View of the Interior Structure

UNIVERSITY OF CINCINNATI

Date: 17-Sep-2010

I, Benjamin Frank Bubnick , hereby submit this original work as part of the requirements for the degree of: Master of Science in Physics It is entitled: Massive Stellar Clusters in the Disk of the Milky Way Galaxy

Student Signature: Benjamin Frank Bubnick

This work and its defense approved by: Committee Chair: Margaret Hanson, PhD Margaret Hanson, PhD

10/4/2010 1,102 Massive Stellar Clusters in the Disk of the Milky Way Galaxy

A Thesis submitted to the Graduate School of the University of Cincinnati

in partial fulﬁllment of the requirements for the degree of

Master of Science

in the Department of Physics of the College of Arts and Sciences 2010

Benjamin Bubnick

Committee Chair: Margaret Hanson ABSTRACT

Title of thesis: Massive Stellar Clusters in the Disk Of the Milky Way Galaxy

Benjamin Bubnick, Master of Science, 2010

Thesis directed by: Professor Margaret Hanson Department of Physics

This thesis outlines successful eﬀorts for identifying and characterizing the stellar content of two Galactic disk star clusters using near-infrared observations.

Astronomers have a great wealth of knowledge about globular clusters. They are easy to see as most lie outside the plane of the galaxy in the halo. Extinction is low, the stellar population is dense in the cluster, and they are fairly common.

However, in the plane of the galaxy, relatively little is known of the open cluster population. Galactic disk open clusters, such as the two discussed in this thesis, are hidden behind gas, dust, and projected against a multitude of ﬁeld stars. Through the use of near-infrared broad-band photometry and spectroscopy, the distance, age and approximate mass of two disk clusters has been determined. c Copyright by Benjamin Bubnick 2010 Acknowledgments

I want to thank everyone who provided me with scientiﬁc and moral support through the writing of this thesis. Foremost, my sincere gratitude goes to my adviser,

Dr. Margaret Hanson, who assisted me invaluably in the data analysis and who provided me with the tools necessary to write this work. I owe much to the University of Cincinnati, and in particular the Physics Department, for for the support and education that has brought me to this point. I owe gratitude also to Dr. Richard

Gass and Aaron Eiben for the discussions that helped me organize my ideas for this work. I appreciate the contributions from Lori Beerman and Yara Beshara to the astrometry analysis for the two clusters.

And ﬁnally my thanks goes out to my family for the moral support they have given me. My parents’ dedication to my education gave me the drive to pursue study in physics, and my wife’s love and support gave me the drive to ﬁnish. Thank you all.

ii Table of Contents

List of Tables v

List of Figures vi

List of Abbreviations vii

1 Introduction 1 1.1WhyNIRAstronomyisuseful...... 1 1.1.1 Stars,Telescopes,andtheInterstellarMedium...... 2 1.1.2 DimmingandReddeningbytheISM...... 4 1.2StellarEvolution...... 8 1.2.1 HRDiagram...... 9 1.2.2 MSLifetime...... 12 1.2.3 Metallicity ...... 12 1.3TheInfraredTelescopes...... 13 1.3.1 NIRTechnology...... 13 1.3.2 IRTF...... 14 1.3.3 VLT...... 15 1.3.4 NTT...... 15 1.4StellarClusters...... 16 1.4.1 GlobularClusters...... 17 1.4.2 OpenClusters...... 19 1.4.3 SuperStarClusters...... 20 1.4.4 TheNearInfraredSkySurveys...... 21 1.5ToolsoftheObservationalAstronomer...... 22 1.5.1 Photometry...... 22 1.5.2 Spectroscopy...... 23 1.6DeﬁnitionofBasicTerms...... 25 1.6.1 CelestialCoordinates...... 25 1.6.2 EquatorialCoordinates...... 25 1.6.3 GalacticCoordinates...... 26 1.6.4 Resolution...... 26 1.6.5 Seeing...... 27 1.6.6 TelluricContamination...... 27

2 The Open Cluster [BDS2003]107 28 2.1Astrometry...... 29 2.1.1 StarLocations...... 30 2.1.2 RadialVelocity...... 32 2.2Photometry...... 44 2.2.1 STARFINDPhotometry...... 44 2.2.2 MagnitudeSplitting...... 45 2.2.3 LimitingMagnitudes...... 47

iii 2.2.4 Color-MagnitudeDiagram...... 48 2.2.5 Color-ColorDiagram...... 48 2.2.6 TheFieldStar#23...... 50 2.2.7 FalseColorImage...... 52 2.3ClusterCharacteristics...... 52

3 The Super Star Cluster Westerlund1 55 3.1Astrometry...... 56 3.2Westerlund1Cross-ReferenceChart...... 60 3.3Photometry...... 65 3.3.1 DAOPHOTPhotometry...... 65 3.3.2 LimitingMagnitudes...... 66 3.3.3 Color-MagnitudeDiagram...... 66 3.3.4 Color-ColorDiagram...... 69 3.3.5 FalseColorImage...... 69 3.4ClusterCharacteristics...... 69

4 Massive Cluster Research 72 4.1ImportanceofMassiveClusterResearch...... 72 4.2FutureofMassiveClusterResearch...... 74 4.3ImprovementsonMassiveClusterResearch...... 76 4.3.1 TheRadialVelocityofCluster107...... 76 4.3.2 BetterCalibratedPhotometry...... 77

Bibliography 80

iv List of Tables

1.1Zero-MagnitudeFlux...... 4

1.2TheProton-ProtonChain...... 9

1.3AbsoluteSolarMagnitudes...... 10

1.4PhotometricBands...... 23

2.1Cluster107StarsObserved...... 33

2.2Cluster107StarsObserved...... 34

2.3Cluster107StarsObserved...... 35

2.4Cluster107StarsObserved...... 36

2.5FalseColorImageColorRanges...... 54

3.1Wd1StarsObserved...... 57

3.2Wd1StarsObserved...... 58

3.3Wd1StarsObserved...... 59

3.4Wd1CrossReferences...... 62

3.5Wd1CrossReferences...... 63

3.6Wd1CrossReferences...... 64

v List of Figures

1.1EMWaveScattering...... 6

1.2ExtinctionCurve...... 7

1.3HRDiagram...... 11

1.4Color-MagnitudeDiagram...... 18

1.5JHKTransmissionCurve...... 24

2.1FindingChart...... 37

2.2Star#23Spectra...... 38

2.3FluxDiﬀerence...... 40

2.4RadialVelocity...... 41

2.5MilkyWayRotationCurve...... 42

2.6AverageFluxDiﬀerence...... 43

2.7Color-MagnitudeDiagram...... 49

2.8Color-ColorDiagram...... 51

2.9ColorCompositeImage...... 53

3.1FindingChart...... 61

3.2Color-MagnitudeDiagram...... 67

3.3Color-MagnitudeDiagram...... 68

3.4Color-ColorDiagram...... 70

3.5ColorCompositeImage...... 71

4.1SpiralGalaxy...... 73

4.2MilkyWayGalaxy...... 75

4.3ExtremelyNoisySpectra...... 78

vi List of Abbreviations

2MASS Two Micron All Sky Survey Aλ Monochromatic Extinction α Right Ascension BII Galactic Latitude CCD Color-Color Diagram CMD Color-Magnitude Diagram Dec. Declination δ Declination EM Electromagnetic ESO European Southern Observatory Fλ Monochromatic Flux Density FWHM Full Width at Half Maximum GLIMPSE Galactic Legacy Infrared Mid-Plane Survey Extraordinaire Gyr Giga-year (109 years) HgCdTe Mercury Cadmium Telluride HR Hertzsprung-Russell HWHM Half Width at Half Maximum InSb Indium Antimode IPAC Infrared Processing and Analysis Center IRAF Image Reduction and Analysis Facility IRTF Infrared Telescope Facility ISAAC Infrared Spectrometer and Array Camera ISM Interstellar Medium kWaveNumber kpc Kiloparsec LII Galactic Longitude Lλ Monochromatic Luminosity λ Wavelength M Mass of the Sun Mλ Monochromatic Absolute Magnitude mλ Monochromatic Apparent Magnitude MS Main Sequence Myr Mega-year (106 years) μm Micron or Micrometer

vii NASA National Aeronautics and Space Administration NIR Near Infrared NSF National Science Foundation NTT New Technology Telescope PbS Lead Sulﬁde pc Parsec PSF Point Spread Function R.A. Right Ascension SOFI Son of ISAAC (Infrared Spectrometer and Array Camera) Teff Eﬀective Temperature TT Terrestrial Time τMS Main Sequence Timescale VISTA Visible and Infrared Survey Telescope for Astronomy VLT Very Large Telescope VVV VISTA Variables in The Via Lactea W Watts Wd1 Westerlund 1

viii Chapter 1

Introduction

1.1 Why NIR Astronomy is useful

In order to understand the structure of the Milky Way, we need to develop an accurate, unbiased census of star formation in the inner Galaxy[63]. This would be very easy if not for a phenomenon called extinction, which is a result of the interstellar medium (ISM) that lies in the galactic plane. The ISM is the gas and dust that permeates the galaxy between the stars and is responsible for extinction, the dimming of starlight making it more difficult for observation. At visible wavelengths, we can only see about 5% of the way through the Galaxy on average [61]. Imagine you are trying to map out the floorplans of the physics building, but you are confined to a single room. This is very much like the position of the Earth in the plane of the Milky Way. Like a classroom the Earth has windows to see into the halo of the

Milky Way and outside of the galaxy, but the Earth is ”behind a wall” blocking the view of the interior structure. How do we see beyond this wall of dust? We move our observations to the infrared spectrum.

Near infrared (NIR) astronomy deals with the study of electromagnetic (EM) radiation in the 1-5 μm range, just beyond the visible spectrum. It was ﬁrst detected over 200 years ago by William Herschel [45], but only relatively recently have we been seriously studying astronomy in this region of light. Technologically speaking,

1 NIR begins beyond the limit of detection of visible light collecting devices, such as Charge-Coupled Devices [40], [60]. The infrared spectrum is naturally divided by absorption due to water vapor in the Earth’s atmosphere (see section 1.6.6) at about 6 μm [40]. There are smaller absorption bands at 1.4, 1.9, and 2.8 /mum.

Though the NIR is divided into ﬁve photometric bands, this thesis will only discuss the bluest three: J, H, K (see section 1.5.1). For the purpose of this thesis, this puts NIR in the range ∼ 1.2 − 2.2μm (see table 1.4 for more details concerning each instrument involved in this study).

1.1.1 Stars, Telescopes, and the Interstellar Medium

There are three basic physical elements along the light path with any astronomical observation: the source (e.g. star), the medium (e.g. ISM), and the detector

(e.g. telescope). The total radiated ﬂow of energy of the star is the total luminosity.

Because detectors are limited to certain bandwidths we usually deal with the total luminosity divided into wavelength intervals. The monochromatic luminosity (Lλ often just called the luminosity) is the power over that interval and is measured in units of Watts per unit wavelength (W μm−1). The radiation that is received by the detector is the flux density. The flux density (Fλ often just called the flux) is the flux of radiation received per unit wavelength and is measured in units of Watts per area per wavelength (W m−2μm−1). The loss of power from the source to the detector is the extinction, but first the luminosity and flux need to be in proper units for comparison. From this arises the magnitude system in astronomy.

2 The magnitude system has its roots in classical astronomy. The Greek astronomer Hipparchus (c.190-120 B.C.E.) originally classified stars according to how bright they look to our eyes [6]. This apparent magnitude (mλ)systemisinverse logarithmic in nature, that is a lower value denotes a brighter object (first magnitude stars were the first stars to be seen after sunset and second magnitude were the second set of stars to come out and so on). Distance and extinction were not taken into account since the stars were assumed to lie on a celestial sphere with no

ISM to cause the dimming of starlight [27]. In order to calibrate the flux measurement to the apparent magnitude a standard star, astronomers chose Vega to be the zero-magnitude flux standard for each filter (see table 1.1) [30]. Then, the apparent magnitude follows from the following relation:

Fλ mλ = −2.5Log (1.1) F0 where F0 is zero-magnitude flux standard of Vega for each filter J, H, K.Themea- sured flux of Vega at each band is divided out to make the magnitude (mλ) zero.

Without extinction the luminosity to ﬂux relationship is:

Lλ Fλ = (1.2) 4πd2 where d is the distance of the detector from the source. If we take d to be 10 parsecs, then apparent luminosity is called the absolute magnitude (Mλ), and it is the standard by which all apparent magnitudes are compared. Finally, we can show the relation between the light count between the source and detector, taking the

3 distance and extinction into account:

Mλ = mλ − 5(Logd − 1) − Aλ (1.3) where Aλ is the extinction for a certain ﬁlter, λ.

Table 1.1: Zero-Magnitude Flux - Vega.

W W W FJ ( m2μm ) FH ( m2μm ) FK ( m2μm )

3.8 × 10−9 2.0 × 10−9 9.0 × 10−10 These numbers correspond to magnitude data obtained from the Two Micron All Sky Survey (2MASS) project [30]. (see section 1.4.4)

1.1.2 Dimming and Reddening by the ISM

The ISM is very sparse, however on the length scales of d (equation 1.3) the integrated eﬀect of the ISM becomes very apparent. The ISM aﬀects the light in two ways: dimming and reddening. By mass the ISM consists of about 99% gas

(hydrogen) and 1% dust [85]. In this contex dust refers to particles ranging in size from a few molecules to ∼100 μm (the upper size limit of common cement dust is about 100 microns [34]). The dimming of the starlight is the result of the dust absorbing or scattering some photons away from the light path from the star.

Since fewer photons reach the detector, the decrease in ﬂux suggests the star is dimmer than it really is. The reddening of the starlight is a result of the size of the dust grains. These grains will easily absorb or scatter short wavelength light while long wavelength light passes through nearly unaﬀected. The incident EM wave will

4 induce a dipole moment on the dust grain. The dipole produces a radiation field, and the differential scattering cross section goes as the wave number to the fourth power (k4), see figure 1.1.

4 2 4 8π α 2 I = I0 k (1 + cos θ) (1.4) d2 where α here is the polarizability (the ratio of the dipole moment to the EM ﬁeld produced by the dipole) of the dust grain, and d is the distance to the grain. The physics of scattering by a single particle is a relatively simple exercise found in Jack- son’s Classical Electrodynamics [50]. And while a rigorous analysis of the absorption of light has been done, the qualitative understanding of this eﬀect is a simple matter.

The spectrum of energy absorbed by the dust is simply just re-emitted at a longer wavelength, adding to the infrared excess.

ISM extinction plays a big role in how we can observe the disk of the galaxy.

Figure 1.2 shows the average extinction curve one observes by looking at stars within one kiloparsec (kpc). Distances of the clusters analyzed in this thesis are around 2+ kpc. The extinction in and around the visible wavelengths (∼ 0.25−1μm)increases nearly linearly with k. This suggests that the major contribution to interstellar extinction is due to small grains [10]. When we look at the visible spectrum of a star,

∼ 1 we see as much as 4.5 magnitudes of extinction. Compared with the 2 magnitude extinction in the K-band, there is a wall in which visible light observations fail us.

Observations in the very near infrared (the I−band and even the J−band to a very little degree) shows relatively little diﬀerence from visible other than greater

5 z n E

a Ε

Figure 1.1: A good approximation of the starlight reddening is an unpolarized EM ikz−iωt plane wave scattering off a dielectric sphere. For an incident EM wave E0 = e 4 6 2 dσ k a r−1 2 the differential cross scattering is dΩ = 2 r+2 (1 + cos θ). The quantitative understanding of the physics of scattering by a single particle is well known, and even a qualitative comprehension of the physics can be seen without constraining to a specific particle shape, such as this sphere. For an arbitrary particle any finite element will have an induced dipole moment which will oscillate at the frequency of incident radiation, and scatter secondary radiation in all directions [10]. The radiation received at the detector then is simply a superposition of of the scattered wavelets.

V B E V E Λ 3. 1. 11. 9. 7. 5. 3. 1. 10 0.1 8 6 e 1 m m Μ Μ 7 0.2 1 Λ Λ xtinction Curv E 5 4 .2 0 2 0.5 1. NIR 0

0 4 2 6 8

10 12 14

V B E A Λ Figure 1.2: This is anData average Points interstellar are extinction from spectrum Savage and (Bohrenonly ﬁg Mathis an 14.4 [78] average, [10]). It since islies worth the mainly noting distribution in that the the of curve spiral gaswindows shows arms (gaps and of in dust the the Milky is ISM)extinction. Way. show certainly There groups not are of areas uniform. stars of non-local It the with galaxy each other where with little penetrating power through the ISM. In fact, the I−band is more like an extension of the visible bands because CCD technology is still sensitive in these longer wavelength

(see section 1.3.1). There is a significant jump down in extinction from the J−band to the K−band that you do not see in the rest of the infrared. By the time you get to the K−band, the sky looks entirely different. Much of the same analysis tools are used in the NIR as the visible, but the excess in infrared contamination generates subtle differences in data reduction.

1.2 Stellar Evolution

Stellar evolution is divided into three basic parts of the star’s lifetime: pre- main sequence, main sequence (MS), and post-main sequence. The protostar is formed from the collapse of a giant molecular cloud in the ISM. Initial energy is generated from the gravitational collapse, which in turn, ignites the nuclear fusion of hydrogen once the required temperature and density have been reached. The phase where hydrogen is fused into helium is MS.

After the star has exhausted its hydrogen resources and begins fusing helium into heavier elements (all elements heavier than helium are called metals), the star begins to cool and expand because the eﬃciency of power generation is less for these fusion reactions. When the star can no longer support itself, it begins to collapse eventually releasing much of its material into the ISM to seed future generations of stars. The expanding and cooling can be tracked by plotting the luminosity- temperature relation, called the Hertzsprung−Russell diagram.

8 Table 1.2: The Proton-Proton Chain of stellar fusion.

1 1 2 + H + H → H + e + νe

2H + 1H → 3He + γ

3He + 3He → 4He +21H It all begins here. This is what generates the energy in the main sequence. The ﬁrst two reactions are required for the third reaction to take place. The post-MS will see similar cycles in Helium, Carbon, Oxygen, and Silicon burning which will produce elements up to Iron. Stars with masses higher than ∼ 1.5M will undergo a carbon cycle (CNO) that is responsible for the elements you encounter when you look in the mirror every morning.

1.2.1 HR Diagram

The Hertzsprung−Russell (HR) diagram (ﬁgure 1.3) is a plot of the luminosity of a group of stars versus their eﬀective temperatures (Teff ), which is related to the spectral type. It is the theoretical version of the color-magnitude diagram (CMD).

Teff is not a directly observable quantity; the spectral type is used to determine it.

Often the precise spectral classification is not easily identified, especially without high resolution spectra analysis. In this case, the observed quantity is the B-V color index. It is calculated to be the difference between the magnitudes in the B− and

V −bands. The temperature is then determined by by solving the Planck Equation:

2hc2 1 Bλ = (1.5) 5 hc − λ exp[ λKT ] 1 which is usually approximated by the numerical relation:

9 ⎧ ⎪ ⎨⎪ 100.27[14.55−(B−V )] :(B − V ) > −0.0413 ≈ T ⎪ √ (1.6) ⎪ ⎩ 104.95− 0.09+2.91(B−V ) :(B − V ) < −0.0413

Once Teff is known, or the spectral type is already determined, the luminosity can be calculated if the radius of the star is known. Using equation 1.3, Mv can be determined. Then the monochromatic luminosity in solar units is given by the relation:

0.4(Mλ−Mλ) Lλ =10 (1.7)

Table 1.3: Absolute Solar Magnitudes.

UBVR I JHK

5.61 5.48 4.83 4.42 4.08 3.64 3.32 3.28 Data were obtained from Binney and Merriﬁeld table 2.1 [9].

where Table 1.3 shows the absolute magnitudes of the Sun. In order to determine the total luminosity a bolometric correction must be applied to each star depending on its spectral type [3]. Figure 1.3 shows an HR diagram made from 10,000 stars from

Hipparcos catalog [73]. The Luminosity and temperature are calculated using the above method, but the absolute magnitudes are already determined in the catalog.

To put the HR diagram into context, the theoretical curves are put in to show MS,

Giant, and Supergiant stars ([3], Table 15.7).

10 HertzsprungRussell Diagram BV 0. 0.2 0.4 0.6 0.8 1. 1.5 2. 2.5 3.

8 Supergiants

104

2 e L

100 0 Luminosity bsolute Magnitud A

2 Main Sequence Giants

10000 8000 6000 4000 2000 Temperature K

Figure 1.3: Hertzsprung-Russell Diagram made with 10,000 stars from the Hipparcos Catalog [73]. The theoretical curves are from Allen [3].

11 1.2.2 MS Lifetime

The MS lifetime is determined by the amount of hydrogen available for nuclear fusion, which is dependent on the mass of the star. The time a star spends on the

MS (τMS) depends on the mass of the star (M) and the amount of power the star is generating (L). The mass-luminosity relationship for MS stars in solar units is:

⎧ ⎪ ⎪ 3.5 ⎨ M :2M

M 1−a τMS = = M (1.9) L

The arrangement of stars by mass on the MS curve shows the importance of stellar mass to its lifetime. The mass-luminosity relationship arises from the weight of the stars outer layers requiring a higher nuclear fusion rate to support the hydrostatic equilibrium.

1.2.3 Metallicity

An important thing happens here. The next generation of stars will have more metals than the parent star because of the elements produced during the parent’s lifetime. We can record the metallicity (the proportion of stellar matter made up of metals) and determine a system of stellar generations called Population.

Population I stars are stars with high metallicity. They are found in the disks of galaxies, particularly in the spiral arms like open clusters. The sun is a Population

12 I star. Population II stars have low metallicity and tend to be found in Galactic bulge and globular clusters. They tend to be older, less luminous and cooler than

Population I stars.

1.3 The Infrared Telescopes

All of the telescopes used in this thesis are infrared telescopes. The infrared telescope is similar in operation to an optical telescope but diﬀers slightly from the silicon detectors and charge-coupled devices (CCD) that collect visual light. IR telescopes need to be shielded from local heat sources, because infrared radiation is emitted by warm objects. This is typically achieved by chilling the detectors with liquid nitrogen. Most infrared radiation (and certainly all IR signals in this thesis) has too low of energy to excite a CCD via the photo-electric eﬀect. New technology had to be developed to detect the low energies of infrared wavelength radiation.

1.3.1 NIR Technology

The band-gap of silicon does not detect beyond 1.1 μm[60].TheﬁrstNIR detector was made from galena, a natural mineral form of lead sulﬁde (PbS) in

1904 [14]. However, the ﬁrst PbS detectors suitable for research were a product of

German research during World War II [54]. The development of interference filters set up the first useful photometric system in 1962 [51, 40] that enabled the first NIR survey, the Two Micron Sky Survey (TMSS) [71, 40]. Then, PbS was replaced by indium antimode (InSb), which works in the 1 - 3.8 μm range, and paved the way

13 for IRTF.

1.3.2 IRTF

The National Aeronautics and Space Administration (NASA) Infrared Tele- scope Facility (IRTF) has been operating since 1979 [87]. The 3.0 meter telescope is located at the peak of Mauna Kea, Hawaii [17] and is operated by the Univer- sity of Hawaii [87]. The telescope is situated at 4200 m. This greatly reduces the telluric interference from atmospheric water vapor and improves observing at the

J, H, KS bands. In section 1.6.6, I talked about how the atmosphere radiates in the infrared with rapid ﬂuctuation. A NASA survey conducted before construction of

IRTF showed that these ﬂuctuations are relatively low at Mauna Kea [66].

IRTF houses NSFCAM2, a 1-5 micron camera used for photometric study

(see table 1.4). The camera uses a 2048x2048 mercury cadmium telluride (HgCdTe) detector array [82]. The image scale is 0.04 arcsec/pixel, giving a ﬁeld of view of 80”x80” [76]. IRTF also houses the spectrographic tool SpeX [75], used in obtaining spectra for [BDS2003]107 in Chapter 2 [41]. SpeX is a medium-resolution spectrograph that works over the range 0.8-5.5 μm. SpeX uses a 1024×1024 InSb array in its spectrograph, and has spectral resolution of R ∼1000-2000 across 0.9-2.5

μm. The slit viewer on SpeX uses a 512×512 InSb array, and was used to take the acquisition images in Chapter 2.

14 1.3.3 VLT

The European Southern Observatory (ESO) Very Large Telescope (VLT) is a series of four 8.2 m telescopes and four movable auxiliary 1.8 m telescopes. The four main telescopes are the Antu telescope (UT1, named for the Sun), the Kueyen telescope (UT2, named for the Moon), the Melipal telescope (UT3, named for the

Southern Cross), and the Yepun telescope (UT4, named for Venus) [35]. The VLT is located on Cerro Paranal in Atacama, Chile at an altitude of 2635 m.

The VLT UT3 houses the Infrared Spectrometer and Array Camera (ISAAC), used in obtaining spectra for Westerlund 1 in Chapter 3. ISAAC is a high resolution spectrograph that has two arms which work in diﬀerent NIR wavelength ranges. The

Hawaii Rockwell arm that produced the spectra in Chapter 3 works at 1-2.5 μm.It is a 1024×1024 HgCdTe array, and has spectral resolution of R ∼4400 [65].

1.3.4 NTT

The ESO New Technology Telescope (NTT) is a 3.6 m telescope located at

La Silla Observatory, in the south of the Atacama Desert in Chile. NTT houses two instruments: the visual light camera and spectrograph ESO Faint Object Spec- trograph and Camera 2 (EFOSC2), and the NIR camera and spectrograph Son of

ISAAC (SOFI). SOFI is a low-resolution spectrograph [36]. It was used to obtain photometric data of Westerlund 1, discussed in Chapter 3. It employs a 1024×1024

HgCdTe array and has spectral resolution of R ∼600-2200 across 0.9-2.5 μm [64].

15 1.4 Stellar Clusters

Generally, a star cluster is a group of stars that are formed from the same molecular cloud. Star clusters are bound by gravity when they are born. Star clusters are called laboratories because so many of the variables are already set

[13]. Since clusters come from the same giant cloud, they have similar chemical composition; they all have the same age, the same distance, and the same extinction.

Stars within the cluster have similar locations; the largest cluster in this thesis focuses on stars within about a 2.5 arcminute area (see chapter 3).

Traditionally, there are two basic types of star clusters: larger, densely packed

Globular Clusters (GC), and relatively small, gravitationally tenuous Open Clusters

(OC). There is a third type of cluster called Super Star Clusters (SSC), which are believed to be precursors to the GC (see section 1.4.3) [39]. Since both the OC and the SSC are young clusters, they are found in the plane of the galaxy where star forming regions exist.

It is vital to make a distinction between visual star clusters and true clusters.

A visual star cluster is a group of stars that are not local to each other, but rather appear to be a cluster because their extinction is low, i.e. there is a window or gap in the ISM contributing to less extinction rather than showing a true cluster. Ap- proximately half of cluster candidates found in infrared sky surveys are true bound clusters (see section 1.4.4), while the other half are these false clusters representing only an apparent grouping of stars.

16 1.4.1 Globular Clusters

As the name suggests, Globular Clusters (GC) are a collection of stars that have a general spherical shape. They are also spherically distributed throughout the

5 galaxy, placing them in the halo of the Milky Way. They have masses ∼ 10 M, and are tightly bound by gravity. It is for this reason, GC are very dense with

3 −3 ρc ≥∼ 10 Mpc ([92], Table 1) at their centers. GC are old relics of the early

Universe, ∼ 10 Gyr ([92], Table 1.) having low metallicities (see section 1.2). There are about 150 discovered GC in Milky Way [44] out of an estimated 180±20 existing

GC [4]. Figure 1.4 shows the color-magnitude diagram (CMD, see section 1.2.1) of a typical GC called Messier 2. In Figure 1.4, there are prominent giant and horizontal branches, seen because of the advanced age. Solar mass stars have already evolved into red giants, and the main sequence (MS, see section 1.2.1) stars are extremely faint red dwarfs. The stars of M2 are metal-poor, Population II stars (see section

1.2). There are in fact two types of GC discovered by Pieter Oosterhoﬀ in 1939 showing both metal-rich (Type I) and metal-poor (Type II) GC [72]. It should be noted that the stars in Type I Oosterhoﬀ GC still have lower metallicities than stars in younger clusters.

GC are very well studied because there is such low extinction in the halo of the Milky Way they are easily seen. The stars in GC are the the oldest known objects in the Milky Way, so they give important information about the production of the very ﬁrst elements. They show stars as they existed in the early Universe and during the formation of the galaxy.

17 Figure 1.4: The color Magnitude Diagram of Messier 2 (M2, also called NGC 7089), showing an extensive horizontal giant branch (taken from Carney, ﬁgure 13 [18]). M2 is a GC that can be seen in the constellation Aquarius [21]. It is ∼11.5 kpc away and its age is estimated to be 13 Gyr. Even though M2 is one of the largest known GC, the CMD is typical of globular clusters.

18 1.4.2 Open Clusters

Open Clusters (OC), like the cluster [BDS2003]107 in Chapter 2, are the small- est category of star clusters. They can be a few tens to a few hundreds solar masses

3 of stars, though [BDS2003]107 and most others are ∼ 10 M ([92], Table 1). OC are found entirely in the disk of the galaxy, almost always in the spiral arms. Like

SSC they are young with lifetimes less than ∼ 1 Gyr. The kinematics of the OC combined with the gravitational disturbances and the rotation of the Milky Way quickly disperse most of these smaller clusters.

Open Clusters are especially useful as laboratories for observing the properties of stars. They are more common than the SSC and many will have high mass stars still on the MS. Their distances can be determined by a photometric study to give us the structure of the galactic disk. The age of the cluster is determined by the

Hertzsprung−Russell (HR) diagram (see section 1.2.1). When the HR diagram is observed for a cluster, MS stars will begin to turn oﬀ the theoretical curve at the upper end. The hottest stars, which have the shortest lifetimes, will have gone through the hydrogen burning stage. This MS turnoﬀ point can then determine the age of the cluster. The most massive stars are the O− and B−stars. Any O−stars will be gone by ∼10 Myr, which is long before K− and M−stars have even made it to the main sequence. This gives us only a short window to observe these high mass stars, which is why the study of young, massive clusters is so important.

19 1.4.3 Super Star Clusters

Super Star Clusters (SSC) are a special class of young, very massive open clusters. SSC have ages ∼ 100 Myr or less (Table 1 in Zwart [92].), and they are massive enough to survive for ∼ 10 Gyr or longer. They have masses ranging

4 7 10 − 10 M and are believed to be the evolutionary precursors of globular clusters

[39]. Though there are a few SSC in the Milky Way and the Local Group of galaxies,

SSC are particularly abundant in starburst galaxies [92]–galaxies with an unusually high rate of star formation compared to other galaxies. The abundance of SSC in starburst galaxies is no accident. It is estimated that ∼ 60 − 90% of stars are born in clusters [56], [55], so it follows that the majority of SSC would be in galaxies with high star formation rates.

Very young SSC contain a very large number of high mass stars hidden in a cloud of ionized gas. Previously unknown clusters such as the Arches and Quin- tuplet clusters are so obscured and so distant that they are invisible in the visual wavelengths [37]. These clusters are near the center of the Milky Way (∼ 8kpc away) and represent the densest clusters in the galaxy. But their sizes pale in comparison to nearby SSC Westerlund 1, which is the topic of chapter 3. Like Arches and Quintuplet, Westerlund 1 is behind a dust cloud contributing to high extinction in the visible with Av ≈ 11, making it eﬀectively invisible in that wavelength.

With a much lower extinction in the infrared (see Chapter 3), Westerlund 1 is an easier target than Arches and Quintuplet for studies of its stellar population [92].

Observations have found a huge number of high mass stars in Westerlund 1 [15].

20 1.4.4 The Near Infrared Sky Surveys

Essential to analyzing stellar clusters are the sky surveys that discover them.

In 1997 2MASS became the successor of the Two Micron Sky Survey (TMSS, [71]), which had outgrown its usefulness as a tool for NIR astronomy. 2MASS surveyed the entire sky in the three photometric bands discussed in this thesis: J, H, Ks (see table 1.4). 2MASS went far deeper than the previous surveys with a 10-σ limits of J =15.8, H =15.1andKs =14.3 magnitudes [30]. 2MASS is sponsored by the University of Massachusetts, the Infrared Processing and Analysis Center

(IPAC), NASA, and the National Science Foundation (NSF). The Two Micron All

Sky Survey (2MASS, [83]) has been a primary tool in locating new stellar clusters in the galactic plane. Clusters have been detected by individual inspection of the survey images (e.g. Dutra et al. [33] and Bica et al. [8] in 2003) and automated methods searching for peaks in the stellar surface density.

Recently, an algorithm which ﬁts the sizes of these high stellar densities to a

2-variable Gaussian function [63], which has been applied to the mid-infrared survey

Galactic Legacy Infrared Mid-Plane Survey Extraordinaire (GLIMPSE, [5]). Using these methods, over 2000 star clusters or candidates have been found (e.g. 1461 candidates: [31], [32], [33], [8], [38] and 761 conﬁrmed clusters: [48], [12], [13], [63],

[38]).

21 1.5 Tools of the Observational Astronomer

Essentially all information about astronomical objects comes from observations of the EM spectrum. This light contains a great deal of information which can be carefully analyzed. The light carries two bits of raw data: the number of photons incident on the detector (a function of the wavelength) and the location of where those photons originate. The latter is part of the astrometry and is discussed in chapters 2 and 3. The number of photons incident on the telescope detector gives rise to the magnitude of the star, and it is a function of the wavelength. The analysis of this light is segmented into categories based on the resolution of the observation (see section 1.6.4). The lowest resolution corresponds to photometry, where the light is measured of broad wavelength bands. At higher resolutions, the realm of spectroscopy takes over, which is also segmented into low−,medium− and high−resolution spectroscopy.

1.5.1 Photometry

Photometry is the analysis of ﬂux measurements over large wavelength bands.

The NIR photometry in this thesis covers the J−,H−,K−bands which generally have effective wavelengths 1.25 μm,1.65μm,and2.2μm. Each detector is different, and the ones used for data collection in this thesis have been listed in Table 1.4. The interior of the star is opaque and acts like a blackbody radiating at a single effective surface temperature (Teff ). The blackbody spectrum is what interacts with the ISM to cause extinction. This spectrum also interacts with the outer atmosphere of the

22 star which creates the spectral features of the star (section 1.5.2).

Table 1.4: Photometric Bands.

Instrument J(μm)WidthJH(μm)WidthHKs(μm)WidthKs

ISAAC 1.2 ... 1.6 ... 2.2 ...

SOFI 1.247 0.290 1.6534 0.297 2.162 0.275

NSFCAM 2 1.26 0.17 1.62 0.30 2.15 0.35

2MASS 1.25 ... 1.65 ... 2.17 ... ISAAC: [65], SOFI: [64], NSFCAM 2: [82], [16], 2MASS: [88].

Each of the J−,H−,K−bands line up with a ”window” in the atmosphere.

Telluric absorption (see section 1.6.6), mainly due to water vapor, will block EM transmission. So the NIR bands are conveniently placed to line up in regions maximum transmission (see ﬁgure 1.5). In order to reduce CO2 absorption, it has been advantageous to replace the K−band with the narrower Ks−band (read ”k short”) in newer instruments.

1.5.2 Spectroscopy

Spectroscopy is the study of the ﬂux of photons as a function of wavelength. It is similar to photometric analysis but operates at a much higher spectral resolution.

That is Δλ is much smaller (see equation 1.10). This allows us to see individual atomic and molecular lines, such as CO band-heads. Stars are approximated by their two main components. If the interior of the star behaves mostly like a blackbody

23 aitn tasnl ffcietmeaue t temperature, IR- effective single program a the at radiating using [1]. pages produced web transmission lines worldwide The UKIRT telluric the the from Mathematica. and obtained with [64], were TRANS4, made SOFI from curve are transmission curves JHK 1.5: Figure uha h antcfil ftesa,cnb eemndbtaentdsusdin discussed not are but determined be can thesis. star, this the of details, Other field magnetic star. the the as of such motion the even the or into temperature, its details composition, giving stellar emission, and absorption through radiation blackbody that Normalized Flux 0.2 0.4 0.6 0.8 0. 1.

H2O 3 1.0 0 0 2 7 5

H2O 2 5 0 J 2 2 5

H2O J 2 1.5 KTasiso Curv Transmission HK 0 0 W F H requency avelength 24 1 7 5 TH Μ e h tla topeeitrcswith interacts atmosphere stellar the hen m z es H2O 1 2.0 5 0 K s 1 2 5 2.5 CO2 7 5 2 0 1 00. 5. 0. 5. .

Percent Absorption by the Atmosphere 1.6 Deﬁnition of Basic Terms

Here is a quick section of astronomical terms. The terminology here is intended to be very basic but is important to understanding this thesis.

1.6.1 Celestial Coordinates

The locations of objects in the sky are mapped out in several types of coordinate systems called celestial coordinates. Every system will use a system of spherical coordinates projected onto the imaginary sphere that appears to contain the distant stars. Observational astronomers will mainly use equatorial coordinates for the convenience it has with Earth-bound observations. In some cases, such as the radial velocity calculations in section 2.1.2, the use of galactic coordinates are more convenient.

1.6.2 Equatorial Coordinates

The celestial coordinate system is the projection of the Earth’s axis of rotation out into the sky. This immediately deﬁnes the celestial equator. The celestial equivalent of latitude is called declination (δ, Dec.) and is measured in degrees with zero set to the equatorial equator. The celestial equivalent of longitude is called right ascension (α, R.A.) and is measured in time for historical reasons. The zero point of R.A. is the point at which the Sun crosses the celestial equator on the vernal equinox.

Because of the precession of the equinoxes, it is important to calibrate mea-

25 surements to a standard epoch. That is, some moment in time used as a reference point for time-varying quantities. The current epoch is J2000.0, which is 1 January

2000 at 12:00:00 terrestrial time (TT) as deﬁned by the International Astronomical

Union.

To put all of this this into perspective with respect to the clusters studied in this thesis, the supermassive black hole that lies at the center of the Milky Way,

Sagittarius A*, is at a location R.A.(J2000.0) 17h45m40.045s Dec. −29◦027.9”.

1.6.3 Galactic Coordinates

The galactic coordinate system uses the sun as the center and the direction of the center of the Milky Way is (almost) the zero point. This is oﬀset slightly from Sagittarius A* at galactic longitude (LII) 359◦5639.5”, galactic latitude (BII)

0◦0246.3. For the purposes of the broad calculations in section 2.1.2 the center of the Milky Way can be taken to be zero. The direction of the galactic north pole points upward by right-handed coordinates. Compared to the equatorial system, the galactic north pole is at a location R.A.(J2000.0) 12h51m26.282s Dec.

+27◦0742.01”.

1.6.4 Resolution

The spectral resolution is the measure of detail in the electromagnetic spectrum obtained from the telescope. It is equal to

λ R = (1.10) Δλ

26 where λ is the wavelength of light and Δλ is the division in wavelength of the instrument.

1.6.5 Seeing

The astronomical seeing is the measure of the full width at half maximum

(FWHM) of the point spread function (PSF) of the image as it passes through the atmosphere. The PSF is the spatial distribution produced by light from a point source. The blurring of stars is due to turbulent mixing in the atmosphere which constantly changes the refractive index.

1.6.6 Telluric Contamination

Absorption lines or bands in the spectrum of a celestial object are due to absorption by gases, such as oxygen, water vapor, or carbon dioxide, in Earth’s atmosphere. This absorption is part of the total extinction, adding to the effect from the ISM. Photometry is taken in the wavelength bands where the telluric interference is minimal (see section 1.5.1, figure 1.5). In infrared astronomy, the effects of telluric contamination can be troublesome because the atmosphere is also a source of rapidly fluctuating infrared radiation [87].

27 Chapter 2

The Open Cluster [BDS2003]107

The open cluster [BDS2003]107 (hereafter called cluster 107) was ﬁrst discovered using the 2MASS survey by Bica et al. (2003) [8]. A follow-up study carried out by Borissova et al. (2005) conﬁrmed cluster 107 to be a true cluster [13] when they obtained higher resolution H-andK-band images. They associated cluster

107 with the HII region WC89 005.09-0.39A, which is in the general direction of

h m s ◦ the center of the Milky Way at R.A.(J2000.0) 18 00 42 Dec. −24 04 23”. An HII region is a large cloud of ionized gas in which star formation has recently taken place. The association of the HII region constrains the age of cluster 107 to be younger than 5 Myrs. The largest conﬁrmed cluster of their survey, the mass of cluster 107 was estimated to be 800-1000 M with an upper limit of 4500 M,and an extinction of Av = 18.2.

Data were obtained 14-15 June 2005 at the National Aeronautics and Space

Administration (NASA) Infrared Telescope Facility (IRTF) using SpeX [75] by Mar- garet Hanson and Yara Beshara. My role came with the analysis of the astrometry and the acquisition of 2MASS photometry listed in tables 2.1 - 2.4 and is detailed in the two sections of this chapter. From the photometry a color-color diagram and a color-magnitude diagram are made, which allows us to view the properties of the stars such as extinction and distance especially since the spectral type can be seen

28 from the high-resolution spectroscopy. The locations of 130 stars in the cluster, listed in tables 2.1 − 2.4, were calculated using a set of software packages designed for eﬀective star cluster astrometry. Calculations were made to ﬁnd the radial velocity of the cluster from the Doppler shift of the spectra. From these we can try to constrain the kinematics of the cluster with the galactic structure. I chose star

#23todoaredshiftanalysis,becauseofits strong CO absorption features. In the process, however, I found that the star is not associated with the cluster.

2.1 Astrometry

Astrometry is the determination of the positions of stars in the ﬁeld from precise measurements. The movements of the stars also fall into this category of astrophysics. The basic idea behind the method of determining the stellar positions is to compare the unknown coordinates on an acquisition image to some known coordinates on the same image [7]. The known coordinates are taken from 2MASS measurements [30]. In this way the astrometry is bootstrapped to 2MASS. In a broad sense, the astrometry gives us a reference for reporting the stars found in the cluster. Follow-up studies on individual stars can be done easier with accurate astrometry for example. The position and velocity of any galactic disk cluster is vital in establishing constraints for models of galactic mechanics. This way, we can understand the structure of the Milky Way and ﬁgure out astrophysical quantities that are not easily observed, such as the distribution of dark matter.

29 2.1.1 Star Locations

I made precise calculations of the positions of stars in cluster 107. The locations of the the stars in the ﬁeld were found by using a set of Image Reduction and

Analysis Facility (IRAF) software packages specifically designed for crowded field astrometry titled STARFIND [81], CCMAP [79], and CCTRAN [80]. In addition, a program I wrote titled FORMAT.PRO was used to format the output of STARFIND in order to be used as input for CCMAP. This method computes the plate solutions using matched pixel and celestial coordinate list. The celestial coordinate list was taken from 2MASS data that our survey showed to be single, un-blended stars. The power of this method comes from the use of a point spread function (PSF), which is the best fit of the single star that takes a 2-variable Gaussian distribution.

STARFIND makes the assumption that the point spread function can be approximated by a radial Gaussian function with σ = 0.84932 × the half width at half maximum (HWHM) PSF in pixels [81]. After the PSF is found for the image, it is applied to each point on the field. Minimum and maximum photon counts are defined and rejected from the fit. A boundary extension algorithm is used to evaluate out of bounds image pixels. Once it has determined the shape of the PSF, any object that is a star will have a shape similar to the PSF. In this way stars can be identified in the field, though it was unnecessary for the stars in cluster 107.

Once the PSF is matched to every point within the deﬁned range of good pixels, the following output is generated: the positioninpixel-spaceandthesizeofthesource

(area and FWHM). In addition the shape of the source (roundness and sharpness)

30 is measured as well as the relative magnitudes, which will become important for the photometry calibration (see section 2.2). Taking the data, I used my program

FORMAT.PRO to extract only the position data. I used the imaging program DS9 to find the pixel position of every individual star in tables 2.1 − 2.4 and matched it to the output from STARFIND, eliminating superfluous data. Additional iterations were computed to find fainter stars as well as stars that were obscured in the PSF

ﬁts from nearby brighter stars.

After all the necessary iterations were completed, select data was used to get a coordinate transformation solution. Stars with known positions in 2MASS [30] were chosen to make a matched pixel and celestial coordinate (J2000.0) list. The

CCMAP package then computed the transformation equation that CCTRAN would extrapolate the star locations for the entire ﬁeld. CCMAP worked in equatorial coordinates, and the equations were of the form:

⎧ ⎪ ⎨⎪ α = f(x, y) ⎪ (2.1) ⎪ ⎩ δ = g(x, y) where x and y are the pixel coordinates. The quadratic polynomial was initially assumed to have no cross terms, but yielded incorrect locations further from the central solution point. I resolved the transformation equation to include the cross terms, which provided a correct solution (equation 2.2 includes the full cross term relation).

31 ⎧ ⎪ ⎪ 2 2 2 2 2 2 ⎨ α = a11 + a12y + a13y + a21x + a22xy + a23y x + a31x + a32x y + a33x y ⎪ ⎩⎪ 2 2 2 2 2 2 δ = b11 + b12y + b13y + b21x + b22xy + b23y x + b31x + b32x y + b33x y (2.2)

Once the transformation equation is computed, the CCTRAN package applies the coordinate transformation solution to the list of pixel coordinates generated by

STARFIND to produce the star locations found in tables 2.1 - 2.4.

Finally, a star ﬁnding chart was created from stacked K-band images of the

field (see figure 2.1). Images were taken with the acquisition camera on SpeX. The acquisition images are 2, five-second integrations and are approximately 1 × 1 in area. No single image covered the entire cluster as shown, so I made a mosaic of

5 of the highest resolution images to make the star finding chart. Figure 2.1 was created using the imaging program Photoshop CS [2]. The brightness and contrast of the finding chart was altered to show definition and provide a qualitative tool in locating the relative position of the stars. Figure 2.1 itself is not intended for photometric study.

2.1.2 Radial Velocity

I attempted calculate the radial velocity of the cluster by examining the

Doppler shift of the single star #23. Since all the stars in a cluster have the same proximity and therefore similar velocities, it was advantageous to use a star which we had good spectra with well deﬁned features. Star #23 is the brightest star for which we had spectra and had strong CO absorption lines (see ﬁgure 2.2). However,

32 Table 2.1: Stars Observed.

Star 2MASS J, H, K α(2000) δ(2000) K SpectralType Cluster 107 #1 16.441, 14.912, 13.807 18:00:40.12 -24:04:15.2 Cluster 107 #2 18:00:40.17 -24:04:51.7 Cluster 107 #3 15.479, 14.890, 13.820 18:00:40.19 -24:04:43.6 Cluster 107 #4 18:00:40.23 -24:05:03.6 Cluster 107 #5 16.419, 14.569, 12.730 18:00:40.33 -24:04:30.8 Cluster 107 #6 12.663, 11.615, 11.351 18:00:40.33 -24:04:58.8 Cluster 107 #7 18:00:40.36 -24:04:52.5 Cluster 107 #8 18:00:40.38 -24:05:05.4 Cluster 107 #9 18:00:40.41 -24:05:04.4 Cluster 107 #10 16.013, 13.842, 12.247 18:00:40.48 -24:04:54.2 Cluster 107 #11 18:00:40.62 -24:04:36.7 Cluster 107 #12 18:00:40.67 -24:05:05.9 Cluster 107 #13 18:00:40.69 -24:04:47.4 Cluster 107 #14 18:00:40.71 -24:04:11.5 Cluster 107 #15 18:00:40.78 -24:04:15.2 Cluster 107 #16 18:00:40.79 -24:04:16.4 Cluster 107 #17 14.595, 12.445, 11.407 18:00:40.81 -24:04:48.2 B2? Cluster 107 #18 15.961, 14.930, 13.289 18:00:40.81 -24:04:59.6 Cluster 107 #19 17.256, 14.735, 13.557 18:00:40.86 -24:04:54.3 Cluster 107 #20 18:00:40.97 -24:04:19.0 Cluster 107 #21 18:00:40.99 -24:04:21.0 Cluster 107 #22 18:00:41.03 -24:04:19.5 Cluster 107 #23 13.784, 12.320, 9.473 18:00:41.09 -24:04:46.3 CO abs. Cluster 107 #24 18:00:41.11 -24:04:33.0 Cluster 107 #25 13.5, 11.3, 10.2 * 18:00:41.13 -24:04:08.9 O5 Cluster 107 #26 18:00:41.13 -24:04:19.0 Cluster 107 #27 18:00:41.13 -24:04:20.4 Cluster 107 #28 18:00:41.14 -24:04:14.5 Cluster 107 #29 18:00:41.15 -24:04:34.5 Cluster 107 #30 18:00:41.17 -24:04:32.5 Cluster 107 #31 15.6, 12.9, 11.1 * 18:00:41.18 -24:04:06.7 Cluster 107 #32 18:00:41.18 -24:04:22.7 Cluster 107 #33 18:00:41.20 -24:04:21.8 Cluster 107 #34 18:00:41.21 -24:04:31.0 Cluster 107 #35 18:00:41.25 -24:04:25.1 *Stars for which the 2MASS photometry was blended are given in italics and are expected to be less reliable. See section 2.2.

33 Table 2.2: Stars Observed.

Star 2MASS J, H, K α(2000) δ(2000) K SpectralType Cluster 107 #36 18:00:41.26 -24:04:14.4 Cluster 107 #37 18:00:41.26 -24:04:27.5 Cluster 107 #38 18:00:41.30 -24:04:29.7 Cluster 107 #39 18:00:41.32 -24:04:21.3 Noisy Cluster 107 #40 18:00:41.32 -24:04:32.0 Cluster 107 #41 12.956, 12.076, 11.735 18:00:41.38 -24:04:45.4 Cluster 107 #42 18:00:41.38 -24:04:14.6 Cluster 107 #43 18:00:41.39 -24:04:21.6 Cluster 107 #44 18:00:41.40 -24:04:32.4 Cluster 107 #45 18:00:41.40 -24:04:23.0 Cluster 107 #46 18:00:41.40 -24:04:29.4 Cluster 107 #47 18:00:41.41 -24:04:25.2 Cluster 107 #48 18:00:41.45 -24:04:21.9 Cluster 107 #49 18:00:41.46 -24:04:24.1 Cluster 107 #50 18:00:41.47 -24:04:25.7 Cluster 107 #51 18:00:41.47 -24:04:17.3 Cluster 107 #52 18:00:41.49 -24:04:25.3 Cluster 107 #53 18:00:41.53 -24:04:45.0 Cluster 107 #54 18:00:41.54 -24:04:16.0 Cluster 107 #55 15.559, 13.576, 12.644 18:00:41.56 -24:04:04.3 Cluster 107 #56 18:00:41.65 -24:04:38.1 Cluster 107 #57 18:00:41.72 -24:04:22.5 Cluster 107 #58 18:00:41.74 -24:04:19.7 Cluster 107 #59 18:00:41.76 -24:04:05.1 Cluster 107 #60 18:00:41.77 -24:04:11.5 Cluster 107 #61 18:00:41.77 -24:04:26.7 Cluster 107 #62 18:00:41.78 -24:04:21.3 Cluster 107 #63 16.349, 13.545, 11.780 18:00:41.79 -24:04:51.0 CO em. Cluster 107 #64 18:00:41.82 -24:04:40.9 Cluster 107 #65 16.383, 14.335, 13.457 18:00:41.85 -24:04:17.2 Cluster 107 #66 18:00:41.87 -24:04:04.4 Cluster 107 #67 15.779, 13.356, 12.617 18:00:41.91 -24:04:35.9 Noisy Cluster 107 #68 18:00:41.97 -24:04:12.1 Cluster 107 #69 18:00:41.99 -24:04:28.9 Cluster 107 #70 18:00:42.01 -24:04:22.2

34 Table 2.3: Stars Observed.

Star 2MASS J, H, K α(2000) δ(2000) K SpectralType Cluster 107 #71 18:00:42.07 -24:04:19.4 Cluster 107 #72 18:00:42.07 -24:04:51.3 Cluster 107 #73 18:00:42.09 -24:04:25.5 Cluster 107 #74 18:00:42.09 -24:04:37.4 Cluster 107 #75 18:00:42.12 -24:04:51.3 Cluster 107 #76 18:00:42.13 -24:04:22.6 Cluster 107 #77 18:00:42.14 -24:04:32.5 Cluster 107 #78 18:00:42.16 -24:04:22.1 Cluster 107 #79 18:00:42.18 -24:04:26.0 B2? Cluster 107 #80 18:00:42.18 -24:04:39.2 Cluster 107 #81 18:00:42.19 -24:04:18.0 Cluster 107 #82 18:00:42.22 -24:04:31.9 Cluster 107 #83 18:00:42.23 -24:04:15.0 Cluster 107 #84 18:00:42.23 -24:04:48.2 Cluster 107 #85 18:00:42.24 -24:04:16.9 Cluster 107 #86 18:00:42.27 -24:04:34.7 Cluster 107 #87 13.244, 12.204, 11.937 18:00:42.31 -24:04:10.6 Cluster 107 #88 18:00:42.37 -24:04:19.0 Cluster 107 #89 18:00:42.41 -24:04:17.7 B3 Cluster 107 #90 18:00:42.41 -24:04:21.1 Cluster 107 #91 16.174, 15.232, 13.706 18:00:42.41 -24:04:39.6 Cluster 107 #92 18:00:42.52 -24:04:15.7 Cluster 107 #93 18:00:42.55 -24:04:26.1 Cluster 107 #94 18:00:42.58 -24:04:35.6 Cluster 107 #95 18:00:42.59 -24:04:29.8 Cluster 107 #96 18:00:42.63 -24:04:51.0 Cluster 107 #97 18:00:42.64 -24:04:04.6 Cluster 107 #98 15.173, 14.113, 12.102 18:00:42.76 -24:04:49.4 Cluster 107 #99 18:00:42.77 -24:04:21.4 Cluster 107 #100 18:00:42.77 -24:04:42.7 Cluster 107 #101 18:00:42.79 -24:04:17.4 Cluster 107 #102 18:00:42.79 -24:04:36.8 Cluster 107 #103 18:00:42.80 -24:05:00.6 Cluster 107 #104 18:00:42.82 -24:04:34.5 Cluster 107 #105 18:00:42.85 -24:04:19.7

35 Table 2.4: Stars Observed.

Star 2MASS J, H, K α(2000) δ(2000) K SpectralType Cluster 107 #106 18:00:42.89 -24:04:57.8 Cluster 107 #107 15.798, 14.285, 12.733 18:00:42.91 -24:04:07.6 Cluster 107 #108 18:00:42.93 -24:04:17.7 Cluster 107 #109 18:00:43.02 -24:04:20.3 Cluster 107 #110 15.440, 12.932, 11.499 18:00:43.02 -24:04:49.4 Cluster 107 #111 13.639, 13.015, 12.245 18:00:43.10 -24:04:44.8 Cluster 107 #112 18:00:43.11 -24:04:34.6 Cluster 107 #113 18:00:43.12 -24:04:20.5 Cluster 107 #114 18:00:43.16 -24:04:41.4 Cluster 107 #115 18:00:43.17 -24:04:48.8 Cluster 107 #116 18:00:43.21 -24:04:49.8 Cluster 107 #117 18:00:43.22 -24:04:53.5 Cluster 107 #118 15.9, 13.5, 11.6 * 18:00:43.31 -24:04:52.7 Cluster 107 #119 15.3, 12.3, 10.4 * 18:00:43.33 -24:04:54.9 Noisy Cluster 107 #120 18:00:43.41 -24:05:03.8 Cluster 107 #121 18:00:43.53 -24:04:55.7 Cluster 107 #122 18:00:43.53 -24:04:51.9 Cluster 107 #123 14.989, 13.769, 12.590 18:00:43.56 -24:04:45.6 Cluster 107 #124 14.624, 14.100, 13.485 18:00:43.72 -24:04:31.5 Cluster 107 #125 15.820, 14.934, 13.375 18:00:43.77 -24:04:10.6 Cluster 107 #126 18:00:43.78 -24:05:01.1 Cluster 107 #127 18:00:43.93 -24:04:56.4 Cluster 107 #128 12.960, 12.607, 12.491 18:00:44.02 -24:05:05.0 Cluster 107 #129 15.025, 14.420, 13.902 18:00:44.11 -24:04:18.1 Cluster 107 #130 18:00:44.11 -24:04:57.2 *Stars for which the 2MASS photometry was blended are given in italics and are expected to be less reliable. See section 2.2.

36 Figure 2.1: SpeX K-band image of Cluster 107 with star numbers from tables 2.1 - 2.4 identiﬁed. The image was taken with the IRTF/SpeX instrument in June 2005. The ﬁeld of view is approximately 1 × 1.25.

37 rmitoutr hsc,i a esonta h ope ffc ssonb the by shown is effect Doppler relation: the that shown be can it velocity. physics, transverse introductory If the From calculate velocity. we radial then known, the are call motion we proper and which distance path, the this along lies that velocity the measure Ea to confined is point vantage our because lig of shift Doppler the observe only can We us 107. methods cluster other with two by associated confirmed not is was This star the not that is suggested It analysis velocity absorption. this 2.2.6). CO and strong 2.1.2 showing sections #23 (see 107 cluster the Cluster with of associated Spectra 2.2: Figure Normalized Flux 0.2 0.4 0.6 0.8 1.0 0.0 h ailvlct,i hudb oe,i ieetfo h bouevelocity. absolute the from different is noted, be should it velocity, radial The 1.9 2.0 V 2.1 RAD C W L107 avelength 38 = t.Bcueo hs ecnol directly only can we this, of Because rth. n h htmty(e eto 2.2.6). section (see photometry the ing

to ieo-ih ahfo h star, the from path line-of-sight a on ht c Μ m 2 Δ λ 3 0 λ 2.2

_12CO_ 2.3

_12CO_

_13CO_ _12CO_

_12CO_ 2.4 (2.3) where c is the speed of light, and λ0 is the wavelength a feature in the spectra would lie if the object had zero velocity.

Light from a star in the cluster with significant enough radial velocity will be show a measurable Doppler effect. Finding the radial velocity from stellar spectra is done by calculating it directly from equation 2.3. The fluctuations in the spectra will create a lot of error in the calculation, so the signal-to-noise ratio needs to be high. There should be prominent emission or absorption features to make Δλ easier to match. Star #23 has strong CO absorption features, which makes it ideal for this sort of calculation. I needed to compare the spectra to a standard star in order to have a reference. I chose the M1 supergiant HD14404 from the IRTF spectral library [29]. The CO absorption features in HD14404 would also show at the same wavelengths in a giant star as well, so the comparison could still be made without knowing if star #23 is a supergiant or a giant (see section 2.2.6).

The starlight passes through the spectrometer on SpeX and is collected by the

InSb chip. The wavelength is measured in pixel space and has to be converted to wavelength space.

1Pixel =5.5 × 10−4μm (2.4)

If star #23 is moving towards the Earth, then the wavelengths of light will be blueshifted. But if the star is moving away, then the light is redshifted. In either case, the spectrum will not fall on the same pixels as a stationary star would. Figure

2.3 shows plots of the spectrum for stars #23 and HD14404, and the ﬂux diﬀerence

39 Figure 2.3: The ﬂux has been normalized so that an accurate comparison can be made. Both sets of data were taken using SpeX. HD14404 is taken from Cushing 2005 [29].

40 between the two.

Sgr A

Star 23

V Φ VR

Earth

Image is not drawn to scale.

Figure 2.4: Image is not drawn to scale.

If the two stars were moving at the same radial velocity, then the flux difference would be zero (or at least very close to zero). In galactic coordinates, star #23 is at a location of LII = 5.8945, BII = -0.4338. In figure 2.4 this corresponds to φ ≈ 6◦.

It is a very good approximation to assume a circular orbit for the cluster. Since φ is small, θ ≈ 90◦ − φ and:

VRAD ≈ V sin φ (2.5)

41 Figure 2.5: The rotation curve for the Milky Way. The location of the Earth is ∼8 kpc from the center, and the location of cluster 107 is ∼6 kpc from the center. Graph is from Clemens 1985 [26].

km ≈ km V, approximated from ﬁgure 2.5, is about 225 s . This puts VRAD -23.5 s for star #23. The negative sign in the velocity corresponds with an expected blueshift in the spectra.

The noise in the H−band makes the ﬂux diﬀerence too large to make any dis- cernable calculations, but the prominent CO absorption features are in the K−band.

In figure 2.6, I made a plot of the difference in the normalized fluxes versus a shift in the pixels around the 2.2935 μm CO feature. The minimum of the curve in

ﬁgure 2.6 corresponds with Δλ, albeit in pixel space. With a simple conversion, I

± km calculate that the radial velocity of star #23 is 70 75 s . I could get a much lower uncertainty in VRAD when I ignored the H−band data from my error calculation.

42 Figure 2.6: As the shift lines up for star #23 and HD14404 [29], the diﬀerence in the total ﬂux will drop to a minimum.

However much of the other spectra obtained had this noise in the H−band. The

∼ km velocity uncertainty of 75 s would then be characteristic of all the stars in cluster

107, too. The problem is that this star is redshifted! Even though the uncertainty allows for a small blueshift, it does not come close enough to the predicted value. In fact, the possibility of a signiﬁcant redshift in the radial velocity raises doubts about star #23 actually being in the cluster. That doubt is raised again in sections 2.2.4 and 2.2.5, and resolved in section 2.2.6. An approach to reconciling the theoretical and observed radial velocities is discussed in section 4.3.1.

43 2.2 Photometry

Even though the project was aimed at spectroscopic studies of cluster 107, quick images were taken in the J−,H−, and K-bands from the SpeX acquisition camera. From these acquisition images we estimate the limiting magnitudes to be

J ∼ 16.9, H ∼ 16.1, and K ∼ 16.6, which is deeper in all three bands than the

2MASS 10-σ limits. Photometry of the half dozen highest resolution images from the calibration of data set was obtained using the STARFIND software package [81],

∼ 1 though colors obtained through this process yielded 2 magnitude error.

2.2.1 STARFIND Photometry

I attempted to bootstrap the J, H, K instrument photometry for the acquisition images to the 2MASS magnitudes of all the un-blended stars in the ﬁeld. The

IRAF package STARFIND gave relative magnitudes along with the pixel space positions of every star in the ﬁeld. STARFIND uses the method of ﬁtting a 2-variable

Gaussian PSF to the light spread measured by the detector. The method for generating this PSF is detailed in section 2.1.1. The height of the PSF is recorded as the magnitude of the star at that location. While the relative magnitudes are automatically accurate (see sections 2.2.4 and 2.2.5) the magnitudes themselves are linearly shifted by an arbitrary zero point for the detector. From equation 1.1

Fλ mλ = −2.5Log + ZP (2.6) F0 where ZP is the zero point, an arbitrary standard of magnitude calibration. The

44 common procedure is to use Vega as F0, having zero magnitude relative to all other stars. One immediately can see that calibrating the instrumental response to Vega is unnecessary, as the contribution of F0 can be absorbed in the ZP constant. That is,

∗ mλ = −2.5LogFλ + ZP (2.7)

I wrote a program in Mathematica to calculate this zero point (ZP∗), bootstrapped to 15 stars found in 2MASS and our acquisition images for each photometric band. The stars needed to be resolved as individual stars in both images.

As such, every star was on the outskirts of the cluster, likely entirely beyond the half-mass radius of the cluster. Also the stars needed to be in nebula-free regions of the cluster, eliminating the brightest star in the cluster. Because the small list of stars for the bootstrap and because we did not solve color cross terms, the calibrated magnitudes generated by my program had high error associated with them

∼ 1 ( 2 magnitude error), and we did not use the magnitudes in the published work

[41].

2.2.2 Magnitude Splitting

The acquisition images often showed multiple, resolved stars where 2MASS showed the stars blended together as an unresolved blob. When the acquisition image showed only two stars had been blended by 2MASS, I separated the photometry using some basic physical relations. I deblended the magnitudes using two

45 similar but different methods. In the first method I calculated it numerically using Mathematica. In the second method I used the flux-magnitude relations in An

Introduction to Modern Astrophysics [20]. Both methods produce similar results.

In order to get the best resolved photometry, I used the IRAF software package

DAOPHOT[86]. DAOPHOT is similar to STARFIND in that it uses a PSF ﬁtted to each single star with the shape of a 2-variable Gaussian (see section 3.3.1), but

DAOPHOT has greater control to customize the shape of the PSF. From equations

1.1 and 1.2 it can be shown that the ﬂuxes of 2 stars, F1 and F2, are related to the luminosities of those stars, L1 and L2,by:

L1 M2 − M1 = −2.5Log (2.8) L2 and

L1 =100.4(M2−M1) (2.9) L2

Since these relations can work in ratios, it does not matter what the magnitude zero point is (especially since it’s diﬀerent depending on who you ask), and it does not matter what the bootstrap magnitudes are. The relative magnitudes are still accurate. Then the combined magnitude of the two stars is:

−0.4M1 −0.4M2 M2MASS = −2.5Log(10 +10 ) (2.10)

This equation and the measured diﬀerence in magnitudes give us a system that

I solved in Mathematica. In addition, I wrote a program that will calculate the

46 magnitudes using the ﬂux-magnitude:

F1 =2.512(M2−M1) (2.11) F2

F1 =2.512(M2MASS−M1) (2.12) F1 + F2 which will produce the same result within the uncertainty of the measured quantities.

The second method is preferred when working with Mathematica. The inverse function Mathematica needed to use in the second set of equations to ﬁnd a solution does not require a numerical method. The set of equations in the ﬁrst method

(Log{A + B}) is less straightforward. Neither the functions Solve or NSolve will produce a solution, and an iterative method had to be used.

I calculated new apparent magnitudes for 4 individual stars listed in tables

2.1 and 2.4 (marked with asterisks). These deblended photometric values are less reliable than the un-blended magnitudes. Typically, 2MASS photometric errors are on the order of 5% [84] and we would not trust our deblended photometry to any better than 20% [41].

2.2.3 Limiting Magnitudes

I was able to use equation 2.7 to estimate the limiting magnitude, that is what the magnitude of the dimmest stars in the ﬁeld should be. I did this by extrapolating the data from STARFIND of the ten dimmest stars in the images to equation 2.7.

Two of the ten stars were associated with the cluster (stars #72 and #75 in table

47 2.3, ﬁgure 2.1). From this extrapolation, I broadly estimate the dimmest stars to have magnitudes J ∼16.9, H ∼16.1 (about 1 magnitude deeper than 2MASS at 10

σ), and K ∼16.6 (about 2 magnitudes deeper than 2MASS at 10 σ)[41].

2.2.4 Color-Magnitude Diagram

I created a color-magnitude (CMD) diagram of cluster 107 (ﬁgure 2.7). The

CMD is the observer version of the HRD (section 1.2.1). Using NIR colors makes

figure 2.7 look decidedly different than the HRD (figure 1.3). The goal of this was to create a CMD that would correspond to the H− and K−band spectra used to classify the stars in tables 2.1 - 2.4. The photometry from attempting to bootstrap STARFIND magnitudes to 2MASS carried too much error to be of any use. Unfortunately, only 25 of the 130 stars in cluster 107 have 2MASS photometry, plus the four deblended stars. Because of this, the CMD and the color-color diagram in section 2.2.5 are sparsely populated. Spectral analysis of star #25 clearly types the star as an O5 dwarf [41], so I used the luminosity and spectral type of this star to constrain the cluster distance to 1750 pc in figure 2.7 (see the dotted lines plotted over the points). This also shows an extinction for cluster 107 to be Av ∼ 23 magnitudes. Note that star #23 is extremely reddened (far right side of figure 2.7).

2.2.5 Color-Color Diagram

I also created a color-color diagram (CCD) for the cluster (ﬁgure 2.8). Since the cluster stars form in the cloud of dust and cluster 107 is still very young, many

48 Figure 2.7: 2MASS Color-magnitude diagram of Cluster 107. The main sequence, based on magnitudes from Hanson et al. (1997) are shown for a distance of 2000 parsecs. Note the extremely red H − K color of Star #23, which we believe is a background ﬁeld star.

49 of the stars have circumstellar disks encasing them (e.g. stars #118 and #119, #25 and #31). The dust is heated by the star and radiates like a blackbody. Because of this, observations have an excess of infrared radiation, pulling stars towards the right of the CCD. Included in ﬁgure 2.8 is the theoretical MS curve (solid line) and the reddening bands which show the extinction in the visible band (dotted lines).

The stars that fall to the right of these curves are the infrared excess sources. One of these objects, star #119, shows very high extinction and a possible infrared excess.

This suggests that it may harbor a circumstellar disk, which can be seen in the false color image, ﬁgure 2.9 (section 2.2.7). Star #119 is likely a very young star on the

MS, possibly a mid-B star.

2.2.6 The Field Star #23

The brightest star in cluster 107 is star #23. We have identified it as a field star, and it is not associated with the cluster. In figures 2.8 and 2.7 star #23 is extremely reddened. Figure 2.2 will not tell us whether star #23 is a red giant field star or a massive supergiant. However the photometric data does show it is not associated with cluster 107 regardless of the luminosity class. If star #23 is indeed a super giant, then the apparent magnitude (see table 2.1) is much too dim to be in the cluster; it would need to be ∼5 magnitudes brighter. The magnitude and reddening puts star #23 many kiloparsecs beyond cluster 107. The radial velocity measurement in section 2.1.2 suggests that star #23 is most likely a low-mass, evolved red field giant. If star #23 is a red giant, the H − K color predicts that the

50 Figure 2.8: 2MASS Color-color Diagram of Cluster 107. Star #25 is an O5 dwarf, #17 is an early-B dwarf, and #63 is the massive YSO showing a B-spectrum in the short K−band with CO bandhead emission at 2.3 μm. Star #23 shows very strong CO bandheads in absorption and is likely a ﬁeld star. Stars #6 and #87 are plotted nearly on top of each other at the point [0.26, 1.05].

51 J−band magnitude should be very dim (J>17). The 2MASS cutoﬀ for the J−band is J=15.8 (see section 2.2.3). It is very likely that 2MASS is measuring 2 stars at this location: the blending this red ﬁeld giant and some other dim foreground star

[41].

2.2.7 False Color Image

The J−,H−,K−bands are in the NIR, and any image shown in true color would be dark to the human eye. We know that light is emitted in that spectrum by the simple fact that the light interacts with the InSb detector. The acquisition images are not only useful in the quantitative color-magnitude and color-color diagrams, but also in creating a qualitative false-color image. In ﬁgure 2.9, the morphology of the cluster is easily seen, with stellar characteristics such as the neb- ulosity surrounding star #119. The measured intensities that are listed in table 2.5 were layered together in Photoshop CS [2] using Maxwell’s three-color technique

[59]. That is, combining the colors red, green, and blue to make a full color image.

In this process, ∼0.75 μm is subtracted from the J−Band, ∼1 μm is subtracted from the H−band, and ∼1.5 μm is subtracted from the K−band.

2.3 Cluster Characteristics

The hottest star in the cluster, star #25, is readily classiﬁed as an 05 dwarf star [41]. This constrains the age of cluster 107 to ≤5 Myr. I used the spectral

52 Figure 2.9: Color composite (J,blue;H, green; K, red) of Cluster 107. These images were taken with the IRTF/SpeX instrument in June 2005. The ﬁeld of view is approximately 1 × 1.

53 Table 2.5: False Color Image Color Ranges.

J (Blue) H (Green) K (Red)S

1.16 - 1.33 (μm) 1.48 - 1.78 (μm) 1.95 - 2.30 (μm) Images were taken in these wavelength ranges [16] and used to create the false color image 2.9. type of star #25 to estimate the distance of the cluster to be 1750 pc (about 22% the distance to Sgr A∗) on the CMD. I was also able to show in the CMD and the

CCD that the extinction for cluster 107 is Av ∼ 23 magnitudes, which is what we have come to expect looking at this distance directly into the center of the Galaxy.

Finally, though I was not able to accurately calculate the radial velocity of cluster

107, I do have an idea for constraining it which I discuss in section 4.3.1.

54 Chapter 3

The Super Star Cluster Westerlund1

Westerlund 1 (hereafter called Wd1) is a super star cluster (SSC) with a mass

4 well over 10 M. It was discovered by Bengt Westerlund in 1961 [89]. Wd1 is located in the southern constellation Ara at R.A.(J2000.0) 16h47m04.0s Dec.

45◦5104.9”. Even though the cluster was known, it was long overlooked due to the interstellar extinction. The ISM blocks most of the visible light with an extinction of Av ≈ 11. Extinction blocks most of the stars from view in the visual bands. It was only the presence of several dozen very luminous evolved stars that showed a cluster in the ﬁrst place. Westerlund followed up with a later study, but still never went further red than the I-band (∼ 0.8μm) [90]. It was not until an infrared follow-up study by Clark and Negueruela [22] suggested Wd1 to be the most massive, most luminous cluster in the Local Group. This study identiﬁed more than a dozen Wolf-Rayet (WR) stars, which yielded estimates on both age (∼ 5Myr) and distance (∼ 5 kpc). However a later study into the main sequence (MS) stars of Wd1 show and age of 3.2 Myr and a distance of ∼ 3.55 kpc [15].

New spectra were obtained for 95 stars 31 June - 2 August 2004 at the Euro- pean Southern Observatory (ESO) Very Large Telescope (VLT) using the Infrared

Spectrometer and Array Camera (ISAAC) by Margaret Hanson. My role came with the analysis of the astrometry and the acquisition of photometry bootstrapped to

55 2MASS, listed in tables 3.1 - 3.3. The analysis of astrometric and photometric data is detailed in the two sections of this chapter. The locations of the 95 stars for which spectra were obtained are detailed in section 3.1. In section 3.2, I have included a cross-reference chart of notable stars in other studies with our stars. Also, I made a color-magnitude diagram and a color-color diagram for Wd1 in section 3.3.

3.1 Astrometry

I used the 2MASS coordinates again to calculate the image solution for Wd1, as I did with cluster 107. A K−band image was taken using ISAAC and was used in calculating the star locations for Wd1. The method I used is very similar to cluster

107, the only big diﬀerence being that I abandoned my program FORMAT.PRO and formatted the output manually in Microsoft Excel (2007). I again used the

IRAF software packages titled STARFIND [81], CCMAP [79], and CCTRAN [80].

I computed the plate solutions by using 25 stars in the ﬁeld of known celestial coordinates, matching them to the pixel coordinates on the ISAAC image. STARFIND approximated the PSF using a radial Gaussian function with σ = 0.84932 HWHM

(see section 2.1.1). I needed to define maximum and minimum photon counts to be rejected from the PSF fit. The PSF identified every star in the field, and I used the imaging program DS9 to individually pick out every star for which we had spectra

(tables 3.1 - 3.3). I formatted the output in Microsoft Excel (2007), keeping only the pixel-space positions.

Stars with known positions in 2MASS [30] were chosen to make a matched

56 Table 3.1: Stars as part of ISAAC spectroscopic study.

Star Bootstrap J, H, K α(2000) δ(2000) K SpectralType Wd1#1 10.442 , 9.921 , 9.691 16:46:59.05 -45:50:28.45 O9.5 Ib Wd1#2 12.441 , 11.374 , 11.264 16:46:59.39 -45:51:07.20 B star Wd1#3 9.336 , ... , ... 16:46:59.72 -45:50:51.30 O9.5Ia-B0.5Ia Wd1#4 11.519 , 10.793 , 10.699 16:47:00.44 -45:50:11.60 cool Wd1#5 10.704 , 10.218 , 10.125 16:47:00.63 -45:50:53.80 B star Wd1#6 11.525 , 10.872 , 10.663 16:47:00.76 -45:51:25.00 B star Wd1#7 10.793 , 10.056 , 9.783 16:47:00.79 -45:51:02.00 B star Wd1#8 13.481 , 12.031 , 11.729 16:47:00.91 -45:50:33.20 Cool Wd1#9 11.868 , 11.102 , 10.841 16:47:01.04 -45:49:49.00 B star Wd1#10 9.432 , 9.286 , 9.208 16:47:01.11 -45:51:13.60 B0.5Ia Wd1#11 13.265 , 12.925 , 12.056 16:47:01.12 -45:51:16.80 B star Wd1#12 10.280 , 9.647 , 9.406 16:47:01.18 -45:50:26.90 B star Wd1#13 11.274 , 10.386 , 10.291 16:47:01.41 -45:51:34.80 B star Wd1#14 ... , ... , ... 16:47:01.42 -45:50:37.40 LBV, F2Ia+, G0Ia+ Wd1#15 12.543 , 10.910 , 10.438 16:47:01.49 -45:50:18.60 cool Wd1#16 11.211 , 10.314 , 10.033 16:47:01.53 -45:50:55.80 B star Wd1#17 9.081 , ... , ... 16:47:01.56 -45:49:58.10 B1Ia Wd1#18 10.811 , 9.822 , 9.462 16:47:01.59 -45:51:45.30 WN7o, WN8 Wd1#19 12.823 , 11.976 , 11.741 16:47:01.69 -45:51:20.30 B star Wd1#20 12.836 , 12.028 , 11.753 16:47:01.83 -45:51:08.30 B star Wd1#21 11.301 , 10.753 , 10.389 16:47:01.83 -45:50:56.30 B star Wd1#22 11.441 , 10.614 , 10.434 16:47:01.96 -45:50:21.90 B star Wd1#23 12.936 , 12.596 , 11.977 16:47:02.02 -45:51:10.70 B star Wd1#24 9.881 , 9.148 , 9.169 16:47:02.16 -45:51:12.50 B1, OB binary/blend? Wd1#25 ... , ... , ... 16:47:02.23 -45:50:59.80 LBV, A5Ia+ Wd1#26 11.952 , 11.214 , 10.930 16:47:02.29 -45:51:15.00 B star Wd1#27 9.927 , 9.226 , 9.085 16:47:02.34 -45:50:57.30 B star Wd1#28 13.369 , 12.943 , 12.443 16:47:02.53 -45:50:43.70 B star Wd1#29 10.502 , 9.722 , 9.548 16:47:02.60 -45:51:17.80 B star Wd1#30 12.273 , 11.485 , 11.269 16:47:02.70 -45:50:25.50 B star Wd1#31 9.701 , 9.190 , 9.033 16:47:02.71 -45:50:57.10 O9-9.5Ia Wd1#32 10.343 , 9.622 , 9.242 16:47:02.87 -45:50:46.20 B4 Wd1#33 12.053 , 11.476 , 11.082 16:47:02.95 -45:50:22.60 B star Wd1#34 12.456 , 11.745 , 11.650 16:47:02.96 -45:50:44.00 B star Wd1#35 10.865 , 9.909 , 9.612 16:47:02.98 -45:51:25.70 B star

57 Table 3.2: Stars as part of ISAAC spectroscopic study.

Star Bootstrap J, H, K α(2000) δ(2000) K SpectralType Wd1#36 11.688 , 10.808 , 10.673 16:47:03.01 -45:50:47.00 B star Wd1#37 9.972 , 9.153 , 9.081 16:47:03.05 -45:50:23.90 O9.5Ia-B0.5Ia Wd1#38 12.022 , 10.940 , 10.807 16:47:03.06 -45:51:01.20 B star Wd1#39 12.618 , 11.538 , 11.492 16:47:03.22 -45:50:48.50 B star Wd1#40 13.098 , 12.211 , 12.169 16:47:03.27 -45:51:00.40 B star Wd1#41 13.371 , 12.356 , 12.459 16:47:03.34 -45:50:47.90 B star Wd1#42 11.776 , 11.081 , 10.810 16:47:03.39 -45:50:55.20 B star Wd1#43 10.813 , 9.913 , 9.599 16:47:03.52 -45:50:54.50 B star Wd1#44 10.695 , 9.775 , 9.646 16:47:03.52 -45:50:50.30 B star Wd1#45 10.925 , 9.973 , 9.914 16:47:03.62 -45:51:07.10 B star Wd1#46 10.848 , 9.855 , 9.635 16:47:03.66 -45:50:51.40 B star Wd1#47 10.018 , 9.035 , 8.950 16:47:03.73 -45:51:12.60 B star Wd1#48 10.220 , 9.400 , 9.403 16:47:03.76 -45:50:58.40 B star Wd1#49 11.318 , 10.568 , 10.365 16:47:03.76 -45:50:35.70 B star Wd1#50 11.030 , 10.013 , 9.901 16:47:03.80 -45:51:06.10 B star Wd1#51 10.351 , 9.268 , 9.008 16:47:03.81 -45:50:38.80 WN8o Wd1#52 13.005 , 11.906 , 11.656 16:47:03.83 -45:50:53.90 B star Wd1#53 9.707 , ... , ... 16:47:03.96 -45:51:37.70 WC9d, WC9 Wd1#54 13.351 , 12.451 , 12.209 16:47:04.00 -45:50:53.40 B star Wd1#55 10.015 , ... , 9.907 16:47:04.10 -45:50:39.40 Mid-O Ia Wd1#56 12.520 , 11.889 , 11.680 16:47:04.11 -45:51:43.30 B star Wd1#57 ... , ... , ... 16:47:04.20 -45:50:53.70 O9.5Ia-B0.5Ia Wd1#58 9.719 , 10.205 , 9.774 16:47:04.34 -45:51:10.10 B star Wd1#59 11.431 , 10.741 , 10.542 16:47:04.34 -45:50:36.40 B star Wd1#60 12.156 , 11.475 , 10.822 16:47:04.36 -45:51:02.30 B star Wd1#61 9.681 , 11.555 , 11.773 16:47:04.39 -45:50:47.20 B star Wd1#62 9.990 , 9.823 , 9.668 16:47:04.41 -45:50:39.90 O9.5Ia-B0.5Ia Wd1#63 12.686 , 11.862 , 11.469 16:47:04.52 -45:50:55.30 B star Wd1#64 11.366 , 10.459 , 10.219 16:47:04.56 -45:50:59.40 B star Wd1#65 9.493 , 11.421 , 9.856 16:47:04.66 -45:50:38.50 O9.5Ia-B0.5Ia Wd1#66 11.207 , 10.331 , 10.029 16:47:04.71 -45:51:01.00 B star Wd1#67 13.186 , 11.222 , 12.010 16:47:04.72 -45:50:53.00 B star Wd1#68 ... , ... , ... 16:47:04.86 -45:50:59.30 O9.5Ia-B0.5Ia Wd1#69 ... , ... , ... 16:47:04.96 -45:50:58.50 B star Wd1#70 12.319 , 11.259 , 10.787 16:47:05.12 -45:51:06.70 B star

58 Table 3.3: Stars as part of ISAAC spectroscopic study.

Star Bootstrap J, H, K α(2000) δ(2000) K SpectralType Wd1#71 9.577 , 9.851 , 9.804 16:47:05.15 -45:50:41.40 Mid-O Ia Wd1#72 10.470 , 9.695 , 9.665 16:47:05.37 -45:51:04.80 WN7o, Wd1#73 11.944 , 11.257 , 11.284 16:47:05.39 -45:50:43.40 B star Wd1#74 13.878 , 12.760 , 12.338 16:47:05.56 -45:51:07.70 B star Wd1#75 12.817 , 11.915 , 11.400 16:47:05.62 -45:51:02.90 B star Wd1#76 10.164 , 9.937 , 10.095 16:47:05.64 -45:51:05.20 B star Wd1#77 ... , 13.145 , 12.058 16:47:05.66 -45:51:10.70 cool Wd1#78 13.778 , 13.273 , 13.095 16:47:05.66 -45:50:43.30 B star Wd1#79 9.665 , ... , ... 16:47:05.70 -45:50:50.50 B star Wd1#80 10.947 , 10.141 , 10.015 16:47:05.85 -45:51:02.70 B star Wd1#81 10.280 , 9.602 , 9.505 16:47:06.01 -45:50:47.50 B star Wd1#82 10.059 , 9.947 , 9.954 16:47:06.06 -45:52:08.60 WC 9 Wd1#83 12.898 , 12.554 , 12.311 16:47:06.16 -45:50:45.40 B star Wd1#84 10.402 , 9.745 , 9.590 16:47:06.24 -45:50:49.30 O9 Iab Wd1#85 10.093 , ... , ... 16:47:06.25 -45:51:03.90 B star Wd1#86 13.702 , 13.246 , 12.348 16:47:06.38 -45:51:17.30 B star Wd1#87 13.631 , 13.269 , 12.223 16:47:06.49 -45:51:18.30 B star Wd1#88 11.389 , 10.670 , 10.430 16:47:06.55 -45:50:45.90 B star Wd1#89 13.561 , 12.703 , 12.266 16:47:07.30 -45:50:54.10 B star Wd1#90 ... , ... , ... 16:47:07.50 -45:52:29.10 LBV A2 Iab Wd1#91 10.183 , 9.533 , 9.550 16:47:07.82 -45:51:48.00 B star Wd1#92 11.261 , 9.623 , 9.803 16:47:08.61 -45:50:38.80 B star Wd1#93 11.038 , 10.427 , 10.060 16:47:09.20 -45:50:48.40 B star Wd1#94 11.268 , 10.576 , 10.411 16:47:09.62 -45:50:34.10 B star Wd1#95 12.335 , 11.359 , 11.094 16:47:09.82 -45:51:50.50 B star

59 pixel and celestial coordinate (J2000.0) list. The CCMAP package then computed the transformation equation that CCTRAN would extrapolate the star locations for the entire ﬁeld. CCMAP worked in equatorial coordinates, and the equations were of a quadratic polynomial form found in equation 2.1. I calculated 2.1 both with and without cross terms and found the same locations for each solution. The complete and accurate list of the astrometry for Wd1 is found in tables 3.1 - 3.3.

Finally, a star ﬁnding chart was created from the Ks-band image of the ﬁeld

(see figure 3.1). The images was taken with the ISAAC. The array images are 4, sixty-second integrations and are approximately 2.5’×2.5’. Figure 3.1 was created using the imaging program Photoshop CS [2], where I altered the contrast to provide a qualitative tool in locating the relative position of the stars. Like the finding chart for Cluster 107 (figure 2.1), figure 3.1 is not intended for photometric study.

3.2 Westerlund 1 Cross-Reference Chart

Being the largest SSC in the Local Group, Wd1 is a very popular cluster to study. Because of this, I have created a chart of cross-references of the individual stars for the purposes of organization. I meticulously cross-referenced our stars

(tables 3.1 - 3.3) and Westerlund’s 1987 followup paper (also called W87, [90]) with ten diﬀerent NIR studies over the last six years: Clark et al. 2005 [23], Negueruela and Clark 2005 [68], Crowther et al. 2006 [28], Bonanos 2008 [11], Brandner et al.

2008 [15], Clark et al. 2008 [24], Negueruela et al. 2008 [69], Ritchie et al. 2009

[77], Clark et al. 2010 [25], and Negueruela and Clark 2010 [70].

60 Figure 3.1: ISAAC Ks-band image of Wd1 with star numbers from tables 3.1 - 3.3 identiﬁed. The image was taken with the VLT/ISAAC instrument in August 2004. The ﬁeld of view is approximately 1 × 2.5.

61 Table 3.4: Cross References.

Wd1 W87 RA Dec Other Sources 56b 16:16:58.85 -45:51:45.8 56b[70] 86 16:46:57.15 -45:50:09.9 86[70] 232 16:46:57.71 -45:53:20.1 232[70] 373 16:46:57.71 -45:53:20.0 373[77]373[70] 228b 16:46:58.05 -45:53:01.0 228b[70] 55 16:46:58.40 -45:51:31.2 55[23]55[11]55[77]55[25]55[70] 56 16:46:58.93 -45:51:48.8 56[23] 1 84 16:46:59.05 -45:50:28.45 36[24]84[77]84[70] 2 16:46:59.39 -45:51:07.20 3 2a 16:46:59.72 -45:50:51.30 2a[23]2a[69]2a[77]2a[70] 4 82 16:47:00.44 -45:50:11.60 5 3 16:47:00.63 -45:50:53.80 6 16:47:00.76 -45:51:25.00 [11] 10 21 16:47:01.11 -45:51:13.60 21[11]21[69]21[77]21[25]21[70] 12 50b 16:47:01.18 -45:50:26.90 50b[70] 57a 16:47:01.35 -45:51:45.6 57a[23]57a[11]57a[69]57a[77]57a[25]57a[70] 13 16:47:01.41 -45:51:34.80 [11] 14 4 16:47:01.42 -45:50:37.40 4[23]4[11]4[25] 234 16:47:01.44 -45:52:35.0 234[77] 17 78 16:47:01.56 -45:49:58.10 78[69]78[77]78[25]78[70] 18 16:47:01.59 -45:51:45.30 P[68] 52 16:47:01.85 -45:51:29.4 52[69]52[70] 49 16:47:01.90 -45:50:31.5 49[70] 24 24 16:47:02.16 -45:51:12.50 24[23]10[24]24[69]24[25]24[70] 11 16:47:02.23 -45:50:47.0 11[23]11[11]11[69]11[25]11[70] 25 12a 16:47:02.23 -45:50:59.80 12a[23]12a[25] 61a 16:47:02.29 -45:51:41.6 61a[23]61a[11]61 a[69]61a[25]61a[70] 62a 16:47:02.5 -45:51:38.0 W62a[24]62a[70] 61b 16:47:02.56 -45:51:41.6 61b[23]61b[25]61b[70] 23a 16:47:02.57 -45:51:08.7 23a[23]23a[11]23a[77]23a[25]23a[70] Table of our stars shown along side Westerlund 1987 (W87) [90] and other notable papers on infrared studies on Westerlund 1.

62 Table 3.5: Cross References.

Wd1 W87 RA Dec Other Sources 29 47 16:47:02.60 -45:51:17.80 13[24] 31 41 16:47:02.71 -45:50:57.10 41[23]3[24]41[25]41[70] 32 38 16:47:02.87 -45:50:46.20 7[24]38[70] 33 6b 16:47:02.95 -45:50:22.60 6b[70] 34 16:47:02.96 -45:50:44.00 9[24] 5 16:47:02.97 -45:50:19.50 S[68]5[23]S[28]5, WR 77f (S)[11]5[25]5[70] 37 6a 16:47:03.05 -45:50:23.90 6[23]21[24]6[11]6a[77]6a[25]6a[70] 54 16:47:03.06 -45:51:30.5 54[70] 237 16:47:03.09 -45:52:18.8 237[25] 42a 16:47:03.25 -45:50:52.1 42a[23]42a[11]42a[77]42a[25]42a[70] 10 16:47:03.32 -45:50:34.7 10[23]10[11]10[25]10[70] 63a 16:47:03.39 -45:51:57.7 63a[70] 43b 16:47:03.52 -45:50:56.5 43b[70] 43a 16:47:03.54 -45:50:57.3 43a[23]43a[11]43a[69]43a[77]43a[25]43a[70] 46b 16:47:03.61 -45:51:20.0 46b[70] 7 16:47:03.62 -45:50:14.2 7[23]7[11]7[25]7[70] 32 16:47:03.67 -45:50:43.5 32[23]32[25] 48 43c 16:47:03.76 -45:50:58.40 1[24]43c[70] 31 16:47:03.78 -45:50:40.4 31[70] 51 16:47:03.81 -45:50:38.80 V[28] 64 16:47:03.89 -45:51:46.3 64[70] 46a 16:47:03.93 -45:51:19.6 46a[69]46a[70] 53 16:47:03.96 -45:51:37.70 M[68]M[28]66, WR77i (M)[11] 55 30a 16:47:04.10 -45:50:39.40 30[23]30a[77]30a[25]30[70] 33 16:47:04.12 -45:50:48.3 33[23]33[11]33[25]33[70] 60 16:47:04.13 -45:51:52.1 60[23]60[15]60[11]60[77]60[25]60[70] 9 16:47:04.14 -45:50:31.1 9[25] 57 35 16:47:04.20 -45:50:53.70 35[23]35[25]35[70] 44 16:47:04.20 -45:51:06.9 L[68]44[23]L[28]44, WR 77k (L)[11]44[25] 61 34 16:47:04.39 -45:50:47.20 34[70] Table of our stars shown along side Westerlund 1987 (W87) [90] and other notable papers on infrared studies on Westerlund 1.

63 Table 3.6: Cross References.

Wd1 W87 RA Dec Other Sources 62 29 16:47:04.41 -45:50:39.90 29[23]29[15]29[25]29[70] 238 16:47:04.41 -45:52:27.6 238[23]238[11]238[25]238[70] 65 28 16:47:04.66 -45:50:38.50 28[23]28[11]28[69]28[25]28[70] 20 16:47:04.7 -45:51:23.8 20[25] 8a 16:47:04.79 -45:50:24.9 8a[23]8a[11]8a[25] 68 19 16:47:04.86 -45:50:59.30 19[23]19[69]19[25]19[70] 8b 16:47:04.9 -45:50:26.5 8b[69]8b[77]8b[25]8b[70] 71 27 16:47:05.15 -45:50:41.40 27[70] 239 16:47:05.21 -45:52:25.0 239[77]239[25] 72 16:47:05.37 -45:51:04.80 B[68]B[23]8[24]WR 77o (B)[11] 26 16:47:05.4 -45:50:36.5 26[25] 79 18 16:47:05.70 -45:50:50.50 18[11]18[69]18[70] 25 16:47:05.78 -45:50:33.3 25[70] 14a 16:47:05.94 -45:50:23.3 14a[23] 81 37 16:47:06.01 -45:50:47.50 37[70] 82 241 16:47:06.06 -45:52:08.60 E[68]E[23]E[28]42[24]241, WR77p[11]241[25] 14c 16:47:06.07 -45:50:22.60 R[68]R[28]14c, WR 77q (R)[11] 84 17 16:47:06.24 -45:50:49.30 17[24]17[77]17[70] 85 16:47:06.25 -45:51:03.90 [11] 265 16:47:06.26 -45:49:23.7 265[23]265[77]265[25] 13 16:47:06.45 -45:50:26.0 13[23]13[11]13[77]13[25]13[70] 88 8 16:47:06.55 -45:50:45.90 16a 16:47:06.61 -45:50:42.1 16a[23]16a[11]16a[25] 15 16:47:06.63 -45:50:29.7 15[23]15[15]15[11]15[77]15[25]15[70] 74 16:47:07.08 -45:50:12.9 W74[24]74[11]74[69]74[25]74[70] 90 243 16:47:07.50 -45:52:29.10 243[23]243[11]243[77] 91 16:47:07.82 -45:51:48.00 41[24][11] 72 16:47:08.32 -45:50:45.50 A[68]A[28]72, WR 77sc (A)[11] 71 16:47:08.44 -45:50:49.3 71[23]71[11]71[69]71[77]71[25]71[70] 75 16:47:08.93 -45:49:58.4 75[25] 70 16:47:09.36 -45:50:49.6 70[23]70[11]70[69]70[25]70[70] Table of our stars shown along side Westerlund 1987 (W87) [90] and other notable papers on infrared studies on Westerlund 1.

64 3.3 Photometry

I used public data (Program ID: 67.C-0514) obtained with the Son of ISAAC

(SOFI) instrument, at the ESO New Technology Telescope (NTT). This data was published by Brandner in 2008 [15]. I bootstrapped photometry to 2MASS magnitudes of similar un-blended stars. Because there was a greater mapping of stars between 2MASS and the SOFI data, I was able to get these to ∼0.2 magnitude error.

3.3.1 DAOPHOT Photometry

The photometric values of the stars in Wd1 were calculated from the SOFI

67.C-0514 data (hereafter called SOFI67). Again I used STARFIND to get the pixel coordinate list from SOFI67, but I did not use the magnitudes from the output. I found that the PSF for STARFIND is not custom to the image as it needs to be.

The shape of the PSF need to be unique to each image based on the FWHM of star profiles. STARFIND uses the assumption that σ = 0.84932 × HWHM. Instead, I used an IRAF software package called DAOPHOT [86]. Each image in SOFI67 has a unique PSF, because the FWHM is different for each image. In order to minimize the inconsistencies across the different bands, each star is fitted with the unique shape of the PSF for their image. Only the height of the PSF changes, which is calculated as the magnitude of each star. If an object does not match the PSF very well, such as a ”blended” star or a star with a bright circumstellar disk, it is marked by DAOPHOT with a high error. I used the Mathematica program I wrote

65 for cluster 107 (equation 2.7) to bootstrap to 2MASS. I was able to use 25 stars from tables 3.1 - 3.3 plus an additional 7 intermediate mass stars that matched with

2MASS to calculate the SOFI67 photometry. Based on the comparison between

2MASS and the 32 calculated magnitudes from the matching stars I estimate an uncertainty of ∼0.2 magnitude.

3.3.2 Limiting Magnitudes

As with cluster 107, I used equation 2.7 to estimate the limiting magnitude of the stars in ﬁgure 3.1. Extrapolating the data from DAOPHOT of the ten dimmest stars, I broadly estimate the dimmest stars to have magnitude K ∼ 17 magnitude

(about 2 magnitudes deeper than 2MASS at 10 σ).

3.3.3 Color-Magnitude Diagram

In ﬁgure 3.2, I present a color-magnitude diagram (CMD) of the stars for which we have spectra (listed in tables 3.1 - 3.3), plotted in green, on top of stars published by Brandner 2008 [15], plotted in blue. Figure 3.3 shows the CMD for just our stars, with photometric values from Brandner 2008 [15]. The curves of the giant branch and MS can be seen in ﬁgure 3.3. From a survey of the OB supergiants in Wd1 [70], I can roughly estimate a distance of 3500 pc. Figure 3.3 also shows an extinction for Wd1 Av ≈ 11.

66 ColorMagnitude Diagram

K 13

0 1 2 3 4 5 6 JK

Figure 3.2: Color-magnitude diagram of Wd1 [15]. The green points correspond to stars for which we have spectra.

67 ColorMagnitude Diagram

17 58 82 79 8 Distance3.5 kpc 61 10

317127 47 85 76 89 05 51 552457 91 9 37 72 06 62 48 81 18 8432 07 29 35 4446 08 80 1 43 7 45 93 5066 10 09 16 6413 5 4988 94 21

K 15 5922 36 70 12 426 B0 4 38 9 11 26 95 73 60 23 33 AV 5 39 5619 3034 63 B1 75 20 52 8 AV 10 23 40 12 67 87 77 B2 83 86 41 11 A 15 54 V B3 28 74 AV 20 13 B5 78 AV 25 0 1 2 3 4 JK

Figure 3.3: Color-magnitude diagram of Wd1 stars for which we have spectra. Star numbers are from tables 3.1 - 3.3, and photometric values are from Brandner 2008 [15].

68 3.3.4 Color-Color Diagram

Figure 3.4 shows a color-color diagram (CCD) of select stars from tables 3.1

- 3.3. Many of the stars lie on the MS, so we are able to ﬁnd the extinction to be

Av ∼ 11. Lower mass stars that are still forming were too dim to be picked up by the study.

3.3.5 False Color Image

I created a false color image of Wd1 from the SOFI67 images. The 1024×1024

HgCdTe chip that SOFI gives four times the area that cluster 107 had. I used this to produce an image with similar resolution but large field of view. The field of view in figure 3.5 approximately 2.5’×2.5’. I used the same three-color technique

[59] as cluster107 and used the same version of Photoshop [2] to make the false color image. In this three-color process, ∼0.75 μm is subtracted from the J−Band, ∼1

μm is subtracted from the h−band, and ∼1.5 μm is subtracted from the K−band.

3.4 Cluster Characteristics

The majority of the observed MS stars are mid-B stars. No O dwarfs are found, though the luminosity compared to the B stars shows that they should be if they are present. This suggests an age for Wd1 to ∼5 Myr. I used the spectral type of the B stars to estimate the distance of the cluster to be 3.5 kpc on the CMD, which matches with some works [15] but is in conﬂict with others [22]. I was also able to show in the CMD and the CCD that the extinction for Wd1 is Av ∼ 11.

69 ColorColor Diagram

1.5 8 Av 15

74 52 39 38 51 2 70 41 50 46 18 1.0 47 95 45 35 44 6454 43 75 40 13 16 36 66 Av 10 19 89 48 22 63 37 80 20 29 30 72 9 49 24 4 726 34 88 32 2794 42 73 81 59 60 91 84 6 56 12

H 93

J 33 21 1 3178 5 0.5 86 28

Av 5 83 23 11

62 10 82

0.0 Infrared Excess Sources

71 0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 HK

Figure 3.4: Color-color Diagram of Wd1 stars for which I bootstrapped J, H, K photometry to 2MASS (see section 3.3.1). The color-color relationships of MS stars and the giant branch are marked with the dotted curves. Most of the stars in the study are B dwarfs.

70 Figure 3.5: Color composite (J,blue;H, green; Ks, red) of Wd1. These images were taken with the NTT/SOFI instrument in June 2001. The ﬁeld of view is approximately 2.5 × 2.5.

71 Chapter 4

Massive Cluster Research

4.1 Importance of Massive Cluster Research

The structure of the inner Milky Way is not very well known. Just like the problem of determining the structure of the physics building from the conﬁnement of a single classroom, astronomers try to piece a picture of our own Galaxy. In this analogy the walls that block the view of the rest of the building is like the dust that causes the extinction in the Milky Way. The windows that would allow us to see other buildings are like the view that allows astronomer to see other galaxies, such as the spiral Pinwheel Galaxy in ﬁgure 4.1. The Pinwheel Galaxy is a face-on spiral galaxy that allows us to fully see the spiral structure. It is located about 103 times farther away than Sgr A∗. Each point on the spiral arms corresponds to a star cluster. Figure 4.1 shows us just how important the distribution of stellar clusters is to the shape of the Galaxy.

We can not see this kind of angle of our own Galaxy. Astronomers are stuck on Earth for the time being. We would be able to tell immediately the location of the Milky Way’s spiral arms if we could travel outside our Galaxy or somehow communicate with some alien graduate student including a ﬁgure of the Milky Way in his thesis on the disk clusters of the Pinwheel Galaxy. We are limited to observing the Milky Way from within the Galaxy.

72 Figure 4.1: The Pinwheel Galaxy is a face-on spiral galaxy that allows us to fully see the spiral structure. Credit: NASA, ESA, K. Kuntz (JHU), F. Bresolin (Uni- versity of Hawaii), J. Trauger (Jet Propulsion Lab), J. Mould (NOAO), Y.-H. Chu (University of Illinois, Urbana), and STScI [67].

73 We look to the massive clusters to determine the shape and dynamics of the

Milky Way. Figure 4.2 is an artist’s conception of the Galaxy taken from the

GLIMPSE survey [62]. It needs to be an artist’s concept image, because we cannot see through to the other side of the Galactic center. We can barely see halfway into our side of the Galactic Center because of the extinction. Because of this, we struggle with understanding even the most basic structural aspects of our Galaxy.

Mapping out the young massive clusters of our Milky Way is critical to providing this insight into our own Galaxy.

4.2 Future of Massive Cluster Research

We need to identify more clusters in the Milky Way, especially towards the center, to determine the structure and dynamics of the spirals arms. A promising survey is the VISTA Variables in The Via Lactea (VVV), which will cover ∼ 109 point sources, including 33 known GC and ∼350 OC. The ﬁnal products will be a deep IR atlas in the Z, Y, J, H, KS bands [91]. As you go deeper into the center, the mounting extinction and crowding eﬀects become problematic. This means moving to slightly longer wavelengths, such as the GLIMPSE survey which works in photometric bands centered at 3.6, 4.5, 5.8, and 8.0 μm [61]. This allows astronomers to observe deeper into the Milky Way without substantial loss in resolution.

Another development that needs to be bigger and better is the algorithms used in identifying stellar clusters. An algorithm which does just that [63] has been very successful in identifying these clusters. A new algorithm is improving upon

74 Figure 4.2: Credit: NASA/JPL-Caltech/R. Hurt (SSC-Caltech) [62].

75 creating a census of the massive star cluster population in the Milky Way [49]. The development of new and faster algorithms to ﬁnd massive star clusters is the biggest challenge. The technology exists for ﬁnding them, but the vast amount of space proves to be a substantial task. Creating an automated programs will be essential to completing the catalogs of massive clusters. This will ultimately yield important information on the ages of the populations and the dynamics of each cluster. And by characterizing them, we grow ever closer to providing a more clear picture of the

Milky Way.

4.3 Improvements on Massive Cluster Research

Some of the methods I did in the analysis for results of this thesis could be improved upon. A radial velocity for cluster 107 may still yet be determined from the existing data, though obviously not from star #23. The large uncertainties for the photometry of cluster 107 and for Wd1 (though the uncertainties are smaller than cluster 107) need to be minimized by better calibration of the output from

DAOPHOT.

4.3.1 The Radial Velocity of Cluster 107

The radial velocity could not be determined from star #23, because it was later shown to be not associated with cluster 107. When the noise in the shorter wavelengths was ignored, then the uncertainty could be minimized. It would have to be ignored for the spectra of other stars as well. The problem comes from the

76 uncertainty that arises from the spread of the spectral emission of features in other stars. However, an opportunity arises with emission spectra found in the extremely noisy (so noisy the stars could not be identiﬁed) spectra from stars #119 and #39

(figure 4.3). The stars have an infrared excess due to circumstellar disks that sur- round the star, which can be seen in figure 2.9. The spectra does, however, carry a nice little feature to help determine the radial velocity. There is a very narrow emission at Br γ (read Brackett gamma) due to extended nebular emission, which is definitely in the cluster. Preliminary calculations on this feature constrain the

∼ ∼ km radial velocity to be between -5 and -75 s . And its blueshifted! The expected radial velocity is within this range. A follow-up method described in section 2.1.2 will likely give an accurate calculation for the radial velocity of cluster 107.

4.3.2 Better Calibrated Photometry

The instrumental photometry computed by DAOPHOT (the method used for

Wd1) was certainly better than using STARFIND (the method used for cluster 107), but it still carries too much uncertainty. It needs to be better calibrated to accurately represent the star magnitudes. DAOPHOT computes the stellar magnitudes to an arbitrary zero point and the magnitudes are oﬀset. The method of bootstrapping photometry to 2MASS is not good enough. Color cross terms need to be solved; the calculated photometry is dependent on accurate photometry of deblended stars in

2MASS. It can be a mess. Standard stars need to be imaged when the main data set images are taken at the telescope. This will solve the problem of those pesky

77 Figure 4.3: Signal-to-noise was too low to obtain a clear spectral type for these stars. However, none of these stars show bands of CO, only possible Brackett Hydrogen transitions. We do not believe they are ﬁeld red giants and are likely lower mass, mid-B, cluster members. The narrow emission at Br γ is due to extended nebular emission (and also seen in the color composite given in ﬁgure 2.9.

78 color cross terms, as the same instrument is used to take the data.

79 Bibliography

[1] Adamson, A., 2004, Atmospheric Transmission,Web, ,5 October 2004.

[2] Adobe Systems Incorporated, 2003, Photoshop (CS), Software, San Jose, CA.

[3] Allen, C.W., 2000, Astrophysical Quantities (4 ed.), London, UK: Athlone Press Ltd.

[4] Ashman, K.M., Zepf, S.E., 1992, The formation of globular clusters in merging and interacting galaxies, Astrophysical Journal, 384, 50.

[5] Benjamin, Robert A., Churchwell, E., Babler, Brian L., Bania, T.M., Clemens, Dan P., Cohen, M., Dickey, J.M., Indebetouw, R., Jackson, J.M., Kobulnicky, H.A., Lazarian, A., Marston, A.P., Mathis, J.S., Meade, M.R., Seager, S., Stolovy, S.R., Watson, C., Whitney, B.A., Wolff, M.J., Wolfire, M.G., 2003, GLIMPSE. I. An SIRTF Legacy Project to Map the Inner Galaxy, The Publi- cations of the Astronomical Society of the Pacific, 115, 953.

[6] Bennett, J., Donahue, M., Schneider, N., Voit, M., 2004, TheCosmicPerspec- tive (3 ed.), San Francisco, CA: Pearson Education, Inc.

[7] Berry, R., Burnell, J., 2000, The handbook of astronomical image processing, Richmond, VA: Willmann-Bell.

[8] Bica, E., Dutra, C.M., Soares, J., Barbuy, B., 2003, New Infrared Star Clus- ters in the Northern and Equatorial Milky Way with 2MASS, Astronomy and Astrophysics, 404, 223.

[9] Binney, J., Merriﬁeld, M., 1998, Galactic Astronomy, Princeton, NJ : Princeton University Press.

[10] Bohren, C.F., Huﬀman, D.R., 1983, Absorption and Scattering of Light by Small Particles, New York, NY: John Wiley and Sons, Inc.

[11] Bonanos, A.Z., 2008, Variability of Young Massive Stars in the Galactic Super Star Cluster Westerlund 1, The Astronomical Journal, 133, 2696.

[12] Borissova, J., Pessev, P., Ivanov, V.D., Saviane, I., Kurtev, R., Ivanov, G.R., 2003, Discovery of new Milky Way star cluster candidates in the 2MASS Point

80 Source Catalog. II. Physical properties of the star cluster CC 01., Astronomy and Astrophysics, 411, 83.

[13] Borissova, J., Ivanov, V.D., Minniti, D., Geisler, D., Stephens, A.W., 2005 Discovery of new Milky Way star cluster candidates in the 2MASS point source catalog. III. Follow-up observations of cluster candidates in the Galactic Center region, Astronomy and Astrophysics, 435, 95.

[14] Bose, J.C., 1904, U.S. Patent 755840.

[15] Brandner, W., Clark, J.S., Stolte, A., Waters, R., Negueruela, I., Goodwin, S.P., 2008, Intermediate to low-mass stellar content of Westerlund 1, Astron- omy and Astrophysics, 478, 137.

[16] Bus, B., 1999, NSFCAM,Web, , 16 September 1999.

[17] Cabaong, C., 2007, About IRTF,Web, , 18 May 2010.

[18] Carney, B.W., Harris, W.E., 2000, Star Clusters (Lecture Notes 1998. Swiss Society for Astrophysics and Astronomy.), Springer, New York, NY.

[19] Carpenter, J.M., 2001 Color Transformations for the 2MASS Second Incremen- tal Data Release, The Astronomical Journal, 121, 2851.

[20] Carroll, B.W., Ostlie, D.A., 1996, An Introduction to Modern Astrophysics, New York, NY: Addison-Wesley Publishing Company, Inc.

[21] Centre de Donnes astronomiques de Strasbourg (CDS), 2001, SIMBAD Astro- nomical Database,Web,,15November 2006.

[22] Clark, J.S., Negueruela, I., 2002, A hidden population of Wolf-Rayet stars in the massive galactic cluster Westerlund 1, Astronomy and Astrophysics, 396, L25.

[23] Clark, J.S., Negueruela, I., Crowther, P.A., Goodwin, S.P., 2005, On the massive stellar population of the super star cluster Westerlund 1, Astronomy and Astrophysics, 434, 949.

[24] Clark, J.S., Muno, M.P., Negueruela, I., Dougherty, S.M., Crowther, P.A., Goodwin, S.P., de Grijs, R., 2008, Unveiling the X-ray point source population

81 of the Young Massive Cluster Westerlund 1, Astronomy and Astrophysics, 477, 147.

[25] Clark, J.S., Ritchie, B.W., Negueruela, I., 2010, A serendioitous survey for variability amongst the massive stellar population of Westerlund 1, Astronomy and Astrophysics, 514, A87.

[26] Clemens, D.P., 1985, Massachusetts-Stony Brook Galactic plane CO survey - The Galactic disk rotation curve, Astrophysical Journal, 295, 422.

[27] Comins, N.F., 2006, Discovering the Essential Universe, New York, NY: W.H. Freeman and Company.

[28] Crowther, P.A., Hadﬁeld, L.J., Clark, J.S., Negueruela, I., Vacca, W.D., 2006, A census of the Wolf-Rayet content in Westerlund 1 from near-infrared imaging and spectroscopy, Monthly Notices of the Royal Astronomical Society, 372, 1407.

[29] Cushing, M.C., Rayner, J.T., Vacca, W.D., 2005, An Infrared Spectroscopic Sequence of M, L, and T Dwarfs, The Astrophysical Journal, 623, 1115.

[30] Cutri, R.M., Skrutskie, M.F., van Dyk, S., Beichman, C.A., Carpenter, J.M., Chester,T.,Cambresy,L.,Evans,T.,Fowler,J.,Gizis,J.,Howard,E.,Huchra, J., Jarrett, T., Kopan, E.L., Kirkpatrick, J.D., Light, R.M., Marsh, K.A., Mc- Callon, H., Schneider, S., Stiening, R., Sykes, M., Weinberg, M., Wheaton, W.A., Wheelock, S., Zacarias, N., 2003, 2MASS All Sky Catalog of point sources, CDS/ADC Collection of Electronic Catalogues, 2246, 0.

[31] Dutra, C.M., Bica, E., 2000, New star clusters projected close to the Galactic Centre, Astronomy and Astrophysics, 359, L9.

[32] Dutra, C.M., Bica, E., 2001, New infrared star clusters and candidates in the Galaxy detected with 2MASS, Astronomy and Astrophysics, 376, 434.

[33] Dutra, C.M., Bica, E., Soares, J., Barbuy, B., 2003, New infrared star clusters in the southern Milky Way with 2MASS, Astronomy and Astrophysics, 400, 533.

[34] The Engineering Toolbox, 2005, Particle Sizes,Web, , 2005.

[35] European Southern Observatory, 1998, The Very Large Telescope,Web , 24 August 2010.

82 [36] European Southern Observatory, 2003, La Silla Observatory Instrumentation, Web , 14 July 2008.

[37] Figer, D.F., Kim, S.S., Morris, M., Serabyn, E., Rich, R. M., McLean, I.S., 1999, Hubble Space Telescope/NICMOS Observations of Massive Stellar Clusters near the Galactic Center, The Astrophysical Journal, 525, 750.

[38] Froebrich, D., Scholz, A., Raftery, C.L., 2007, A systematic survey for infrared star clusters with b < 20◦ using 2MASS, Monthly Notices of the Royal As- tronomical Society, 374, 399.

[39] Gallagher, J. S.; Grebel, E. K., 2002, Extragalactic Star Clusters: Speculations on the Future, IAU Symposium 207, Pucon, Chile 3/2001. Edited: Geisler, D., Grebel, E.K., Minniti, D., San Francisco: Astronomical Society of the Paciﬁc, 2002, 745.

[40] Glass, I.S., 1999, Handbook of Infrared Astronomy, Cambridge, UK: Cambridge University Press.

[41] Hanson, M.M., Bubnick, B.F., 2008, Near-Infrared Spectroscopy of Candidate Members of the Galactic Cluster [BDS2003] 107, Publications of the Astronom- ical Society of the Paciﬁc, 120, 150.

[42] Hall, D.N.B., Aikens, R.S., Joyce, R., McCurnin, T.W., 1975, Johnson Noise Limited Operation of Photovoltaic InSb Detectors, Applied Optics, 14, 450.

[43] Harris, W.E., Pudritz, R.E., 1994, Supergiant molecular clouds and the formation of globular cluster systems, The Astrophysical Journal, 429, 177.

[44] Harris, W.E., 1996, A Catalog of Parameters for Globular Clusters in the Milky Way, Astronomical Journal, 112, 1487.

[45] Herschel,W., 1800, Experiments on the refrangibility of the Invisible Rays of the Sun, Philosphical Transactions of the Royal Society of London, 90, 255.

[46] Herschel,W., 1789, Catalogue of a Second Thousand of New Nebulae and Clus- ters of Stars; With a Few Introductory Remarks on the Construction of the Heavens, Philosophical Transactions of the Royal Society of London, 79, 212.

[47] van de Hulst, H.C., 1957, Light Scattering by Small Particles, New York, NY: John Wiley and Sons. (SSC-Caltech)

83 [48] Ivanov, V.D., Borissova, J., Pessev, P., Ivanov, G.R., Kurtev, R., 2002, Dis- covery of new Milky Way star clusters candidates in the 2MASS point source catalog, Astronomy and Astrophysics, 394, L1.

[49] Ivanov, V.D., Messineo, M., Zhu, Q., Figer, D., Borissova, J., Kurtev, R., Ivanov, G.R., 2010, Infrared surveys of Galactic star clusters, Proceedings of the International Astronomical Union, 266, 203.

[50] Jackson, J.D., 1999, Absorption and Scattering of Light by Small Particles (3 ed., Intl. Version), New Delhi, IN: John Wiley and Sons (Asia), Pte. Ltd.

[51] Johnson, H.L., 1962, Infrared Stellar Photometry, Astrophysical Journal, 135, 69.

[52] Koornneef, J., 1983, Near-infrared photometry. II - Intrinsic colours and the absolute calibration from one to ﬁve micron, Astronomy and Astrophysics, 128, no.1,84.

[53] Kr¨ugel, E., 2003, The Physics of Interstellar Dust, Bristol, UK: The Institute of Physics.

[54] Kutzscher, E.W., 1973, Review on Detectors of Infrared Radiation, Electro- Optical Systems Design, 5,30.

[55] Lada, C.J., Lada, E.A., 2003, Embedded Clusters in Molecular Clouds, Annual Review of Astronomy and Astrophysics, 41, 57.

[56] Lada, E.A., Depoy, D.L., Evans, N.J., Gatley, I., 1991, A 2.2 micron survey in the L1630 molecular cloud, Astrophysical Journal, 371, 171.

[57] Lide,D.R., 1998, Handbook of Chemistry and Physics(87 ed.), Boca Raton, FL: CRC Press, 4-61.

[58] Low, F.J., 1961, Low Temperature Germanium Bolometer, Journal of the Op- tical Society of America, 51, 11.

[59] Maxwell, J.C., 1855, Experiments on Colour, as perceived by the Eye, with remarks on Colour-blindness, Transactions of the Royal Society of Edinburgh, 21, 275.

[60] McLean, I.S., 2002, A Golden Era for Astronomy:The Advent of CCDs and Infrared Arrays Scientiﬁc Detectors for Astronomy Workshop (2002: Waimea, Hawaii), 300,1.

84 [61] Meade, M., 2003, GLIMPSE: Galactic Legacy Infrared Mid-Plane Survey Ex- traordinaire,Web, , 19 August 2010.

[62] Meade, M., 2008, GLIMPSE: Galactic Legacy Infrared Mid-Plane Survey Ex- traordinaire,Web, ,13May 2010.

[63] Mercer, E.P., Clemens, D.P., Meade, M.R., Babler, B.L., Indebetouw, R., Whit- ney, B.A., Watson, C., Wolfire, M.G., Wolff, M.J., Bania, T.M., Benjamin, R.A., Cohen, M., Dickey, J.M., Jackson, J.M., Kobulnicky, H.A., Mathis, J.S., Stauffer, J.R., Stolovy, S.R., Uzpen, B., Churchwell, E.B., New Star Clusters Discovered in the GLIMPSE Survey, The Astrophysical Journal, 635, 560.

[64] Moorwood, A., Cuby, J.G., Lidman, C., 1998, SOFI sees ﬁrst light at the NTT, The Messenger, 91, 9.

[65] Moorwood, A., Cuby, J.G., Biereichel, P., Brynnel, J., Delabre, B., Devillard, N., van Dijsseldonk, A., Finger, G., Gemperlein, H., Gilmozzi, R., Herlin, T., Huster, G., Knudstrup, J., Lidman, C., Lizon, J.L., Mehrgan, H., Meyer, M., Nicolini, G., Petr, M., Spyromilio, J., Stegmeier, J., 1998, ISAAC sees ﬁrst light at the VLT, The Messenger, 94, 7.

[66] Morrison, D., Murphy, R.E., Cruikshank, D.P., Sinton, W.M., Martin, T.Z., 1973, Evaluation of Mauna Kea, Hawaii, as an Observatory Site, Publications of the Astronomical Society of the Paciﬁc, 85, 255.

[67] NASA, 2006, Hubble’s Largest Galaxy Portrait Oﬀers a New High-Deﬁnition View,Web, ,28February 2006.

[68] Negueruela, I.; Clark, J.S., 2005, Further Wolf-Rayet stars in the starburst cluster Westerlund 1, Astronomy and Astrophysics, 436, 541.

[69] Negueruela, I., Clark, J.S., Hadﬁeld, L.J., Crowther, P.A., 2008, Westerlund 1 as a Template for Massive Star Evolution, Proceedings of the International Astronomical Union, 250, 301.

[70] Negueruela, I., Clark, J.S., Ritchie, B.W., 2010, The population of OB supergiants in the starburst cluster Westerlund 1, Astronomy and Astrophysics, 516, A78.

85 [71] Neugebauer, G., Leighton, R.B., 1969, Two-micron sky survey. A preliminary catalogue, NASA SP (Washington, DC: NASA).

[72] Oosterhoﬀ, P.T., 1939, Some remarks on the variable stars in globular clusters, The Observatory, 62, 104.

[73] Perryman, M.A.C., ESA, 1997, The HIPPARCOS and TYCHO catalogues. As- trometric and photometric star catalogues derived from the ESA HIPPARCOS Space Astrometry Mission, Noordwijk, Netherlands: ESA Publications Divi- sion.

[74] Rayleigh, L., 1871, On the light from the sky, its polarization and colour, Philo- sophical Magazine, 41, 274.

[75] Rayner, J.T., Toomey, D.W., Onaka, P.M., Denault, A.J., Stahlberger, W.E., Vacca, W.D., Cushing, M.C., Wang, S., 2003 SpeX: A Medium-Resolution 0.8- 5.5 Micron Spectrograph and Imager for the NASA Infrared Telescope Facility, The Publications of the Astronomical Society of the Paciﬁc, 115, 362.

[76] Rayner, J.T., 2007, NSFCAM 2,Web, , 27 February 2007.

[77] Ritchie, B.W., Clark, J.S., Negueruela, I., Crowther, P.A., 2009, A VLT/FLAMES survey for massive binaries in Westerlund 1, Astronomy and Astrophysics, 507, 1585.

[78] Savage, B.D., Mathis, J.S., 1979, Observed Properties of Interstellar Dust,An- nual Review of Astronomy and Astrophysics, 17, 73.

[79] Science Software Group, 2001, CCMAP,Web, , 1 February 2001.

[80] Science Software Group, 1996, CCTRAN,Web, , 6 December 2000.

[81] Science Software Group, 1997, STARFIND,Web, , 9 October 2000.

[82] Shure, M.A., Toomey, D.W., Rayner, J.T., Onaka, P.M., Denault, A.J., 1994, NSFCAM: a new infrared array camera for the NASA Infrared Telescope Fa- cility, Proc. SPIE 2198, 614.

86 [83] Skrutskie, M.F., Cutri, R.M., Stiening, R., Weinberg, M.D., Schneider, S., Carpenter, J.M., Beichman, C., Capps, R., Chester, T., Elias, J., Huchra, J., Liebert, J., Lonsdale, C., Monet, D.G., Price, S., Seitzer, P., Jarrett, T., Kirkpatrick, J.D., Gizis, J.E., Howard, E., Evans, T., Fowler, J., Fullmer, L., Hurt, R., Light, R., Kopan, E.L., Marsh, K.A., McCallon, H.L., Tam, R., Van Dyk, S., Wheelock, S., 1997, The Two Micron All Sky Survey (2MASS),The Astronomical Journal, 131, 1163.

[84] Skrutskie, M.F., Cutri, R.M., Stiening, R., Weinberg, M.D., Schneider, S., Carpenter, J.M., Beichman, C., Capps, R., Chester, T., Elias, J., Huchra, J., Liebert, J., Lonsdale, C., Monet, D.G., Price, S., Seitzer, P., Jarrett, T., Kirkpatrick, J.D., Gizis, J.E., Howard, E., Evans, T., Fowler, J., Fullmer, L., Hurt, R., Light, R., Kopan, E.L., Marsh, K.A., McCallon, H.L., Tam, R., Van Dyk, S., Wheelock, S., 2006, The Two Micron All Sky Survey (2MASS),The Astronomical Journal, 131, 1163.

[85] Spitzer, L., 1978, Physical processes in the interstellar medium, New York, NY: Wiley-Interscience.

[86] Stetson, P.B., 1987, DAOPHOT - A computer program for crowded-ﬁeld stellar photometry, Publications of the Astronomical Society of the Paciﬁc, 99, 191.

[87] Telesco, C.M., Becklin, E.E., Capps, R.W., Tokunga, A.T., 1980, NASA’s New Infrared Eye, Sky and Telescope, 60, 462.

[88] University of Massachusetts, Infrared Processing and Analysis Center (JPL/ Caltech), 2001, The Two Micron All Sky Survey at IPAC,Web, , 1 Febru- ary 2006.

[89] Westerlund, B., 1961, A Heavily Reddened Cluster in Ara, Astronomical Jour- nal, 70, 57.

[90] Westerlund, B.E., 1987, Photometry and spectroscopy of stars in the region of a highly reddened cluster in ARA, Astronomy and Astrophysics Supplement Series, 70, no. 3, 311.

[91] VISTA/ESO, 2009, VVV Survey,Web, , 23 August 2010.

[92] Portegies Zwart, S., McMillan, S., Gieles, M., 2010, Young massive star clusters, eprint arXiv:1002.1961, To be published in ARAA.

87 [93] Zhang, Q., Fall, S.M., 1999, The Mass Function of Young Star Clusters in the ”Antennae” Galaxies, The Astrophysical Journal, 527, L81.