dohm-Palmer et al., 1998, 2002). The large spatial variations of the most recent star formation found by these and other studies, are crucial to understand the physical processes governing the evolution of dIrr galaxies, and prove the complexity of the star formation in these systems. The spatial and time variations of the star formation, according to Tolstoy et al. (2009), are consistent with the stochastic self propagating star formation model proposed by Seiden et al. (1979). In this model, the star formation is triggered by the activity in neighbor regions, which propagates in different directions in time scales of several tens of thousands years. This behavior indicates that the complexity of the star formation in dIrr galaxies might be more complex than originally pictured by Marconi et al. (1995). The differences between these conclusions might be due to the increased spatial resolution of the latest star formation history studies.

In the review of Tolstoy et al. (2009), it is also mentioned a transition type of local dwarf galaxies in between late-type (dIrr) and early-type (dSph and dE). These galaxies have no recent star formation, but show the presence of HI in them. A clear example of these systems is LSG 3, whose lack of recent activity is interpreted as a regular parenthesis in its steadily declining star formation rate. They concluded that the transition between early and late-types may indicate the average mass at

87 which galaxies lose their gas. This effect would be more pronounced if the galaxy is located closer to a large galaxy for a significant fraction of its history. The variety of environments that have surrounded the different galaxies throughout their lives, could then explain why the transition is not sharp.

For the case of the ultra-faint dwarf galaxies, we have already mentioned that correspond to the least luminous stellar systems ever discovered, and might represent an extension of the dSph galaxies or a totally diﬀerent type of galaxy with diﬀerent formation channels. The ultra-faint galaxies discovered so far are relatively close to the Milky Way (McConnachie, 2012), with galactocentric distances between 23 and 160 kpc. The 2 notable exceptions to this trend are LeoT and Canes Venatici I. It is worth mention- ing though, that the latter galaxy, was originally denominated as an ultra-faint dwarf galaxy because it was discovered by the SDSS survey, however, its structural properties are much more similar to the “classical” dSphs than to the ultra-faint dwarf galaxies (McConnachie, 2012). On the other hand, it is currently very hard to discriminate if the observed measurements for these systems are revealing the intrinsic properties of ultra faint dwarf galaxies, or are just a product of observational bias (Simon & Geha, 2007; Martin et al., 2008a).

Synthetic CMD analysis has been performed for some of these systems (de Jong et al., 2008a), however, the star formation histories of these systems can be very hard to study, because as we indicated in Chapter 2, there is a large problem with Galac- tic contamination in low stellar count systems (Martin et al., 2008a). Without very deep photometry, in most cases spectroscopic data is needed to identify the stars that belong to the ultra-faint dwarf galaxy, and even with that information sometimes the separation can be problematic (e.g. Geha et al., 2009; Belokurov et al., 2009). In some cases a different approach is forced to be used, which is looking for distinctive bright population tracers such as HB or RR-Lyrae. These tracers are most times very sparse in the ultra-faint dwarf galaxies, but their distinction with the Galactic component is straightforward, and they provide a good estimation for the age and spatial extent of the star formation episodes (e.g. Dall’Ora et al., 2006, in BöotesI). According to Tolstoy et al. (2009), the ultra-faint dwarfs are being disrupted (which was questioned by Martin et al. 2008a). They also claim that these galaxies might possibly be just enhancements along streams associated, for example, with the disruption of the Sag- gitarius dwarf galaxy (e.g. Geha et al., 2009; Belokurov et al., 2009). In these cases a proper high quality characterization of their stellar populations and metallicities can prove/disprove possible connections between the ultra-faint dwarf galaxies and different Milky Way components.

Despite all the observational caveats reviewed by Tolstoy et al. (2009) about the derivation of star formation histories of ultra-faint dwarf galaxies, some contributions have come in the last years. One of these studies is Okamoto et al. (2012), who derived the stellar populations and structural properties for 4 ultra-faint dwarf galaxies, using deep photometry that extended below the oldest main sequence turnoﬀs. In this work, the authors concluded that these systems were being tidally disrupted, and that

88 were conformed basically by a single old stellar population. This was interpreted as an indication that the gas in these galaxies was removed more eﬃciently than for brighter dSph in their early epoch of formation. Similar results were obtained in Brown et al. (2014). This article presents the star formation histories of 6 ultra- faint dwarf galaxies, using HST photometry and medium resolution spectroscopy from DEIMOS5. The results found in this study are consistent with all these galaxies having formed 80% of their stars by redshift z∼ 6 (12.8 Gyr ago) and 100% of their stars by redshift z∼ 3 (11.6 Gyr ago). From these results, the authors concluded that the star formation in all these extremely low mass galaxies, was suppressed by a global external event such as the reionization of the Universe. The information of the star formation histories provided by these studies set important constraints on the nature of the ultra-faint dwarf galaxies. The single episode synchronized star formation histories, provide important evidence to support the hypothesis that these systems are galaxies with an evolution path that is qualitatively diﬀerent than the one of brightest dSph galaxies. At the same time goes in opposition to the theory described in Tolstoy et al. (2009) pointing that these systems are just enhancements in stellar streams of the Milky Way. These star formation histories also start to settle the long standing debate about the existence of purely old stellar population galaxies (e.g. Mateo, 1998; Tolstoy et al., 2009), and shows that these systems can display star formation histories that are practically indistinguishable from those of globular clusters.

The main purpose of this section was to highlight the variety of information that can be obtained from the study of star formation histories of local dwarf galaxies. As seen throughout this chapter, these works can help us perfect the morphological classification of local dwarf galaxies, derive information about the physical processes governing the evolution of these systems, and provide significant information to interpret the different scaling relations local dwarf galaxies follow, among many other things.

Despite the great number of studies that have been published in the last years, and the valuable information they have provided, there is a lot yet to be made in terms of deriving high accuracy, reliable star formation histories of local dwarf galaxies. As seen throughout this section, there are a number of characteristics of new observations and methods that would strongly benefit this type of studies. These include deep-wide photometry reaching the oldest turnoff and the outer regions of large angular extent galaxies, proper photometric characterization of recent star formation, spatial separation of the different component of the galaxy, use of spectroscopically derived stellar metallicities as inputs, and the use of synthetic color-magnitude diagram methods. All these characteristics are present in our derivation of the spatially resolved star formation history of the Carina dSph galaxy, described in Chapter 5, where part of the high quality data presented in Chapter 2 is used to derive the star formation history of this galaxy using the method described in this Chapter, and employing the technique derived in Chapter 3 to discriminate between young stars and blue stragglers. In Chap- ter 5, I will summarize the most important aspects and conclusions about that work, and highlight the reasons why it represents an important improvement with respect to

5DEep Imaging Multi-Object Spectrograph on the W. M. Keck Observatory.

89 previous studies about the same topic.

90 Chapter 5

Spatially Resolved Star Formation History of the Carina dSph Galaxy

5.1 Introduction

As we reviewed in Chapter 2, dwarf galaxies are crucial for understanding galaxy assembly and evolution. They are some of the oldest systems in the Universe, and they inhabit the most numerous type of dark matter halos in the framework of a Λ-CDM Universe (e.g. Kauﬀmann et al., 1993). These systems gave origin to larger galaxies like the Milky Way in the early Universe via hierarchical merging (Unavane et al., 1996, e.g.). The dwarf galaxies in the Local Group are particularly interesting, since their proximity allows us to resolve them into individual stars. Thus, it is not surprising that these galaxies have been studied in more detail than any other galaxies (see Tolstoy et al., 2009, for a recent review).

Carina is a Local Group dSph galaxy especially important for galactic evolution, since it is the only one in the Local Group showing an episodic star formation history (see for example Smecker-Hane et al., 1994; Hurley-Keller et al., 1998; Bono et al., 2010; de Boer et al., 2014). According to these and other authors, the episodic star formation episodes may be either related to interactions with the Milky Way or with internal evolution of gas and stars. From the entire sample of objects our catalogue of local dwarf galaxies, presented in Chapter 2, we chose to analyze the star formation history of Carina, before any other, due to its particular evolution. The episodic star formation history of Carina can provide a unique opportunity to study the physical processes responsible for the especial history of this galaxy, and hint us what environment conditions were diﬀerent in Carina than in the rest of the Milky Way satellite galaxies, to leave this imprint in the observed star formation history.

As it was presented in Chapter 4, the star formation history of local systems like Carina has been derived mainly through the study of their CMDs and more recently,

91 via the synthetic CMD analysis (e.g. de Boer et al., 2014), described in section 4.2.

Carina was first thought as a purely intermediate population galaxy, but the presence of RR-Lyrae indicated for the first time the existence of an old (> 10 Gyr) stellar population (Saha et al., 1986). Multiple main sequence turnoffs confirmed later that Carina had an episodic star formation history (i.e., clearly distinguishable episodes of star formation activity separated by episodes where practically no stars were formed). Another key feature of Carina’s CMD is its narrow red giant branch, which was at first interpreted as the result of a low metallicity spread (see Rizzi et al., 2003, and references therein). In that work the authors measured a color spread of the red giant branch of σV−I = 0.021 ± 0.005 and derived a metallicity of [Fe/H]=−1.91 with a spread of 0.12 dex, in agreement with early spectroscopic studies of upper red giant branch stars in Carina (e.g. Armandroff & Da Costa, 1991). More recent spectroscopic observations have claimed a much larger metallicity spread in this galaxy (Helmi et al., 2006; Koch et al., 2006). The latter study finds a mean metallicity of [Fe/H]∼ −1.4 and a spread of 0.92 dex. More recently, de Boer et al. (2014) used Koch’s metallicity distribution function along with CMD information from their deep photometry and derived a self-consistent, complex star formation history for Carina, indicating a strong age/metallicity degeneracy present in Carina. Confirmation or refutation of these results would shed important light on the origin of Carina’s photometric and spectroscopic features.

From all these previous works, there is a general agreement that Carina has a well separated episodic star formation history consisting of at least two episodes; an old and an intermediate age one. On top of this, several authors (Hurley-Keller et al., 1998; Mateo et al., 1998; Monelli et al., 2003) claimed a third episode in Carina, consisting of young (< 1 Gyr) stars. However, the exact age and duration of all these episodes are still uncertain.

Another important feature found in Carina, is the metallicity and age gradient of its stellar populations. In Carina’s external regions, the relative prevalence of older and more metal-poor stars increases (Mu˜nozet al., 2006; Battaglia et al., 2012; McMonigal et al., 2014; de Boer et al., 2014). On the other hand, spatial distribution of stars in Carina have also revealed evidence of tidal encounters occurring (Mu˜nozet al., 2006; Battaglia et al., 2012), which led some authors to try to explain the star formation episodes of the galaxy as the result of close encounters with the Milky Way (Piatek et al., 2003; Pasetto et al., 2011). Despite these works having set constraints about Carina’s orbit, they have reached only some success in explaining Carina’s star formation history as a result of tidal shocks or ram pressure. Another possible origin for the properties of Carina’s star formation history is internal evolution. Gas depletion for example might produce the negative age/metallicity gradients, whereas gas heating (see for example Revaz et al., 2009) might explain the gap in star formation.

In summary, we are still far from ﬁnding a scenario explaining the evolution of Carina that is consistent with all star formation history, chemical enrichment, orbital information and gas dynamics. Achieving this would give fundamental information

92 Figure 5.1 Stars in Carina ﬁeld. Left: g v/s g-r dereddened CMD including all stars within 1.3 × rtidal of Carina, where stars have been selected according to their photometrical errors and a chi/sharp criteria. Red solid line shows the 50% completeness level. Right: Star map of Carina, showing the diﬀerent regions where the star formation history was determined. Internal region: 0 < r/rtidal < 0.3 ; middle region: 0.3 < r/rtidal < 0.6 and external region: 0.6 < r/rtidal < 1.3 about the formation and evolution of the Local Group.

In this work we use deep/wide photometry along with archive metallicities to derive the star formation history of Carina. By making use of the Talos routine (de Boer et al., 2012, see section 4.2.1) we take into consideration all the information in the CMD (and not just some key ﬁngerprints) along with the metallicity distribution function to derive the star formation history in a consistent way. The high quality of the photometry along with the spatial extent of the observations (2 square degrees), enable us to make three independent measures of the Carina’s star formation history at diﬀerent concentric regions. In this way, we increase the amount of information extracted about the evolution of this dwarf galaxy.

5.2 Data

5.2.1 Photometry

Photometry for Carina was obtained using the CFHT MegaCam, as part of the southern region of the catalogue presented in Chapter 2. For the case of Carina, a total of 16 h m s ﬁelds in a 4×4 conﬁguration were observed, centered on the object (α0 = 06.41. 01.70,

93 ◦ 0 00 2 δ0 = −50 57.58.0), achieving a total area of ∼ 2 deg . This translates into a full coverage within 1.3 times the tidal radius (rtidal) of Carina and partial coverage from 1.3 to 3.0×rtidal. The data was processed to obtain the final file for Carina, with calibrated Sloan g− and r− magnitudes and their corresponding errors for each star, along with the equatorial coordinates, and the photometry quality parameters chi and sharpness. The process of obtaining this file, was the same used for all the objects in the southern part of our catalogue, and it was extensively described in section 2.3.

To remove non-stellar objects and spurious detections from the ﬁle containing all Carina sources, we used the DAOPHOT chi and sharpness parameters. We selected sources with a Chi < 5 and −0.4 < sharpness < 0.4. In addition, we only used detections with photometric uncertainties smaller than 0.1 magnitudes in both bands. Fig. 5.1 shows the spatial coverage along with the ﬁnal calibrated CMD of all the sources selected as stars.

5.2.2 Spectroscopy

We use archive, low resolution (R ∼ 6500) Ca II triplet spectroscopy from Koch et al. (2006). This catalogue contains measurements for 435 red giant branch kinematical members of Carina. From this sample 430 sources were matched to stars in our catalogue according to their equatorial coordinates. Equivalent widths of Ca II were originally translated to [Fe/H] using the calibrations of Zinn & West (1984) and Carretta & Gratton (1997). However, in this study we used the calibration of Starkenburg et al. (2010), mainly to avoid saturation at low equivalent width values. Our ﬁnal sample compromises measurement out to 1.0×rtidal of Carina and spans a range in metallicity of −3.8 <[Fe/H]< 0.0.

5.3 Method for deriving the Star Formation His- tory of Carina

To derive the star formation history of Carina we used the Talos (de Boer et al., 2012) for applying the synthetic CMD method. We presented a general overview of Talos in section 4.2.1, where we described the minimization algorithm it uses for deriving the star formation history. In this section we will explain how Talos was implemented for deriving the star formation s history with our Carina data. For that we will describe how we built all input ﬁles Talos used for this study and all the input parameters chosen based in the properties of our Carina data.

94 5.3.1 General Setup

To run Talos and calculate the star formation history, we have to determine a set of parameters associated to our data. These parameters are used to populate the synthetic models that are directly comparable to the data. The region chosen from the CMD to fit the star formation history consisted of a magnitude range of 21 − 24.5 and a color range of −0.5 − −1.15. This region was chosen to avoid CMD locations significantly affected by completeness corrections or contamination sources, while maximizing our signal. The individual bins that defined the Hess diagrams were chosen to have a size of 0.1 in magnitude and 0.025 in color. This dimensions were selected small enough to be able to trace the different features of the stellar populations in the CMD, and big enough to avoid significant shot noise in each bin.

The distance to Carina was determined using the magnitude of the RR-Lyrae and red clump stars from our data, obtaining a distance module of 20.08, consistent with recent values derived in the literature (e.g. Weisz et al., 2014; VandenBerg et al., 2015; Coppola et al., 2013). The binary fraction was determined using the color distribution of stars in the main sequence of Carina and the value obtained was 0.4; consistent with values obtained in other dwarf galaxies (Geha et al., 2013). The reddening was calculated on a star-by-star basis, using its equatorial coordinates and correlating them to the Schlegel maps (Schlegel et al., 1998). This way, we correct for diﬀerential reddening throughout the galaxy.

To take advantage of the large spatial extent of our data, we determine the star formation history independently for three different concentric regions of Carina: Internal: 0 < r/rtidal < 0.3 Middle: 0.3 < r/rtidal < 0.6, and External: 0.6 < r/rtidal < 1.3 For each of these regions, Talos was run using the corresponding Hess diagram (see left panels of fig. 5.5) and metallicity distribution function (blue histograms of fig. 5.6).

For each region, we performed several runs on Talos, each time shifting the grid defining the input photometry/spectroscopy and/or grid defining the resulting star formation history. The final star formation history was determined as the average of all the run solutions while the associated errors were taken as the standard deviations of the different runs. This procedure has been shown to reproduce the main source of statistical errors on the star formation history results (Aparicio & Hidalgo, 2009).

95 Figure 5.2 Convoluting contamination process represented by Hess diagrams in which the color of each box indicates the number of stars in that color-magnitude bin. Left: Hess diagram of contamination region (r> 2.0 × rtidal), counts have been normalized multiplying the area of the contamination region over the area of the complete Carina region. Middle: contamination region normalized hess diagram smoothed using a 6 × 6 gaussian Kernel convolution. Right: hess diagram of the complete Carina region without substracting the contamination. Shot noise is signiﬁcantly reduced in the smoothed diagram while still preserving the most important features that are also seen in Carina hess diagram.

Figure 5.3 Completeness v/s magnitude. Fraction of stars recovered by the artificial photometry as a function of input magnitude g (left) and r (right). The different lines show stars at different input colors

96 Figure 5.4 Photometrical error v/s magnitude. Error estimated as the module of the difference between input and recovered magnitude for the artificial star test as a function of the input magnitudes g (left) and r (right). For each magnitude bin we plot the median of the difference module along with the 1-sigma interval.

5.3.2 Additional input ﬁles

5.3.2.1 Contamination

The Carina dSph is located at relatively low galactic latitude (b = −22.2) and it 0 spans a large area in the sky (rtidal = 31.0). Thus, Carina’s CMD is signiﬁcantly contaminated by Milky Way foreground stars. To analyze the stellar populations in Carina, these stars together with all the background sources have to be subtracted from the Hess diagrams. To account for their contribution, we constructed a contamination Hess diagram using our photometry from the region beyond 2.0×rtidal and extending to 3.0×rtidal. In this way, this Hess diagram does not include features from Carina’s stellar populations and it has enough stars to be a representative sample of the contamination sources.

To improve the reliability of our contamination Hess diagram, we applied a 6 × 6 Kernel to smooth it. This signiﬁcantly reduces the shot noise while still preserving the main features of the diagram. The smoothed contamination Hess was then scaled according to its spatial area and subtracted from the original Carina Hess diagrams. Normalized original and smoothed contamination diagrams are shown on Fig. 5.2, and compared to the Carina Hess diagram.

97 5.3.2.2 Artiﬁcial star test

To calculate the photometric error and the completeness values as a function of color and magnitude we run artificial star tests on our photometry, adding stars to our individual images using the IRAF task, ADDSTAR. Then, photometry was repeated following the same procedure used for the original images to measure the fraction of artificial stars recovered and the magnitude error. The latter of which, was calculated as the difference between the input magnitude and the one obtained with DAOPHOT. For each iteration, only 100 stars were added to avoid a significant alteration of the image crowding. A total of 280000 stars were generated, covering all the regions of the CMD and the spatial extent of Carina inside 1.3 × rtidal. The distribution of the input magnitudes generated had a maximum at g ∼ 25. In this way, we increase the resolution of the completeness fraction determinations at the magnitudes where these fractions vary sharply. After performing the photometry on all the artificial stars, we obtained the completeness fraction as a function of magnitude and color for each region of Carina. These values were then used to correct the number counts in the Hess diagrams of Carina’s different regions. Fig. 5.3 shows the completeness fraction as a function of g− magnitude (left panel) and r− magnitude (right panel) for three different color ranges in the complete Carina region inside 1.3 × rtidal. The left panel of this figure shows that we reach a 50% completeness at g ∼ 24.5, with values at redder colors being slightly higher. This implies that we have completeness fractions higher than 50% for the entire CMD region used as input, which has a g− magnitude range of 21–24.5. With the artificial star test, we could also estimate the magnitude error of our photometry, calculated using the module of the difference between input and recovered magnitudes of the artificial stars. The median of this module was calculated for each magnitude bin, and these values were then used to reproduce the photometrical errors on the synthetic stellar populations. Fig. 5.4 shows the median and 1-sigma intervals of the magnitude error estimation as a function of g- (left) and r- (right) magnitudes.

5.4 Results

The star formation history for each of the Carina regions defined in section 5.3.1 was determined by comparing our data with a set of synthetic populations (as described in section 4.2). To generate these models we used isochrones from the Dartmouth library (Dotter et al., 2008) which span a wide range of ages, metallicities, and alpha-element abundances. The range of metallicities used to define both the star formation history and the metallicity distribution function was −3.5 <[Fe/H]< −0.5, which according to Koch’s metallicity sample includes all the stars in Carina. The ages used ranged from 0.50 Gyr to 14 Gyr, which is constrained by the age of the Universe. To increase the resolution of the star formation history, Talos interpolates between the different isochrones and after this process we obtain a constant age spacing of 0.50 Gyr and a metallicity spacing of 0.2 dex.

98 Figure 5.5 Comparing observed and model CMDs represented by Hess Diagrams. Col- ors in plots represent number of stars in that CMD region. Each panel shows at left the CMD of the data in the middle the modeled CMD and at the right the residuals calculated as (model−observed)/errors top: internal region corresponding to 0 < r/rtidal < 0.3 middle: middle region corresponding to 0.3 < r/rtidal < 0.6 bottom: external region corresponding to 0.6 < r/rtidal < 1.3

99 Once the final grid defining the star formation history was determined, Talos starts the fitting process. Fig. 5.5 shows the Hess diagram of the data and the best fit model as well as the residuals for each Carina region. We see in this figure that for all regions, the best fit is an accurate representation of the observations with residuals smaller than one sigma for 80% of the color/magnitude bins and lower than two sigmas for ∼ 99% of the bins. The Hess diagrams show an old and an intermediate population for all Carina regions. But the relative importance of the intermediate population decreases while going to more external regions. These two populations modeled correctly reproduce the main features of Carina’s CMDs, such as main sequence turnoff positions, the gap separating the two main sub-giant branches, and the width of the red giant branch. There are also hints for a young population in all of Carina’s regions, and their genuineness will be analyzed in section 5.5.

Figure 5.6 shows the comparison between data and best fit model for the metallicity distribution functions. The histograms in this figure include only the stars from the CMD region with significant spectroscopic measurements (as the example histograms shown in figure 4.5). For the case of the spectroscopic metallicities we have used, the CMD region covered is 18.0 ≤g≤ 20.4 and 0.50 ≤(g-r)≤ 1.25. It is important to notice that the model histogram in this figure (blue histogram) is different than the ones in figure 5.9, because the metallicity distribution functions presented there include the stars in the model from the entire CMD region.

As it is the case of the Hess diagrams, metallicity distribution functions of the models closely reproduce the observations for all Carina regions, with diﬀerences between the two always within the errors. This ﬁgure indicates that the average metallicity of the red giant branch stars in Carina decreases with radius, showing a large spread at all regions.

The full star formation history derived for each Carina region is shown in ﬁgure 5.7. This plot displays the stellar mass formed at each age/metallicity combination and clearly shows the presence of two main episodes of star formation separated by a star formation gap. The old episode occurred more than 10 Gyr ago and their stars have very low metallicity (−3.0 <[Fe/H]< −2.0). The intermediate age episode started 8 Gyr ago and stopped 2 Gyr ago, increasing its metallicity from ∼ −2.0 to ∼ −1.0 in that period. This shows that the average stellar metallicity at the moment the intermediate age episode began is consistent with the stellar metallicity at the last stars formed in the old episode. The gap of star formation separating the two main bursts represents an epoch were no signiﬁcant star formation took place. This epoch started 10 Gyr ago and stopped 8 Gyr ago.

The central age and metallicity of the two main episodes is consistent through all Carina regions, however, their relative importance varies from inner to outer regions. The internal region is dominated by the intermediate age episode while still having an important contribution from the old one. The middle region displays both episodes with similar relative importance. Finally, the external region is composed mainly by old stars with a very small contribution from the intermediate age episode.

100 Figure 5.6 Metallicity distribution function (MDF) of observations and model in different regions or Carina. Both the model and observed histogram include only the stars from the CMD region with significant spectroscopic measurements to use as input data. In our case this region is defined by 18.0 ≤g≤ 20.4 and 0.50 ≤(g-r)≤ 1.25 top: internal region corresponding to 0 < r/rtidal < 0.3 middle: middle region corresponding to 0.3 < r/rtidal < 0.6 bottom: external region corresponding to 0.6 < r/rtidal < 1.3

101 Figure 5.7 3D Star Formation History of diﬀerent regions of Carina. Color and height of each bin shows the stellar mass formed at that age and metallicity. top: internal region corresponding to 0 < r/rtidal < 0.3 middle: middle region corresponding to 0.3 < r/rtidal < 0.6 bottom: external region corresponding to 0.6 < r/rtidal < 1.3

102 There is no clear evidence for a young (age < 2 Gyr) episode in ﬁgure 5.7, which could happen because there are no young stars in Carina or because their contribution is negligible compared to the two main episodes.

The star formation history as a function of age is shown in fig. 5.8, where we can see the same general features as in fig. 5.7. There are two main episodes separated by a gap in star formation and the relative importance of the old episode increases with radius. This figure also shows that the negative age gradient is steeper between the middle and external region. fig. 5.9 shows the star formation history as a function of metallicity (or chemical enrichment history). In these plots we can see a negative metallicity gradient present in Carina, which is also steeper between the middle and external regions. From the results presented in this figure, we calculated that Carina has a mean metallicity of [Fe/H]=−1.81 ± 0.29 and a spread of σ = 0.54 dex. The final solution for the star formation history of Carina, implies a large dispersion metallicity distribution function and a Hess diagram that reproduces the narrow red giant branch. This shows that these two features in Carina are consistent, and their implications will be interpreted in the following section. From the star formation history derived in Carina we also 6 estimated the total stellar mass formed in the galaxy, which is 1.60 ± 0.09 × 10 M within 1.3×rtidal, and larger than previous values derived for Carina (e.g de Boer et al., 6 2014, derived a value of 1.07 ± 0.08 × 10 M ).

5.5 Discussion

5.5.1 Young Population v/s Blue Straggler Population

The star formation of Carina clearly shows at least two different episodes, however figures 5.5, 5.7 and 5.8, indicate that a young stellar population might also be present. Several studies have claimed the presence of a young population in Carina (e.g. Hurley- Keller et al., 1998; Mateo et al., 1998; Monelli et al., 2003), nevertheless, these stars might be blue stragglers mistakenly classified as young stars. Blue straggler stars are coeval to a given stellar population, but bluer and brighter than its main sequence turnoff, thus, these stars can populate the same region as young stars in a CMD. Therefore, the blue plume of a CMD can be composed of young stars and/or blue stragglers, and their differentiation is not straightforward.

To analyze the nature of the blue plume of Carina, we are going to use the method presented in Chapter 3, to discriminate blue straggler counts from young stars counts. To apply this method we calculated the number of stars in Carina that are located in a region brighter and bluer than the main sequence turnoﬀ. In principle, for the case of Carina we do not know if these stars are blue stragglers or young stars, and thus, we will denominate the sum of both contributions as blue plume counts. Analogously the fraction that these stars represent from the entire sample of stars in Carina will be denominated as blue plume fraction. The idea is to compare the blue plume fraction

103 Figure 5.8 Star formation rate as a function of age for diﬀerent regions of Carina. top: internal region corresponding to 0 < r/rtidal < 0.3 middle: middle region corresponding to 0.3 < r/rtidal < 0.6 bottom: external region corresponding to 0.6 < r/rtidal < 1.3

104 Figure 5.9 Star formation rate as a function of metallicity for diﬀerent regions for Carina. top: internal region corresponding to 0 < r/rtidal < 0.3 middle: middle region corresponding to 0.3 < r/rtidal < 0.6 bottom: external region corresponding to 0.6 < r/rtidal < 1.3

105 Figure 5.10 Boxes in CMDs to count blue stragglers/blue plume (blue) stars and red giant branch stars. Red giant branch boxes are the same for all galaxies, but for the case of Carina the number of stars in this box has to be corrected to calculate the red giant branch stars that come from the intermediate age population. The blue plume box used for Carina is defined to include stars from 1.39 to 1.55 times the mass of the main sequence turnoff of the intermediate age population, while the blue straggler box used for all the other galaxies is defined to include stars from 1.39 to 1.55 times the mass of the main sequence turnoff of the old stellar population. In the case of Carina we counted blue stragglers coming from the intermediate age population since the ones from the old population are lost in the intermediate age population main sequence

106 Figure 5.11 Blue straggler or blue plume fractions for the diﬀerent galaxies. For Carina, we show the number of blue plume stars over the number of red giant branch stars from the intermediate age population. The blue plume counts include, in principle, young stars and blue stragglers formed from the intermediate age stellar population. For all the other galaxies we show the blue straggler fraction calculated as the number of blue stragglers over the number of red giant branch stars. of Carina with the blue straggler fraction1 found in other local dSph galaxies to check what contribution of the blue plume fraction of Carina comes from blue stragglers and what fraction from young stars. The fundament of this method is that as observed in Santana et al. (2013) and Momany et al. (2007), local dSphs share a common fraction of blue stragglers stars. Based on this, if the fraction of stars in the blue plume of Carina is similar to the blue straggler fractions in the other local dSphs, then they are most likely dominated by blue stragglers. But if this fraction is considerably higher than the average blue straggler fractions in dSphs, then the blue plume is probably dominated by young stars.

Blue plume fractions in Carina were calculated analogously as we calculated the blue straggler fractions of local dSph in Chapter 3, and as it is described in detail in Santana et al. (2013). The CMD box used to count blue stragglers in the comparison dSph galaxies was defined to cover a magnitude range including stars from 1.39 to 1.55 times the mass of its old (and only) main sequence turnoff to avoid contamination from main- sequence stars and horizontal branch stars. For Carina we chose a blue plume box that spanned g− magnitudes equivalent to a mass range of 1.39 to 1.55 times the mass of its intermediate age main sequence turnoff. We use a blue plume box for Carina that included potential blue straggler stars from the intermediate age population, since the blue stragglers formed from the old population stars are largely lost in the CMD because they span a region shared with the much more numerous intermediate age population main sequence stars. In this way, the blue plume box in Carina includes

1In this case I use the term blue straggler fraction instead of blue plume fraction since, as shown in Chapter 3, the stars in the blue plume of these galaxies are blue stragglers and there is no signiﬁcant contribution of young stars.

107 potential blue stragglers formed from the intermediate age population and the blue straggler box for the comparison dSph includes blue stragglers formed from the old population, which is the only component present in these galaxies. The advantage of using this box to count blue plume stars in Carina is that it practically does not include blue stragglers formed from the old population of Carina, since the box is located at a magnitude to bright to be reached by blue stragglers formed from old stars, since these stars are relatively dimmer and the mass transfer process for creating a blue straggler increases the magnitude of the star by a limited value. Therefore, the blue plume box deﬁned for Carina the only blue stragglers that are included are those formed from intermediate age stars, and the blue straggler box deﬁned for the comparisonlocal dSph only includes blue stragglers formed from the old stellar population. In this way, even though Carina is a composed stellar population, the comparison made here is between a single stellar population in Carina with the single stellar populations that represent the comparison dSph.

Then, the blue plume and blue straggler fractions have to be normalized to the number of stars in each corresponding stellar population. Therefore, blue plume counts in Carina were normalized using the number of red giant branch stars from its intermediate age population and the blue straggler counts in the comparison dSph were normalized using the number of red giant branch stars from their old stellar populations.

Figure 5.10 shows the boxes used to count blue plume stars in Carina, blue stragglers in the comparison galaxies and red giant branch stars in all the galaxies.

To calculate the number of intermediate age red giant branch stars falling in the red giant branch box used for Carina, we multiplied the number of stars in the box by the fraction of intermediate age stars populating that region of the CMD according to the star formation history derived. The final blue straggler fraction for each of the comparison local dSphs is shown on figure 5.11, along with the blue plume fraction found for Carina. As we can see in this plot, local dSphs share a common blue straggler fraction (as found in Santana et al., 2013) and, Carina shows a blue plume fraction that is completely consistent with the average blue straggler fraction in the other galaxies. This shows that the number of stars in the blue plume of Carina’s CMD is completely consistent with the number of blue stragglers found in other local dwarf spheroidals; and hence, we claim that there is no considerable contribution of young (< 2 Gyr) stars in Carina. Therefore, we conclude that the Carina dSph galaxy had only two episodes of star formation and the small contribution of young stars derived by Talos are due to misclassified blue straggler stars.

108 5.5.2 Star Formation History of Carina: Internal Evolution v/s External inﬂuence

Carina is a local dSph galaxy whose star formation history has raised great interest since its discovery (Cannon et al., 1977), mainly because this dwarf is the only one in the Local Group showing an episodic star formation history. Additionally, Carina displays clear signs of tidal inﬂuence from the Milky Way (Mu˜nozet al., 2006; Battaglia et al., 2012). Therefore, one of the key questions regarding Carina’s star formation history is how much it is governed by interactions with the Milky Way as opposed to being the result of internal evolution.

The quality of the data presented in this work represents a significant advantage for unraveling the nature of Carina’s star formation history. We have g− and r− Sloan band photometry reaching a 50% completeness at g∼ 24.5 which is more than one magnitude fainter than the oldest turnoff. In addition, the field of view of our observations encompasses an area of ∼ 2 deg2, which translates into full coverage until 1.3×rtidal, and partial coverage until 3.0×rtidal. The combination of depth and coverage of the data, is specially important to trace the oldest stellar population, which extends even farther than rtidal, and presents a faint main sequence turnoff. In this work, we used the full hess diagrams of different regions in Carina, along with a metallicity distribution function from archive spectroscopy (Koch et al., 2006), and we compared them to synthetic stellar populations using the routine Talos (de Boer et al., 2012). With this method, we obtained a star formation history composed of two episodes separated by a gap in star formation. The first one corresponds to an old (age∼ 10–13.5 Gyr) population with metallicities in the range of [Fe/H]∼ −3.0– −2.0, and the second one is an intermediate age (2–8 Gyr) episode with metallicity increasing with age from [Fe/H]∼ −2.0 to [Fe/H]∼ −1.0. The old and intermediate age episodes correspond to 53.7% and 44.9% of the stellar mass respectively. This is the first study of the star formation history of Carina, where the old episode is found to be dominant. This result is in agreement with what is often found for local dSphs (e.g. Grebel, 1997). As mentioned earlier, our ability to better trace the old population comes from the high definition of the sub-giant branch and main sequence turnoff of the old population in our photometry. Furthermore, our spatial coverage reaches the most external regions of Carina where the old population is relatively more important. Separating these two main episodes there is an epoch consistent with no star formation at all, occurring between 8 and 10 Gyr ago, which was previously observed by several studies (e.g. Hurley-Keller et al., 1998; Bono et al., 2010; de Boer et al., 2014). Finally, we also found evidence that could be interpreted as a small (less than 2% of stellar mass) contribution of young stars. However, we demonstrated in section 5.5.1 that these stars are most likely dominated by blue stragglers.

The star formation history derived for Carina in this work produces good matches with both the input CMDs and metallicity distribution functions as shown by ﬁgures 5.5 and 5.6 respectively, which demonstrates the large statistical signiﬁcance of our results.

109 This implies for example, that a metallicity distribution with a large dispersion and a narrow color distribution are completely consistent with a complex star formation history, with two important episodes separated by several gigayears. All this hints that an important metallicity-age degeneracy is present in Carina.

If we analyze the star formation history of the inner, middle and external region of Carina separately, we see that all the regions show the same central ages and metallicities for the two episodes. However, as we go to more external regions, the relative importance of the old episode increases at the expense of the intermediate age one. This produces a negative age and metallicity gradient, which is steeper between the middle and external region. In fact the intermediate age population is practically negligible in the external region. The lack of stars younger than 10 Gyr in the external region of Carina, implies that tidal forces from the Milky Way were not important enough to aﬀect the distribution of stars within 0.6 × rtidal. Otherwise it would have erased the gradient in this region or pushed stars to the external region. Nor was strong enough to alter the gas distribution to create star forming conditions outside 0.6 × rtidal of Carina. Furthermore, we ﬁnd two episodes of star formation in Carina instead of three as it has been often claimed (Hurley-Keller et al., 1998; Rizzi et al., 2003; Pilkington & Gibson, 2012; McMonigal et al., 2014). This makes it harder to explain the star formation episodes in Carina as the periodic result of close passages to the Milky Way (as done in Pasetto et al., 2011), specially because proper motions suggest that the orbital period of Carina is close to 2 Gyr (Piatek et al., 2003; Pasetto et al., 2011).

Another mechanism proposed to explain the star formation history of Carina is gas inflow from the Milky Way. In this scenario, the first episode would have ended due to gas depletion, then it would not have formed stars for a couple of gigayears latter until it renewed its gas content, thanks to an inflow from the Milky Way. This gas could not have comed from the Milky Way disk, since the metal content of the stars in the disk at the moment Carina’s second episode started was orders of magnitudes larger that the stars in Carina (Rocha-Pinto et al., 2000; Reid et al., 2007; Ibukiyama & Arimoto, 2002). Thus, the gas should have come from the Halo, through filaments of gas that were not significantly pre-enriched. Even though this is a possible scenario, the infalling gas should have had the same metallicity as the stars from the end of the first episode to reproduce the age-metallicity relation in Carina. In contrast, we believe that the gas that formed the second episode in Carina was enriched within the same galaxy by the stars formed during the first episode of star formation, which would be a simpler way to explain why the metallicity of the stars at the end of the first episode coincides with the stars at the beginning of the second episode. Internal evolution would also be consistent with the fact that the central metallicities and ages of the different star formation episodes in Carina are the same in all the regions of the galaxy. This would not necessarily be the case if the gas that formed those stars would have been accreted at different moment and each region had different levels of influence from each inflow.

For all these reasons, we claim that the star formation history of Carina is dom-

110 inated by internal evolution. Our interpretation is that after the first episode that formed the majority of the stars in Carina, the physical conditions of the gas made the galaxy unable to form stars for a couple of gigayears until it contracted to activate star formation again. We propose that SN feedback might be an interesting mechanism to explain the gap in star formation in Carina. It has been often proposed (e.g. Kaviraj et al., 2007) that feedback can significantly decrease the efficiency of star formation. The hypothesis is that for the large halo masses regime (galaxy clusters) this would be due to AGNs, whereas for low halo masses (dwarf galaxies) it would be caused by SN ejecta. For example, it has been proposed by simulations (e.g. Revaz et al., 2009), 8 that the star formation of systems with low initial mass (Mi < 3 × 10 M ), could be self-regulated. In these systems, star formation is followed by shocks of energy released by the SNs that heat and expand the gas. Given that the gas density is relatively low, the gas cooling times are large and can take up to several gigayears to contract to form stars again. If this is the case, the gas available decreases, due to the first episode of star formation, and hence, it should contract to a radius smaller than the original to reach the density conditions necessary to reactivate the star formation. This coincides with what we observed in this study, where the second episode occurred in a region that is approximately 0.6 × rtidal of Carina. Unlike the great majority of studies about Carina’s star formation history, we found that the first episode of star formation was the most important one, thus the second episode formed fewer stars than the first and we also know that occurred during a longer period of time. This could explain why, unlike the first episode of star formation, the second one did not halt the star formation and the galaxy could keep forming stars more or less regularly from ∼ 8 to ∼ 2 gigayears ago.

What is different in Carina to make it have a qualitatively different star formation history than any other local group galaxy? There seems to be a trend in the local group wherein more luminous galaxies (Irregular galaxies and brightest classical dwarfs) display more complex star formation histories with important components of intermediate and young star formation; while dimmer galaxies (dimmest classical dwarfs and ultra- faints) are composed of single old episodes of star formation (see for example Tolstoy et al., 2009). Galaxies with larger masses have larger potential wells and shorter cooling times for the gas, which enables them to have significant star formation throughout all their history. While galaxies with lower masses cannot retain the gas to form stars after the first episode or the gas cannot cool and contract again. Carina has a luminosity similar to the dimmest classical dwarf galaxies, but unlike them it has an important component of intermediate young stars. However, there is evidence that this galaxy has lost a significant fraction of its mass in the past (Muñozet al., 2006) and this process is ongoing. This means that Carina was significantly brighter than the dimmest classical dwarfs in the past. Thus, its episodic star formation history could represent an intermediate regime that lays between the massive dwarfs with continuous star formation and the least massive dwarfs with one old episode of star formation. In this context, Carina could have been massive enough to retain the gas after the first episode of star formation, but not massive enough to avoid significant expansion of the gas which stopped the star formation, and given the relatively low gas densities it

111 could only cool down and contract to form stars again after a couple of gigayears.

112 Chapter 6

Conclusion

Throughout this thesis I have highlighted the importance of the work done during my PhD program, in the context of the previous advances that have been made in the ﬁelds of Local Group and Star Formation Histories of resolved stellar systems.

In Chapter 2, I summarized the most important studies about the formation and evolution of the Galaxies in the Local Group, and their implication to hierarchical structural formation. In this Chapter, I reviewed the importance of local dwarf galaxies for tracing dark matter halos, and how the comparison between both led to the appearance of the missing satellites problem. This problem implied that the amount of sub halos predicted by Λ-CDM around galaxies like the Milky Way, was orders of magnitude larger than the observed Milky Way satellite galaxies. The solution to this problem came mainly thanks to two type of contributions. The first one is a proper structural characterization of Milky Way satellites that reaches systems progressively fainter each time, along with a better estimation of the observational selection effect present in observations. The second was a proper characterization in simulations of the conditions that had to be fullfiled for a dwarf galaxy to have formed enough stars to be detectable by current instruments. The main conclusion of these studies was that star formation in very low mass halos, is significantly suppressed by the reionization of the Universe, which implies that either this halos are to dim and low surface brightness to be detected, or even they could have not formed stars at all. Other very important topic covered by studies on Local Group galaxies are the scaling relations these systems follow. When the different characteristics of the satellites are plotted against each other, important conclusions about the properties and history of the different type of dwarfs can be drawn. For example, the luminosity v/s size diagram can help us distinguish between dwarf galaxies and globular clusters, even though there is an overlap region where the discrimination is not straightforward. This diagram also shows that the transition between the smallest dwarf galaxies, the ultra faint dwarf galaxies, and largest galaxies is smooth, which has been interpreted as similar formation mechanisms for the different type of dwarf galaxies. Other important observable that provides important information, is the surface brightness of dwarf galaxies. When plotted against

113 the absolute magnitudes of the systems, there appears a clear break between dim and bright dwarf galaxies, separating two regimes that show different behaviors. While the surface brightness of bright dwarf galaxies correlate with luminosity, the dimmest dwarf galaxies seem to have a constant surface brightness value. This possible “floor” for the dwarf galaxy surface brightness has been interpreted in two different manners. Either it reflects a selection effect of observations that can not detect objects with lower surface brightness values, or is indicating that there is a physical process that prevents that dwarf galaxies with lower surface brightness values are formed. The mass of these systems has also shed some important information. It had been proposed that all dwarf galaxies share a common mass scale within ∼ 300 pc from their centers. This also hints that there is a clear limit below which galaxies can not be formed (or detected). How- ever, more recent studies proposed that measuring the mass of galaxies within their half light radius had a more straightforward physical interpretation. This measurement was claimed to be directly related to the total mass of systems, while the mass within 300 pc looses meaning if we consider the large range of sizes that dwarf galaxies cover. Interestingly, when measuring the mass of dwarf galaxies within their half light radius, there is no apparent lower limit for the mass reached yet, and lower luminosity galaxies seem to inhabit smaller mass halos. The evolution of these systems has also been interpreted from the point of view of the stellar metallicity v/s luminosity relation. It has been derived by observations that the metal content of stars is proportional to the luminosity of the stellar system. The most intuitive interpretation would be that the enrichment in dwarf galaxies grows when larger mass potential wells surround them, preventing that the metals expeled by evolved stars escape the galaxy. However, the stellar metallicity is much less dependent on the total mass than it is on stellar mass, or alternatively stellar luminosity. This hints that the baryons themselve regulate the enrichment from star formation, and is probably related with the processes involving gas cooling to form stars.

In this context, we present a state-of-the-art catalogue of Milky Way satellites containing all the stellar overdensities in the Milky Way Halo, located at galactocentric distances larger than 25 kpc1. The complete process for constructing the southern region of this catalogue was presented in Chapter 2, and the results show the great quality of the data obtained. The photometrical depth reached in this catalogue largely exceeds the one obtained in public data, like SDSS. Moreover, our depth is comparable—and is some cases larger—than the one reached in studies that cover only one or a couple of galaxies. However, we homogeneously derived CMDs for practically all the outer Milky Way satellites, reaching a photometrical depth comparable to the one obtained in studies of single galaxies. The ﬁles extracted for each one of the galaxies and clusters in our catalogue, have the further advantage that considere a very large spatial extent for each system. These coverage include always regions larger than the one delimited by the tidal radius of each system, and therefore, imply that each time we are mapping practically the complete extent of the object. The angular areas we cover are orders of

1Excepting the Magellanic Clouds, whose great extension in the sky escape the observing possibilities of this catalogue.

114 magnitude larger, for example, than the ones reached by the Hubble Space Telescope, which provides extremely useful instruments for deriving good quality CMDs thanks to the high accuracy photometry that can be performed with them. On the other hand, the spatial coverage we reach can only be compared with the one obtained with the largest area instruments on ground based telescopes, like the MegaCam instruments used in this study or the DECam in the 4−meter telescope in Cerro Tololo.

Therefore, the catalogue presented in Chapter 2 provides a unique opportunity to derive, for example, high quality structural parameters for Milky Way satellites, and high accuracy stellar populations characterization. These parameters will signiﬁcantly help improve the scaling relations of dwarf galaxies, while analyzing their stellar population would help quantify the evolution of these systems. As described above, and throughout this thesis, all this information will provide important insights about the formation, evolution, and interaction of the objects conforming the Milky Way Halo, and hence, about the formation of the Galaxy itself. This information will also set valuable constraints for the processes governing structure assembly and baryonic physics involved in the formation of stars, which are very relevant topics in modern astrophysics.

The third Chapter of this thesis presented the study of blue stragglers in Milky Way Satellites. The main purposes of developing this work can be summarized in two ideas. The first one was to study was to unravel the properties and formation mechanisms of blue stragglers formed in very low stellar density systems, an environment in which this type of stars had almost never been studied. The second one was to develop a method to discriminate between blue straggler counts and young star counts, given that both populations span similar regions in the CMD. One of the main conclusions regarding this work was that blue stragglers are ubiquitous among Milky Way satellites, despite the low stellar densities these systems display. Moreover, in the low stellar density regime, blue stragglers are even more frequent for lower densities or collision rates, until it reaches a constant fraction for dwarf galaxies. These systems display a high and flat distribution of blue straggler fractions, even though the span a range in luminosities of about 6 magnitudes. We also demonstrated that the stars found in the blue plume region of our local dwarf galaxies are genuine blue stragglers as opposed to young stars. This conclusion was based on various properties we derived for these stars. The first one is their magnitude distribution, which matches the one of blue stragglers in globular clusters, and hence, point to a common origin for both sets of stars. This magnitude distribution is also consistent with what is expected for stars that originate from the mass transfer or merger between two old main sequence stars, just as blue stragglers are believed to be formed. The second one is their radial distribution which in practically all cases is not centrally concentrated, as it is often expected for young stellar populations Tolstoy et al. (see for example 2009). The third one is that the common fraction of blue stragglers in dwarf galaxies implies that for these stars to be mistakenly classified young stars, uniquely fine-tuned star formation histories are required in local dwarf galaxies. Namely, all these galaxies should have formed from 1 to 3 percent of their stars in the narrow age range of 2.5±0.5 Gyr ago. Such coordinated star formation histories, seem highly unlikely, mainly because practically every review

115 dealing with the star formation history of local dwarf galaxies, concludes that there are no two local dwarf galaxies displaying the same star formation history (e.g. Mateo, 1998; Grebel, 1999; Tolstoy et al., 2009). We concluded from all this information, that blue stragglers in local dwarf galaxies and low density globular clusters, are formed through mass-transfer in binary stars, and for slightly larger density systems, the blue straggler fraction decreases because larger fraction of the binaries from which these blue stragglers are formed, are disrupted or widened, hence, preventing blue straggler stars formation.

The characterization of the fraction of blue stragglers in local dwarf galaxies, enabled us to realize that these fractions are constant for these galaxies, and the stars conforming it are genuine blue stragglers as opposed to mistakenly classified young stars. Therefore, we developed a method to discriminate blue straggler counts from young star counts in local dwarf galaxies. This method is based in counting the amount of stars in the blue plume of dwarf galaxies, which in principle, include both blue stragglers and young stars. Then, comparing this numbers with the fraction of blue stragglers we derived for Milky Way satellite galaxies, we are able to discriminate what fraction of the stars in the blue plume of a certain galaxy are young stars and what fraction are actual blue stragglers. This technique provides a very useful tool for the studies of the star formation histories of resolved stellar populations. These studies often analyze what combination of stellar populations reproduce the observed CMDs of the objects studies. However, the formation of blue stragglers is practically never considered when doing this analysis. Therefore, this often translates in the fact that blue stragglers formed from an old stellar population are confused with a young stellar population. This significantly bias the derived values of recent star formation, and it is probably significantly misleading the conclusions drawn from these star formation histories. Ap- plying this technique for discriminating blue straggler counts from young star counts, can significantly help to correct the bias introduced by artificially adding young star counts to the star formation history results. In fact this technique has already been used in the work of this thesis, for the case of the Carina dSph galaxy. Based on this, we concluded that the stars classiffied as young by our routine were actually blue stragglers. This significantly changes the interpretation of the star formation history derived, as I explain below.

The fourth Chapter of this thesis deals with the star formation history of local dwarf galaxies. In this section I presented some basic deﬁnitions related to the star formation history, explained the main properties of the methods used to derive the star formation histories, and highlighted the most important studies performed in the last years regarding this topic.

The description of the star formation history of a stellar system is one of the most important physical characterizations of an object, given that it describes quantitatively all its evolution. It is related to various physical processes that govern the formation of stars, such as, gas cooling, SN feedback, chemical enrichment of the interstellar medium, interaction with external stellar systems. The star formation history can tell

116 us key information about the conditions of the environment in which a certain system was formed. Moreover, the star formation history is related to practically all the observables of a stellar system, such as luminosity, metal content, surface brightness, color, etc, and therefore, it is extremely important to interpret the observed properties of an object.

In that Chapter we saw that the star formation history of local dwarf galaxies is not easy to categorize, mainly because no two different galaxies display the same star formation history. Nevertheless, one of the most important trends found for the star formation histories of local dwarf galaxies, is that the stellar population of early-type dwarf galaxies (dE and dSph) tend to be older than the stellar population of late-type dwarf galaxies, namely the dIrrs. Since, local dSph also tend to be located closer than dIrr to their progenitor galaxy (Milky Way or M31), the difference in age between these galaxies, indicates that there is a morphological transformation produced by the influence of the Milky Way, in the sense that it suppresses the recent star formation for systems close to it. This is really similar to what was observed in terms of the gas content of these galaxies, which is higher for the dIrr than for transition galaxies and practically inexistent in dSph. This relation is known as the position morphology relation and also points to a transformation in galaxies due to the influence from the Milky Way.

In the lowest luminosity regime spanned by the ultra faint dwarf galaxies, studies of their star formation histories have showed that these systems are practically fossils from the early Universe, because they are composed only by a very old stellar population. From the characterization of their stellar population some authors have concluded that there is a common epoch of formation for these systems. This has been interpreted as a global eﬀect in the Universe—like reionization—truncating the star formation history of these systems.

For the case of low luminosity dwarf galaxies like classical dwarf spheroidals and ultra faint dwarf galaxies, the star formation history is specially important to make a proper structural characterization of these systems, which is not always straightforward given their low stellar counts. The star formation history of these systems can also help distinguish their stellar populations from those that could be contaminating the CMD of the object, for example from stellar streams of the Milky Way. On the other hand, connecting stellar populations of dSph galaxies with streams or clouds of the Milky Way, can help us recover the merging history of that particular galaxy and make possible connections between different galaxies with common accretion origins. The star formation history of these systems can also provide important information to unravel the physical processes behind the evolution of this galaxies. One of the most noticeable examples of this is the Carina dwarf galaxy, for which its episodic star formation history has often been interpreted as the result of the tidal influence from the Milky Way, gas inflow in the Halo, or internal evolution of the gas from which stars are formed.

For dIrr galaxies, the star formation histories have often focused on the most recent

117 star formation episodes, since their are a common property in these galaxies and they are in most cases very prominent. In fact, some dIrr galaxies have young stellar populations that are so important that the identification of the old population in the CMD is not straightforward (as is the case of Leo A). Moreover the very recent star formation episodes in a system can be traced through a number of observables other than the CMD, such as, radio emission, X-ray emission, infrared continuum, and recombination lines. The observations have shown that the recent star formation in dIrr varies significantly from galaxy to galaxy, and even within the same galaxy. The strong time and spatial variation of the recent star formation in these galaxies has been interpreted as the result of activity triggered by star formation in neighbor regions that propagate in all directions in timescales of several tens of kilo years. On the other hand, old stellar populations have been detected in various dIrr galaxies, displaying very different fractions of the total stellar mass for different systems. These old stellar population have been found to resemble the stellar populations in dSph galaxies, hinting for common origins for both type of galaxies which where then subject to different environment conditions.

Described above are some of the main pieces of information about the origin and physical processes governing local dwarf galaxies that can be revealed by analyzing the star formation history of these systems. Then, in the same Chapter of star formation histories, I presented the most popular method to derive the star formation histories in these systems, which is the synthetic CMD method. As shown throughout the text, this is the most powerful technique for deriving the star formation history of resolved stellar systems, because it uses the information of the amount of stars in all the regions of the CMD and performs a quantitative comparison with synthetic stellar population models to derive the combination of synthetic stellar populations that best describes the data, and hence, the star formation history of the system. In that Chapter I also presented the main characteristics of the specific routine I used for deriving the star formation history of the Carina dSph galaxy, called Talos. This routine is very powerful to derive star formation histories, and provides various advantages with respect to other synthetic CMD analysis routines, mainly due to the following reasons. (1) Talos uses all the information from the CMD to fit the star formation history and not just key fingerprints of it. (2) It uses as an input the metallicity distribution function of the data, and therefore, no assumptions on the enrichment of the system have to be made. (3) It considers many characteristics from the photometry when constructing the models, which makes these models directly comparable to the data. In summary, we presented the implementation of a very powerful technique to derive the star formation histories of resolved stellar systems. And the application of this technique to the local dwarf spheroidal galaxies would shed information about the structural characterization, evolution, and assembly history of these systems.

Finally, Chapter 5, presents a study that takes advantages of the work involved in all the previous chapters of this thesis. This study involves the derivation of the star formation history of the Carina dSph, derived with the Talos routine, and using the technique presented in Chapter 3 to discriminate between blue straggler counts and

118 young star counts. The high quality data used, along with the use of a state-of-the-art routine for deriving the star formation history, and the proper characterization of blue stragglers in this study, provided us with results with unprecedented accuracy. The star formation history of Carina has been long studied since its discovery, and our research confirms most of the main properties often found in the star formation history of this object, like the gap in star formation, the age and metallicity gradients, and the episodic behavior of the star formation episodes. However, our analysis provided valuable new information about the evolution of Carina. For example, this was the first study to make a proper photometric characterization of the stars in the oldest main sequence turnoff, and with this we obtained that the old stellar population is the one dominating the star formation, and not the intermediate age population as it had always been claimed. Moreover, using our technique to discriminate between blue straggler counts and young star counts, we realized that the stars that were considered as young stars by Talos, were actually misclassified blue stragglers. This in turn, implies that Carina is conformed by two episodes of star formation separated by a gap, instead of three episodes as it had been often proposed. Talos enabled us to use independent metallicity measurements as inputs as input when deriving the star formation history. With this, we significantly decreased an important source of bias, which is the age-metallicity degeneracy. By this means, we could also match explicitly the star formation history with the chemical enrichment of the galaxy, and proved that the narrow red giant branch displayed by Carina is consistent with the wide age range of their stellar population, a topic that had often intrigued the people studying the CMD of Carina. Moreover, moment and duration of the star formation episodes derived in our study, along with all the other details of our star formation history derived, presented a statistical significance that had never been reached before by the studies about the star formation history in this galaxy. With all these new information in hand we concluded that the star formation history of Carina is governed by internal evolution as opposed to tidal influence from the Milky Way as it has been often proposed. This interpretation is supported by the observation that no intermediate age stars are found in the external regions of Carina, which implies that tidal forces exerted by the Milky Way were not strong enough to drag stars to this region nor move significant gas masses to trigger star formation in the outer region of Carina. Therefore, we deem as unlikely that tidal force is the main physical process governing Carina’s evolution. Moreover, we observed two instead of three episodes of star formation, and thus, the time difference between the different episodes of star formation is considerably larger than the orbital period of Carina, which is about 2 Gyrs. Therefore, in this scenario is hard to explain the star formation episodes in Carina as the result of the periodic close passages from the Milky Way, where the tidal influence is larger. Other evidence supporting internal evolution rather than other scenarios, is the observation that the stars formed at the beginning of the second episode have the same metallicity as the stars formed at the end of the first episode. This hints that the gas from which the stars of the second episode were formed, was enriched by the stars from the first episode. We interpreted this as an evidence for an internal evolution process governing the star formation in Carina, as opposed, for example, to a gas inflow scenario. There is much evidence

119 that Carina has had significant mass loss events throughout its history, implying that it was considerably more massive in the past. This possibly means that Carina had an intermediate mass compared to other local dwarf galaxies, lying in between the regime spanned by the most massive dSph and dIrr and the regime spanned by the ultra faint dwarf galaxies. Therefore, the scheme we propose is that Carina could have been massive enough to retain the gas after the first episode of star formation, but not massive enough to avoid significant expansion of the gas, which stopped the star formation, and given the relatively low gas densities, it could only cool down and contract to form stars again after a couple of gigayears.

Throughout this text I have shown the important pieces of information that have been derived from the diﬀerent works presented here. Nevertheless, the scope of these works go well beyond the one of this thesis, and their contribution will only increase in the upcoming years. In the near future the astronomical community will beneﬁt from the new information obtained through: (1) the interpretation of the new results obtained with the high quality catalogue presented, (2) the use of our new technique for discriminating between blue stragglers and young star counts, and (3) the implementation of state-of-the- art routines for deriving star formation histories, like Talos. In this context one of the most immediate and powerful results that will be obtained from this tools in the near future, will come when we analyze the star formation history of all the other galaxies and clusters presented in the catalogue applying the technique that we already used for the case of the Carina dSph galaxy.

120 Bibliography

Aparicio, A., & Hidalgo, S. L. 2009, AJ, 138, 558

Armandroﬀ, T. E., & Da Costa, G. S. 1991, AJ, 101, 1329

Baade, W. 1944a, ApJ, 100, 147

—. 1944b, ApJ, 100, 137

Bailyn, C. D. 1995, ARA&A, 33, 133

Baldry, I. K., et al. 2002, ApJ, 569, 582

Battaglia, G., Irwin, M., Tolstoy, E., de Boer, T., & Mateo, M. 2012, ApJ, 761, L31

Belokurov, V., et al. 2007, ApJ, 654, 897

—. 2009, MNRAS, 397, 1748

Bender, R., Paquet, A., & Nieto, J.-L. 1991, A&A, 246, 349

Benson, A. J., Lacey, C. G., Baugh, C. M., Cole, S., & Frenk, C. S. 2002, MNRAS, 333, 156

Besla, G. 2015, ArXiv e-prints

Bode, P., Ostriker, J. P., & Turok, N. 2001, ApJ, 556, 93

Bonanos, A. Z., Stanek, K. Z., Szentgyorgyi, A. H., Sasselov, D. D., & Bakos, G. A.´ 2004, AJ, 127, 861

Bono, G., et al. 2010, PASP, 122, 651

Bothun, G. D., Mould, J. R., Caldwell, N., & MacGillivray, H. T. 1986, AJ, 92, 1007

Bouchard, A., Da Costa, G. S., & Jerjen, H. 2009, AJ, 137, 3038

Bradford, J. D., et al. 2011, ApJ, 743, 167

Brasseur, C. M., Martin, N. F., Macci`o,A. V., Rix, H.-W., & Kang, X. 2011, ApJ, 743, 179

121 Bressan, A., Marigo, P., Girardi, L., Salasnich, B., Dal Cero, C., Rubele, S., & Nanni, A. 2012, MNRAS, 427, 127

Brown, T. M., et al. 2012, ApJ, 753, L21

—. 2014, ApJ, 796, 91

Bullock, J. S. 2010, ArXiv e-prints

Bullock, J. S., Kravtsov, A. V., & Weinberg, D. H. 2000, ApJ, 539, 517

—. 2001, ApJ, 548, 33

Calabretta, M. R., & Greisen, E. W. 2002, A&A, 395, 1077

Caldwell, N., Armandroﬀ, T. E., Da Costa, G. S., & Seitzer, P. 1998, AJ, 115, 535

Caldwell, N., Armandroﬀ, T. E., Seitzer, P., & Da Costa, G. S. 1992, AJ, 103, 840

Caldwell, R. R., Dave, R., & Steinhardt, P. J. 1998, Phys. Rev. Lett., 80, 1582

Cannon, R. D., Hawarden, T. G., & Tritton, S. B. 1977, MNRAS, 180, 81P

Carney, B. W., Latham, D. W., & Laird, J. B. 2005, AJ, 129, 466

Carney, B. W., Latham, D. W., Laird, J. B., Grant, C. E., & Morse, J. A. 2001, AJ, 122, 3419

Carollo, D., et al. 2010, ApJ, 712, 692

Carrera, R., Aparicio, A., Mart´ınez-Delgado,D., & Alonso-Garc´ıa,J. 2002, AJ, 123, 3199

Carretta, E., & Gratton, R. G. 1997, A&AS, 121, 95

Chabrier, G. 2003, PASP, 115, 763

Chatterjee, S., Rasio, F. A., Sills, A., & Glebbeek, E. 2013, ArXiv e-prints

Clark, L. L., Sandquist, E. L., & Bolte, M. 2004, AJ, 128, 3019

Clarkson, W. I., et al. 2011, ApJ, 735, 37

Conti, P. S., Hensberge, G., van den Heuvel, E. P. J., & Stickland, D. J. 1974, A&A, 34, 393

Coppola, G., et al. 2013, ApJ, 775, 6

Dalessandro, E., Lanzoni, B., Ferraro, F. R., Rood, R. T., Milone, A., Piotto, G., & Valenti, E. 2008, ApJ, 677, 1069

Dall’Ora, M., et al. 2006, ApJ, 653, L109

122 —. 2012, ApJ, 752, 42

Davies, M. B., Piotto, G., & de Angeli, F. 2004, MNRAS, 349, 129 de Boer, T. J. L., Tolstoy, E., Lemasle, B., Saha, A., Olszewski, E. W., Mateo, M., Irwin, M. J., & Battaglia, G. 2014, A&A, 572, A10 de Boer, T. J. L., et al. 2012, A&A, 539, A103 de Jong, J. T. A., Rix, H.-W., Martin, N. F., Zucker, D. B., Dolphin, A. E., Bell, E. F., Belokurov, V., & Evans, N. W. 2008a, AJ, 135, 1361 de Jong, J. T. A., et al. 2008b, ApJ, 680, 1112

De Young, D. S., & Heckman, T. M. 1994, ApJ, 431, 598

Deason, A. J., Wetzel, A. R., Garrison-Kimmel, S., & Belokurov, V. 2015, MNRAS, 453, 3568

Dohm-Palmer, R. C., Skillman, E. D., Mateo, M., Saha, A., Dolphin, A., Tolstoy, E., Gallagher, J. S., & Cole, A. A. 2002, AJ, 123, 813

Dohm-Palmer, R. C., et al. 1998, AJ, 116, 1227

Dolphin, A. 1997, New Astronomy, 2, 397

Dolphin, A. E. 2002, MNRAS, 332, 91

Dolphin, A. E., Weisz, D. R., Skillman, E. D., & Holtzman, J. A. 2005, astro- ph/0506430

Dotter, A., Chaboyer, B., Jevremovi´c,D., Kostov, V., Baron, E., & Ferguson, J. W. 2008, ApJS, 178, 89

Dressler, A., Oemler, Jr., A., Poggianti, B. M., Smail, I., Trager, S., Shectman, S. A., Couch, W. J., & Ellis, R. S. 2004, ApJ, 617, 867

Drlica-Wagner, A., et al. 2015, ApJ, 813, 109

Eggen, O. J., & Lynden-Bell, D. and Sandage, A. R. 1962, ApJ, 136, 748

Einasto, J., Saar, E., Kaasik, A., & Chernin, A. D. 1974, Nature, 252, 111

Ellis, R. S. 1997, ARA&A, 35, 389

Ferraro, F. R., et al. 2009, Nature, 462, 1028

Freeman, K., & Bland-Hawthorn, J. 2002, ARA&A, 40, 487

Fregeau, J. M., Ivanova, N., & Rasio, F. A. 2009, ApJ, 707, 1533

Gallagher, III, J. S., Hunter, D. A., & Tutukov, A. V. 1984, ApJ, 284, 544

123 Gallart, C., Aparicio, A., Bertelli, G., & Chiosi, C. 1996, AJ, 112, 1950

Gallart, C., & Lcid Team. 2007, in IAU Symposium, Vol. 241, IAU Symposium, ed. A. Vazdekis & R. Peletier, 290–294

Gallart, C., et al. 1999, ApJ, 514, 665

Garofalo, A., et al. 2013, ApJ, 767, 62

Geha, M., Willman, B., Simon, J. D., Strigari, L. E., Kirby, E. N., Law, D. R., & Strader, J. 2009, ApJ, 692, 1464

Geha, M., et al. 2013, ApJ, 771, 29

Geller, A. M., Hurley, J. R., & Mathieu, R. D. 2013, AJ, 145, 8

Geller, A. M., & Mathieu, R. D. 2011, Nature, 478, 356

Gilmore, G., Wilkinson, M. I., Wyse, R. F. G., Kleyna, J. T., Koch, A., Evans, N. W., & Grebel, E. K. 2007, ApJ, 663, 948

Glaspey, J. W., Pritchet, C. J., & Stetson, P. B. 1994, AJ, 108, 271

Grebel, E. K. 1997, in Reviews in Modern Astronomy, Vol. 10, Reviews in Modern Astronomy, ed. R. E. Schielicke, 29–60

Grebel, E. K. 1999, in IAU Symposium, Vol. 192, The Stellar Content of Local Group Galaxies, ed. P. Whitelock & R. Cannon, 17–+

Grebel, E. K. 2001, Astrophysics and Space Science Supplement, 277, 231

Gullieuszik, M., Held, E. V., Rizzi, L., Girardi, L., Marigo, P., & Momany, Y. 2008, MNRAS, 388, 1185

Harbeck, D., et al. 2001, AJ, 122, 3092

Harris, J., & Zaritsky, D. 2001, ApJS, 136, 25

Harris, W. E. 2010, ArXiv e-prints

Helmi, A., et al. 2006, ApJ, 651, L121

Hills, J. G., & Day, C. A. 1976, Astrophys. Lett., 17, 87

Hodge, P. 1989, ARA&A, 27, 139

Hooper, D., & Linden, T. 2015, ArXiv e-prints

Hunter, D. A., & Gallagher, III, J. S. 1985, ApJS, 58, 533

—. 1986, PASP, 98, 5

124 Hurley, J. R., Tout, C. A., Aarseth, S. J., & Pols, O. R. 2001, MNRAS, 323, 630

Hurley-Keller, D., Mateo, M., & Grebel, E. K. 1999, ApJ, 523, L25

Hurley-Keller, D., Mateo, M., & Nemec, J. 1998, AJ, 115, 1840

Ibata, R. A., Wyse, R. F. G., Gilmore, G., Irwin, M. J., & Suntzeﬀ, N. B. 1997, AJ, 113, 634

Ibukiyama, A., & Arimoto, N. 2002, A&A, 394, 927

Irwin, M., & Hatzidimitriou, D. 1995, MNRAS, 277, 1354

James, P. 1991, MNRAS, 250, 544

James, P. A., Salaris, M., Davies, J. I., Phillipps, S., & Cassisi, S. 2006, MNRAS, 367, 339

Johnston, K. V., Spergel, D. N., & Hernquist, L. 1995, ApJ, 451, 598

Jorgensen, H. E., & Hansen, L. 1984, A&A, 133, 165

Kamionkowski, M., & Liddle, A. R. 2000, Physical Review Letters, 84, 4525

Kauﬀmann, G., White, S. D. M., & Guiderdoni, B. 1993, MNRAS, 264, 201

Kaviraj, S., Kirkby, L. A., Silk, J., & Sarzi, M. 2007, MNRAS, 382, 960

Kirby, E. N., Cohen, J. G., Guhathakurta, P., Cheng, L., Bullock, J. S., & Gallazzi, A. 2013, ApJ, 779, 102

Klypin, A., Kravtsov, A. V., Valenzuela, O., & Prada, F. 1999, ApJ, 522, 82

Klypin, A., Zhao, H., & Somerville, R. S. 2002, ApJ, 573, 597

Knigge, C., Leigh, N., & Sills, A. 2009, Nature, 457, 288

Koch, A., Grebel, E. K., Wyse, R. F. G., Kleyna, J. T., Wilkinson, M. I., Harbeck, D. R., Gilmore, G. F., & Evans, N. W. 2006, AJ, 131, 895

Komatsu, E., et al. 2011, ApJS, 192, 18

Koposov, S., et al. 2008, ApJ, 686, 279

Koposov, S. E., Belokurov, V., Torrealba, G., & Evans, N. W. 2015, ApJ, 805, 130

Kormendy, J., & Bender, R. 2012, ApJS, 198, 2

Kozai, Y. 1962, AJ, 67, 591

Kravtsov, A. V., Gnedin, O. Y., & Klypin, A. A. 2004, ApJ, 609, 482

125 Kroupa, P. 1997, New Astronomy, 2, 139

—. 2001, MNRAS, 322, 231

Kuehn, C., et al. 2008, ApJ, 674, L81

Latyshev, I. N. 1978, Soviet Ast., 22, 186

Law, D. R., & Majewski, S. R. 2010, ApJ, 714, 229

Lee, M. G., et al. 2003, AJ, 126, 2840

Leigh, N., & Sills, A. 2011, MNRAS, 410, 2370

Leigh, N., Sills, A., & Knigge, C. 2007, ApJ, 661, 210

—. 2011a, MNRAS, 416, 1410

—. 2011b, MNRAS, 415, 3771

Leonard, P. J. T. 1989, AJ, 98, 217

Lynden-Bell, D. 1976, MNRAS, 174, 695

Majewski, S. R., Skrutskie, M. F., Weinberg, M. D., & Ostheimer, J. C. 2003, ApJ, 599, 1082

Mapelli, M., Ripamonti, E., Tolstoy, E., Sigurdsson, S., Irwin, M. J., & Battaglia, G. 2007, MNRAS, 380, 1127

Mapelli, M., Sigurdsson, S., Ferraro, F. R., Colpi, M., Possenti, A., & Lanzoni, B. 2006, MNRAS, 373, 361

Marconi, G., Tosi, M., Greggio, L., & Focardi, P. 1995, AJ, 109, 173

Martin, N. F., de Jong, J. T. A., & Rix, H.-W. 2008a, ApJ, 684, 1075

Martin, N. F., et al. 2008b, ApJ, 672, L13

Mart´ınez-Delgado,D., Aparicio, A., & Gallart, C. 1999, AJ, 118, 2229

Mateo, M. 1996, in Astronomical Society of the Paciﬁc Conference Series, Vol. 92, Formation of the Galactic Halo...Inside and Out, ed. H. L. Morrison & A. Sarajedini, 434

Mateo, M., Fischer, P., & Krzeminski, W. 1995, AJ, 110, 2166

Mateo, M., Harris, H. C., Nemec, J., & Olszewski, E. W. 1990, AJ, 100, 469

Mateo, M., Hurley-Keller, D., & Nemec, J. 1998, AJ, 115, 1856

126 Mateo, M., Olszewski, E. W., Pryor, C., Welch, D. L., & Fischer, P. 1993, AJ, 105, 510

Mateo, M. L. 1998, ARA&A, 36, 435

Mathieu, R. D., & Geller, A. M. 2009, Nature, 462, 1032

Mayer, L., Governato, F., Colpi, M., Moore, B., Quinn, T., Wadsley, J., Stadel, J., & Lake, G. 2001, ApJ, 559, 754

Mayer, L., Mastropietro, C., Wadsley, J., Stadel, J., & Moore, B. 2006, MNRAS, 369, 1021

McConnachie, A. W. 2012, AJ, 144, 4

McConnachie, A. W., Arimoto, N., & Irwin, M. 2007, MNRAS, 379, 379

McConnachie, A. W., Arimoto, N., Irwin, M., & Tolstoy, E. 2006, MNRAS, 373, 715

McConnachie, A. W., & Irwin, M. J. 2006, MNRAS, 365, 1263

McConnachie, A. W., et al. 2009, Nature, 461, 66

McCrea, W. H. 1964, MNRAS, 128, 147

McMonigal, B., et al. 2014, MNRAS, 444, 3139

Miller, G. E., & Scalo, J. M. 1979, ApJS, 41, 513

Momany, Y., Held, E. V., Saviane, I., Zaggia, S., Rizzi, L., & Gullieuszik, M. 2007, A&A, 468, 973

Monelli, M., et al. 2003, AJ, 126, 218

Monkiewicz, J., et al. 1999, PASP, 111, 1392

Moore, B., Ghigna, S., Governato, F., Lake, G., Quinn, T., Stadel, J., & Tozzi, P. 1999, ApJ, 524, L19

Muñoz,R. R., Geha, M., Côté,P., Vargas, L. C., Santana, F. A., Stetson, P., Simon, J. D., & Djorgovski, S. G. 2012a, ApJ, 753, L15

Mu˜noz,R. R., Majewski, S. R., & Johnston, K. V. 2008, ApJ, 679, 346

Mu˜noz,R. R., Padmanabhan, N., & Geha, M. 2012b, ApJ, 745, 127

Mu˜noz,R. R., et al. 2006, ApJ, 649, 201

Musella, I., et al. 2009, ApJ, 695, L83

—. 2012, ApJ, 756, 121

127 Nemec, J. M., & Cohen, J. G. 1989, ApJ, 336, 780

Nemec, J. M., & Harris, H. C. 1987, ApJ, 316, 172

Nemec, J. M., Mateo, M., Burke, M., & Olszewski, E. W. 1995, AJ, 110, 1186

Okamoto, S., Arimoto, N., Yamada, Y., & Onodera, M. 2012, ApJ, 744, 96

Pasetto, S., Grebel, E. K., Berczik, P., Chiosi, C., & Spurzem, R. 2011, A&A, 525, A99

Perets, H. B., & Fabrycky, D. C. 2009, ApJ, 697, 1048

Perets, H. B., & Kratter, K. M. 2012, ApJ, 760, 99

Piatek, S., Pryor, C., Olszewski, E. W., Harris, H. C., Mateo, M., Minniti, D., & Tinney, C. G. 2003, AJ, 126, 2346

Pilkington, K., & Gibson, B. K. 2012, in Nuclei in the Cosmos (NIC XII), 227

Piotto, G., et al. 2004, ApJ, 604, L109

Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. 1992, Numerical recipes in C. The art of scientiﬁc computing

Preston, G. W., & Sneden, C. 2000, AJ, 120, 1014

Reid, B. A., et al. 2010, MNRAS, 404, 60

Reid, I. N., Turner, E. L., Turnbull, M. C., Mountain, M., & Valenti, J. A. 2007, ApJ, 665, 767

Revaz, Y., et al. 2009, A&A, 501, 189

Rizzi, L., Held, E. V., Bertelli, G., & Saviane, I. 2003, ApJ, 589, L85

—. 2004, Mem. Soc. Astron. Italiana, 75, 110

Rocha-Pinto, H. J., Scalo, J., Maciel, W. J., & Flynn, C. 2000, A&A, 358, 869

Rood, R. T. 1970, ApJ, 162, 939

Saha, A., Monet, D. G., & Seitzer, P. 1986, AJ, 92, 302

Salpeter, E. E. 1955, ApJ, 121, 161

Sandage, A. 1958, Ricerche Astronomiche, 5, 41

Sandage, A. R. 1953, AJ, 58, 61

Sandquist, E. L. 2005, ApJ, 635, L73

128 Santana, F. A., Muñoz,R. R., Geha, M., Côté,P., Stetson, P., Simon, J. D., & Djorgovski, S. G. 2013, ApJ, 774, 106

Schlaﬂy, E. F., & Finkbeiner, D. P. 2011, ApJ, 737, 103

Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525

Searle, L., Sargent, W. L. W., & Bagnuolo, W. G. 1973, ApJ, 179, 427

Searle, L., & Zinn, R. 1978, ApJ, 225, 357

Seiden, P. E., Schulman, L. S., & Gerola, H. 1979, ApJ, 232, 702

Simon, J. D., & Geha, M. 2007, ApJ, 670, 313

Skillman, E. D., & Gallart, C. 2002, in Astronomical Society of the Paciﬁc Conference Series, Vol. 274, Observed HR Diagrams and Stellar Evolution, ed. T. Lejeune & J. Fernandes, 535

Skillman, E. D., Kennicutt, R. C., & Hodge, P. W. 1989, ApJ, 347, 875

Skillman, E. D., Tolstoy, E., Cole, A. A., Dolphin, A. E., Saha, A., Gallagher, J. S., Dohm-Palmer, R. C., & Mateo, M. 2003, ApJ, 596, 253

Smecker-Hane, T. A., Stetson, P. B., Hesser, J. E., & Lehnert, M. D. 1994, AJ, 108, 507

Sollima, A., Lanzoni, B., Beccari, G., Ferraro, F. R., & Fusi Pecci, F. 2008, A&A, 481, 701

Somerville, R. S. 2002, ApJ, 572, L23

Starkenburg, E., et al. 2010, A&A, 513, A34

Stetson, P. B. 1991, AJ, 102, 589

Stetson, P. B., Hesser, J. E., & Smecker-Hane, T. A. 1998, PASP, 110, 533

Strigari, L. E., Bullock, J. S., Kaplinghat, M., Simon, J. D., Geha, M., Willman, B., & Walker, M. 2008, Nature, 454, 1096 (astro-ph/0808.3772)

Tojeiro, R., Heavens, A. F., Jimenez, R., & Panter, B. 2007, MNRAS, 381, 1252

Tolstoy, E. 1996, ApJ, 462, 684

Tolstoy, E., Hill, V., & Tosi, M. 2009, ARA&A, 47, 371

Tosi, M., Greggio, L., Marconi, G., & Focardi, P. 1991, AJ, 102, 951

Tremonti, C. A., et al. 2004, ApJ, 613, 898

Unavane, M., Wyse, R. F. G., & Gilmore, G. 1996, MNRAS, 278, 727

129 van den Bergh, S. 1994, ApJ, 428, 617

VandenBerg, D. A., Stetson, P. B., & Brown, T. M. 2015, ApJ, 805, 103

Vanseviˇcius,V., et al. 2004, ApJ, 611, L93

Walker, M. G., Mateo, M., Olszewski, E. W., Pe˜narrubia,J., Wyn Evans, N., & Gilmore, G. 2009, ApJ, 704, 1274

Walsh, S. M., Willman, B., Sand, D., Harris, J., Seth, A., Zaritsky, D., & Jerjen, H. 2008, ApJ, 688, 245

Weisz, D. R., Dolphin, A. E., Skillman, E. D., Holtzman, J., Gilbert, K. M., Dalcanton, J. J., & Williams, B. F. 2014, ApJ, 789, 147

Wetzel, A. R., Deason, A. J., & Garrison-Kimmel, S. 2015, ApJ, 807, 49

Willman, B., & Strader, J. 2012, AJ, 144, 76

Willman, B., et al. 2005, AJ, 129, 2692

Wolf, J., Martinez, G. D., Bullock, J. S., Kaplinghat, M., Geha, M., Mu˜noz,R. R., Simon, J. D., & Avedo, F. F. 2010, MNRAS, 406, 1220

York, D., et al. 2000, AJ, 120, 1579

Zinn, R., & West, M. J. 1984, ApJS, 55, 45

130