Ultraviolet Variable Sources in the Kepler Field

Nestor´ Daniel Olmedo Aguilar

Thesis submitted in partial fulﬁllment of the requirements for the degree of MASTER OF SCIENCE IN ASTROPHYSICS

at the Instituto Nacional de Astrof´ısica, Optica´ y Electronica´

February 2017 Tonantzintla, Puebla

Under the supervision of: Ph.D. Miguel Chavez´ INAOE Ph.D. Emanuele Bertone INAOE

c INAOE 2017 The author hereby grants to INAOE permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part.

Abstract

This work presents the UV flux catalog Multi-visit GALEX CAUSE Kepler (MGCK), created from the observations conducted over a period of about 45 days by the Galaxy Evolution Explorer (GALEX) space telescope within the Complete All-Sky Ultravio- let Survey Extension (CAUSE) in August-September 2012. The construction of the catalog was made with the Source Extraction software (SExtractor) in a dual mode using GALEX CAUSE Kepler (GCK) coadded catalog as input to perform the sources detections and their photometric measurements in each tile for each visit. MGCK contains the light curves of 660,490 point sources in the near ultraviolet (NUV) detected in the 104 square degrees field observed by the Kepler space telescope. MGCK has 475,164 sources in common with the Kepler Input Catalog (KIC). Approximately 31,000 sources from MGCK present significant variability. The MGCK catalog should enable the UV variability study in the Kepler field of astronomical sources like variable stars, eclipsing stars, UV flares detection, and extragalactic objects, if detected, like AGNs and Quasars, as well the complementary characterization of the Visible variability.

Acknowledgments

I express my gratitude to:

My thesis directors, Ph.D. Miguel Chavez´ and Ph.D. Emanuele Bertone, for their guidance, teachings, and patience.

M.Sc. Manuel Olmedo, fellow group colleague who guided me through the creation of the MGCK catalog.

M.Sc. Ricardo Lopez,´ fellow group colleague who guided me through the spectroscopic data reduction process.

To my thesis reviewers, for reading it and make useful suggestions.

CONACyT, for the ﬁnancial support it granted me to study my Masters degree. And thus, the opportunity to do science!

CONCyTEP, for the scholarship granted in the last months of this thesis.

To my mother, father, and brothers, Luis and Manuel, for the encourage- ment they gave me every day.

To Alejandra Jurado, Xiomara Garc´ıa and my friends, for all the support.

To my generation colleagues, with whom I shared classes and many study hours.

Dedico esta tesis a mi madre, a mi padre, y a mis dos hermanos.

Contents

Abstract iii

1 The Kepler ﬁeld in the UV 1 1.1 UV phenomena ...... 2 1.1.1 Thermal emission in the UV ...... 2 1.1.2 Stellar activity ...... 2 1.1.3 Non-thermal processes ...... 2 1.2 Variable sources in the UV ...... 2 1.2.1 Cepheids ...... 3 1.2.2 β Cephei ...... 4 1.2.3 Flare Stars ...... 4 1.2.4 Cataclysmic Variables ...... 5 1.2.5 Eclipsing binaries and rotational variables ...... 5 1.3 Contents ...... 6

2 The GALEX and Kepler missions 7 2.1 Kepler Mission ...... 7 2.1.1 Kepler telescope ...... 8 2.1.2 Kepler Field of View ...... 10 2.1.3 Kepler Input Catalog, Kepler Objects of Interest and other catalogs ...... 10 2.1.4 Kepler ligthcurves and discoveries so far ...... 11 2.1.5 Kepler K2...... 12 2.2 Galaxy Evolution Explorer ...... 14 2.2.1 GALEX telescope ...... 15 2.2.2 GALEX Complete All-Sky UV Survey Extension (CAUSE) . . 15 2.3 The GCK Catalog ...... 16 2.3.1 GALEX CAUSE proposal on the Kepler Field ...... 16 2.3.2 Observations ...... 17 2.3.3 Assembly of a catalog of NUV sources ...... 18 Image Co-adding ...... 18 Background and threshold estimations ...... 19 Source extraction and photometry ...... 19

ix Artifact Identiﬁcation ...... 19 2.3.4 The GCK UV source catalog ...... 20

3 Multi-visit GALEX CAUSE Kepler Catalog (MGCK) 23 3.1 Detection and photometry ...... 23 3.1.1 Weighting ...... 26 3.1.2 Artifact flags ...... 26 3.1.3 Cross-identification: Association ...... 27 3.1.4 Photometry ...... 27 3.1.5 Input files ...... 28 3.1.6 Extra processing ...... 28 3.2 Catalog description ...... 29 3.2.1 Output files ...... 30 3.3 Catalog quality ...... 32

4 Sources with Significant Variability 37 4.1 Variability detection algorithm ...... 37 4.1.1 Discrimination of artifacts and systematic errors ...... 38 4.1.2 False positives ...... 40 4.2 Examples of UV variable sources ...... 44 4.2.1 Eclipsing binary system ...... 44 4.2.2 Cepheid stars ...... 44 4.2.3 Cataclysmic variable ...... 45 4.2.4 Flare events ...... 46 4.3 Examples of UV variable sources of unidentified nature ...... 52 4.3.1 One visit event source ...... 52 4.3.2 Outburst variable ...... 53 4.3.3 Sources showing flare-like events ...... 54 4.3.4 Sources showing transit-like events ...... 55 4.3.5 UV variable - Visible stable source ...... 57

5 Optical spectroscopic follow-up of MGCK sources of unknown nature 59 5.1 Spectroscopic data reduction ...... 59 5.2 Spectrum and object’s nature inference ...... 60

6 Conclusions 63

List of Figures 65

List of Tables 67

A MGCK’s DataFrame 69

Bibliography 75

x Chapter 1

The Kepler ﬁeld in the UV

To study the nature and evolution of astrophysical objects and phenomena, the multiwavelength observations are a fundamental tool. In the particular case for stars, the observations in the space Ultraviolet (UV) range are an important complement to the optical data, infrared, X-ray, etc. The UV is particularly useful to the study of the stellar emission variability because it is more sensible to the external layers of stellar atmospheres in the case of solar-type stars. This variability is studied through light curves, this is the brightness fluctuations as a function of time. The data obtained by Kepler has revolutionized the study of exoplanets and astrophysics by providing high-precision, high-cadence, continuous lightcurves of tens of thousands of stars (Howell et al. 2014). The Kepler space telescope observed approximately 150,000 stars in the visible range between May 2009 and June 2013 searching for planets through planetary transits (see section 2.1). GALEX space telescope observed the same field in the UV range between August and September 2012, as part of the private fund Complete All-Sky Ultraviolet Survey Extension (CAUSE), in this case by the Cornell University (see subsection 2.3.1). The data were used to create the GALEX CAUSE Kepler (GCK) catalog of Ultraviolet point sources in the Kepler field. The catalog is published in Olmedo et al. (2015). The goal of this work is to create a NUV light curve catalog (multi-visit catalog) of the sources observed by GALEX in the Kepler Field, and to produce a sample of variable objects. This catalog will allow the UV study of variable phenomena, described below; in the case of stars and sources also observed by Kepler, a comparison between the visible and the UV ligth curves can be made. Having simultaneous observations offers an exceptional opportunity to study UV-Visible variability and study different phenomena. GALEX observations have approximately 17 visits throughout its month of observations and we expect to find a correlation between the two wavelenght ranges. To illustrate the richness of the information that this catalog can contribute, we show some examples of sources with significant variability.

1 1.1 UV phenomena

Before we analyze the GALEX data it is worth to recall some of the potential sources of UV radiation and its variability. In this subsection, we brieﬂy describe some of the physical phenomena that produce UV light in stars and galaxies. They are summarized below:

1.1.1 Thermal emission in the UV Stars radiate energy at all electromagnetic wavelengths with a flux peak defined by its temperature according to the Planck function through the Wien law. Thus, all stars radiate in the UV, but especially the spectral types O and B whose peak is in those wavelengths. This radiation is affected when stars change their Teff , either in short periods as in the case of pulsating stars or in long timescales as a result of stellar evolution. Thermal Bremsstrahlung emission is generated by the deceleration of an electron after it passes nearly a nucleus or ion field without being captured. The interaction can change the kinetic energy of the electron producing free-free radiation. This also applies for very hot gases with T > 106 K, where hydrogen is fully ionized.

1.1.2 Stellar activity The presence of photospheric features such as spots, faculae and flares (Hall 2008) are associated with chromosphere activity that may produce variations in the UV flux. These phenomena are associated with magnetic fields and are closely related to stellar rotation.

1.1.3 Non-thermal processes This is electromagnetic radiation produced by particles due to physical processes other than their thermal energy. Synchrotron emission occurs when a magnetic ﬁeld affects a free charge forcing it to accelerate in a spiral orbit. The electron is thus constantly accelerated and will emit electromagnetic radiation in the direction of its velocity vector. Depending on the energy of the electron and the strength of the magnetic ﬁeld, synchrotron emission can also occur at visible, ultraviolet and X-ray wavelengths1.

1.2 Variable sources in the UV

The UV variable sources we expect to ﬁnd in the GALEX Kepler survey are mostly stellar. Extragalactic transients sources may be found if they undergo signiﬁcant en-

1http://astronomy.swin.edu.au/cosmos/S/Synchrotron+Emission

2 hancement of UV emission. We consider, however, that extragalactic sources will be hardly present in our catalog because we are looking at the galactic plane where the interstellar extinction has very strong effects. On what follows we will consider only stellar sources. We show a variable star classiﬁcation diagram in Fig. 1.1 and the location of different classes in the HR diagram in Fig. 1.2. These stars can produce UV-Visible variations and below we give some examples with a brief description of some of the phenomena.

   Type I Classical    Cepheids    Type II W Virginis      Pulsating  RR Lyrae     stars RV Tauri       Long-period Mira type       variables Semiregular      Intrinsic Flare stars  Eruptive Variable   R Coronae Borealis  Stars      Supernovae          Novae   Cataclysmic   Recurrent novae   stars     Dwarf novae      Symbiotic stars    Eclipsing binaries  Extrinsic  Rotating variables

Figure 1.1: Variable stars classiﬁcation diagram2.

1.2.1 Cepheids In the Kepler ﬁeld, only one Cepheid star, V1154, is present (Derekas et al. 2016), and other stars may be candidates to also be classical Cepheids. These stars have very stable and well characterized light curves with periods typically from a couple of days up to a hundred days. The relation between period and luminosity is well studied and can be used to measure distances to stellar systems, to nearby galaxies, as well as to distant galaxies (Leavitt & Pickering 1912). The star V1154 has been study with detail in the optical, therefore, it provides an exceptional opportunity to also study its variability in the UV regime. The UV-Visible light curve for this source is shown in subsection 4.2.2.

2http://www.atnf.csiro.au/outreach//education/senior/astrophysics/ variable types.html

3 e

bsolute

Spectral class

Figure 1.2: HR diagram showing the position of variable sources. Image taken from Karttunen (2007).

1.2.2 β Cephei The β Cephei stars are located in the upper left of the HR diagram, in the B class type. They are hot massive stars, radiating mainly in the ultraviolet. The variation period is expected to be between three and seven hours, of small amplitude in the visible (Lesh & Aizenman 1978) and up to a magnitude in UV. The pulsation mechanism of the β Cephei stars is similar to that of Cepheids.

1.2.3 Flare Stars Although flare events occur in nearly all main-sequence stars with outer convective envelopes, the term Flare stars (UV Ceti stars) designates dwarf K and M stars with transient optical brightening (Pettersen 1989). Flares are believed to occur due a magnetic reconnection event that creates a beam of charged particles which impacts the stellar photosphere, generating rapid heating and emission at nearly all wavelengths (Davenport 2016). Although since the initial heating produces a very hot gas, the flare is most pronounced at short wavelengths: ultraviolet and x-ray (Percy 2007). Maehara et al. (2012) searched for stellar flares in optical wavelengths on solar-type stars (G- type main-sequence stars) using Kepler data from April 2009 to December 2009. They

4 found 365 superflares (flares with a typical energy release > 1033 ergs) on 148 of these stars with typical durations of a few hours and amplitudes of 0.1-1% of the stellar luminosity. Davenport (2016) presented the first automated search for flares from the full Kepler dataset regardless of their spectral type, founding 4041 stars presenting 851,168 flare events. The study of the UV flares is important in the case of the stars that host extrasolar planets. The UV, being high-energy radiation that may be detrimental for life, is of special interest in main sequence M stars due to their enhanced chromospheric activity (Segura et al. 2010).

1.2.4 Cataclysmic Variables

Cataclysmic variables or explosive variables are stars that undergo irregular sudden outbursts that can last from days to years and then return to a quiescent state. They are subdivided into several types, like supernovae, which are explosions that change the structure of the star with absolute magnitudes up to Mv = -19. Other types, less bright without the modiﬁcation of their structure may be present in the ﬁeld: Novae, Recurrent novae and Dwarf Novae. They consist of a white dwarf (primary star) accreting material from and a cool main sequence star (secondary), forming an accretion disk. Instabilities in this disk lead to short and long period photometric variability (Breedt et al. 2014). Strong UV and X-ray emission is often seen from the accretion disk, powered by the loss of gravitational potential energy from the infalling material. For Dwarf Novae, the sudden brightening is of 2 to 6 magnitudes within about 1 day, then fades back to is quiescent brightness; outburst recurrence times vary from system to system and depend primarily on the mass transfer rate (Sterken 1996).

1.2.5 Eclipsing binaries and rotational variables

The geometrical position of the orbital plane in binary systems with respect to the line of sight is also a cause of variation in their light curves. For example, eclipsing binaries are stellar systems in which as they rotate its center of mass, one of the components passes in front of the other, blocking light and producing brightness drops variations in its light curve. The amplitude of the variability in the visible and UV are similar in morphology to light curves observed in planetary transits, but much larger. Another geometrical variability is caused by the rotation of the star itself. Ro- tating variables are stars that diminish their brightness a few percentage as they rotate due to starspots. Since all stars rotate, any star with a patchy surface will be a rotating variable given the rotation axis is not aligned with the line of sight of the observer (Percy 2007). Starspots can be found in cooler stars like the sun and are regions of the photosphere which are cooler and therefore darker than the surrounding. Starspots are a manifestation of magnetic activity as a consequence of stellar rotation.

5 1.3 Contents

In chapter 2, we give a general description of the space missions GALEX and Kepler. The detailed description of the creation of the coadded catalog of the UV sources in the Kepler ﬁeld on which this work is based is described in section 2.3. In chapter 3 we describe the creation of the multi-visit catalog. In chapter 4 we show a sample of detected sources with signiﬁcative variability, meanwhile, in chapter 5 we describe the spectroscopic observation of one of these variable sources. In chapter 6 we detail the future work and conclusions of this work.

6 Chapter 2

The GALEX and Kepler missions

Kepler is a space telescope launched on March 6th, 2009 and forms part of NASA Discovery program. Kepler surveyed approximately 150,000 stars in the Milky Way galaxy at visible wavelengths in the search for extrasolar planets, with emphasis on the detection of Earth-size and smaller planets, inside and outside of the habitable zone, and with the goal of determining the fraction of stars that might harbor such planets. Currently, Kepler is performing a new mission that started in June 2014, known as K2 or “Second Light”, and is expected to end in 2018. The Galaxy Evolution Explorer (GALEX) is also a space telescope (currently in- active); it was launched on April 28th, 2003 and was led by NASA and subsequently by the California Institute of Technology. Its primary mission was to observe galaxies in ultraviolet light across 10 billion years of cosmic history. In 2012 it started the Complete All-Sky Ultraviolet Survey Extension (CAUSE) to pursue Milky Way science related to pre- and post-main sequence stellar variability. GALEX was turned off on June 28, 2013, and will be orbiting Earth the next 65 years and burn upon re-entering the atmosphere1.

2.1 Kepler Mission

The scientiﬁc objetive of the Kepler Primary Mission was “To explore the structure and diversity of planetary systems”2. During its primary mission, this goal was achieved by surveying a large sample of stars from a 104 deg2 region in the Cygnus constellation along Orions arm centered on galactic coordinates RA=19h 22m 40s, Dec=+44◦ 30’ 00” to pursue the following goals, as stated in Kepler Mission webpage3:

1Jet Propulsion Laboratory webpage, retrieved October 20, 2016, from http:// www.jpl.nasa.gov/news/news.php?release=2013-211 2Kepler Mission webpage, retrieved May 12, 2016, from http://kepler.nasa.gov/Mission/ QuickGuide/ 3Kepler Mission webpage, retrieved May 12, 2016, from https://kepler.nasa.gov/Mission/ QuickGuide/index.cfm

7 • Determine the percentage of terrestrial and larger planets that are in or near the habitable zone. • Determine the distribution of sizes and shapes of the orbits of these planets. • Estimate how many planets there are in multiple-star systems. • Determine the variety of orbit sizes and planet reﬂectivity’s, sizes, masses and densities of short-period giant planets. • Determine the properties of these stars that harbor planetary systems.

The Kepler primary mission used the Transit Method to detect planets. A transit occurs when a planet passes in front of its host star and blocks part of the starlight. An observer in the line of sight can observe a dip in the stars brightness, as can be seen in Figure 2.1, for star KIC 10593626 and planet Kepler-22b. A dip in the light with a duration of approximately 7 hours, periodic and observed at least 3 times indicates the presence of a planet blocking its light (Borucki et al. 2012). The light curve provides a wealth of information about the system. For instance, the size of the planet can be inferred from the depth of the transit and the star size; the planet temperature can be inferred from the orbital radius and the temperature of the star. From this information, it can be determined if the planet can harbor liquid water, which is the main criterium to consider it habitable.

Figure 2.1: Folded light curve with model ﬁt in red. Black dots represent individual observations. Dark blue points represent 30 minute binned data, and cyan points represent residuals after ﬁtting. Red asterisk represents the mid-transit time. Image taken from Borucki et al. (2012).

The results have yielded a broad understanding of planetary evolution, the frequency of formation, the structure of individual planetary systems, and the generic characteristics of stars with terrestrial planets.

2.1.1 Kepler telescope The Kepler instrument is a 0.95 meter Schmidt telescope. It has a very large Field of View (FOV), allowing it to focus on the same huge star ﬁeld for the entire primary

8 mission and to continuously monitor the brightness of more than 100,000 stars. The photometer is composed of an array of 42 Charge Couple Devices (CCDs). Each 50×25 mm CCD has 2200×1024 pixels. The exposure is integrated for 30 minutes for the majority of stars and, for a very limited number of targets, a short exposure time mode (1 min) is available. The instrument has the sensitivity to detect an Earth-size planet transit of a mv = 12 G2V (solar-like) star at 4σ significance level in 6.5 hours of integration. The instrument has a spectral bandpass from 400 nm to 850 nm. Data from the individual pixels that make up each of the 100,000 main sequence stars brighter than mv = 14 are recorded continuously and simultaneously. The data is stored on the spacecraft and transmitted to the ground about once a month. Pointing to the same field during the entire mission gives Kepler photometric stability. The only moving parts are the reaction wheels used to maintain the pointing, and the ejectable cover. A diagram of the telescope is shown in figure 2.2 with some of its components indicated.

Figure 2.2: Kepler space telescope. Image taken from Kepler ofﬁcial webpage4.

4Retrieved October 28, 2016, from https://kepler.nasa.gov/Mission/QuickGuide/ index.cfm

9 2.1.2 Kepler Field of View The FOV target of the Kepler mission was selected under the two following criteria: (i) The transit method requires a continuous monitoring of the stars, their brightness must be measured at least once every few hours. This restricted the choice of the stellar field that must be visible and unobstructed through all the year. Kepler orbits the Sun and, to avoid it, the field must be out of the ecliptic plane. (ii) Additionally, since the size of the sunshade was limited, the target field was chosen to be 55 degrees from the ecliptic plane so that the Sun wouldn’t shine into the telescope at any time. This can be observed in figure 2.3, note how the solar panels and the sunshade are always pointing to the Sun. The selected field with respect to the Milky Way arms is shown in figure 2.4. The second requirement was that the stellar field must have a large number of stars, preferably out of the galactic plane to reduce field confusion. This led to the selection of a region in the Cygnus constellation along Orion’s arm centered on galactic coordinates 76.32◦,+13.5◦ or RA=19h 22m 40s, Dec=+44◦ 30’ 00” (Figure 2.5). This field also virtually eliminated any confusion resulting from occultations by asteroids and Kuiper-belt objects, which commonly orbit near the ecliptic plane. Data from the US Naval Observatory digitalization of the Palomar Observatory Sky Survey (USNO-A1.0), complete to mv = 18, was used to determine that the actual number of stars with mv < 14 of all spectral types and luminosity classes in the 105 deg2 FOV was 223,000. About 61%, i.e., 136,000, are estimated to be main-sequence (MS) stars. During the first year of the mission, the 25% most active of the dwarf stars were eliminated reducing the number to 100,000 useful target stars.

2.1.3 Kepler Input Catalog, Kepler Objects of Interest and other catalogs The Kepler Input Catalog (KIC) is a database containing photometric and physical data for sources in the Kepler ﬁeld of view. KIC is used by the Kepler mission to select optimal targets (Brown et al. 2011), principally, to discern dwarfs from giants and in this way optimize the target selection toward ﬁnding Earth-sized planets in the habitable zones of Sun-like stars (Batalha et al. 2010). KIC contains roughly 13.2 million stars but only about 4.4 million fall on the Kepler detector for at least one season8. Kepler Stellar is the catalog composed of the 197,096 stars observed by the prime

5Retrieved October 28, 2016, from https://kepler.nasa.gov/mission/QuickGuide/ missiondesign/launch/ 6Retrieved October 28, 2016, from https://kepler.nasa.gov/Mission/QuickGuide/ index.cfm 7Retrieved October 28, 2016, from https://kepler.nasa.gov/science/about/ targetFieldOfView/ 8The Mikulski Archive for Space Telescopes (MAST), retrieved May 16, 2016, from http: //archive.stsci.edu/kepler/kic10/help/search help.html

10 Figure 2.3: Kepler’s orbit showing the spacecraft movements. The green circle corresponds to Kepler’s orbit, the gray circle corresponds to Earth’s orbit and the blue dots to Kepler’s position on March 5th of each year. Image taken from Kepler official webpage5. mission9, which were selected from the KIC based on their variability and periodicity. A Kepler Object of Interest (KOI) is a transit-like event, periodic and well vetted from the Kepler Stellar observed stars; thus there may be stars with multiple KOIs. Each KOI can be catalogued as planet candidate (later may be confirmed as planet) or flagged as false positive. KOIs are identified from the Threshold-Crossing Event (TCE) table for further vetting10.

2.1.4 Kepler ligthcurves and discoveries so far As stated by the Kepler mission documentation, its prime mission operated for four years until a second reaction wheel failed on the spacecraft. During this time, the spacecraft completed a 90 roll every 3 months in such a way that the panel always pointed to the sun for maximum solar panel efficiency. Therefore, Kepler data is divided into four quarters each year, separated by the quarterly rolls of the spacecraft. Kepler long cadence (30 min) images and light curves are stored in files that span a quarter. Short cadence (1 min) images and light curves are stored in files that span a month.

9NASA Exoplanet Archive, retrieved November 1, 2016, from http:// exoplanetarchive.ipac.caltech.edu/docs/Kepler stellar docs.html 10NASA Exoplanet Archive, accessed November 1, 2016, in http:// exoplanetarchive.ipac.caltech.edu/docs/Kepler KOI docs.html

11 Figure 2.4: Kepler pointing in solar neighborhood. Image taken from Kepler ofﬁcial webpage6.

The 14th quarter of the Kepler observations are contemporaneous to GCK, which goes from June 28 to Oct 03, 2012. Kepler has found 2,331 of the 3,449 confirmed planets11, including the latest 1,284 verified by NASA announced on May 10, 201612. Currently, Kepler has 4,696 exoplanet candidates, which have an 80-90% probability of being actual planets13. Be- sides planets, due to the shared method to find them, Kepler also found 2,878 eclipsing binaries and other variable stars.

2.1.5 Kepler K2 After the failure of two of the four reaction wheels of the spacecraft in July 2012 and May 2013, it was concluded that the degraded pointing accuracy affected the photometric precision; this resulted in the cancelation of the Kepler mission, which at that time was in a 4-year extension phase. The spacecraft was put in its rest position, waiting for the deﬁnition of an alternative scientiﬁc mission14. Using a combination of its remaining two working reaction wheels and thrusters for spacecraft attitude control, Kepler is now running the denominated “K2 mission”. K2 was approved for 2 years on May 16, 201415, and uses the same techniques as the

11NASA Exoplanet Archive, retrieved October 23, 2016, from http:// exoplanetarchive.ipac.caltech.edu/docs/counts detail.html 12NASA News webpage, retrieved October 23, 2016, from http://www.nasa.gov/press- release/nasas-kepler-mission-announces-largest-collection-of-planets-ever- discovered 13NASA New Worlds Atlas, retrieved October 23, 2016, from https://exoplanets.nasa.gov/ newworldsatlas/ 14NASA News, retrieved October 23, 2016, from http://www.nasa.gov/content/nasa- ends-attempts-to-fully-recover-kepler-spacecraft-potential-new-missions- considered#.WA uD2VHRFI 15NASA News, retrieved October 23, 2016, from http://www.nasa.gov/content/ames/kepler- mission-manager-update-k2-has-been-approved

12 Figure 2.5: Kepler Field of View. Image taken from Kepler official webpage7. prime mission for the searching of planets but in other fields. Due to the restrictions im- plicated by the described failures and by the Sun’s radiation, these fields are localized in the ecliptic plane and each have been continuously observed for approximately 75 days (Figure 2.6). The observations are divided into a series of sequential campaigns, each one composed of 10,000 targets in each 105 square degrees fields. K2 will carry out approximately 4 campaigns per year. The targets are selected by community proposals, through the Guest Observer program, that include exoplanet, stellar, extragalactic and solar system science (Howell et al. 2014). On June 9, 2016, NASA announced a two-years extension of the K2 mission, through 2018 mid-year, by which time the on-board fuel is expected to be fully de- pleted16. Currently, 145 planets detected by K2 have been confirmed and has provided 520 more candidates planets17.

16NASA, retrieved October 27, 2016, from https://www.nasa.gov/feature/ames/kepler/mission- manager-update-k2-marches-on 17Kepler and K2 Science Center, retrieved October 28, 2016, from https:// keplerscience.arc.nasa.gov/index.html

13 Figure 2.6: K2 observing strategy, each sequential campaign is a different ﬁeld with 83 days duration from which 75 are dedicated to science. Image taken from Howell et al. 2014.

2.2 Galaxy Evolution Explorer

The Galaxy Evolution Explorer (GALEX) was a space telescope launched in 2003 with the goal of perfom a UV survey of the sky as complete as possible. Indeed, GALEX performed the ﬁrst all-sky imaging and spectroscopic surveys in the ultraviolet at 1350- 2750 A˚ (Martin et al. 2005). GALEX primary goals were to study galaxy star formation rate (SFR) and determine the cosmic star formation history over the last 9 Gyr and its dependence on environment and create a predictive model of global SFRs in diverse contexts. GALEX performed several nested surveys to accomplish these goals and the baseline mission surveys were completed in Fall 2007. We brieﬂy describe them below. GALEX also performed Spectroscopic Surveys using its slitless spectroscopic capabil- ity. GALEX had a Guest Investigator Program (GIP), which assigned observing time through peer reviewed proposals. The variety of astronomical objects observed by the GIP helped to broaden the science return of the mission.

The baseline imaging surveys were:

1. All-sky Imaging (AIS): Covered a total of 26,000 square degrees, with exposures of 100 seconds to a sensitivity of mAB = 20 for far ultraviolet (FUV) and mAB = 21 for near ultraviolet (NUV).

14 2. Medium Imaging (MIS): Covered a 1000 square degrees matching positions with Sloan Digital Sky Survey (SDSS), and had 1500 seconds expositions achieving a mAB = 24. 3. Deep Imaging (DIS): Survey dedicated to an 80 square degrees ﬁeld in regions where major multiwavelength efforts were already underway. Achieved a depth of mAB = 25 with 30,000 seconds of integration time. 4. Nearby Galaxy (NGS): it targets nearby galaxies, 71 of which are also included in the Spitzer Nearby Galaxies Survey. It has a nominal exposure times between 1000 and 1500 seconds.

2.2.1 GALEX telescope GALEX is a 50 cm diameter modiﬁed Ritchey-Chretien´ telescope (Fig.2.7) with two channels used to observe at two bands: FUV 1344-1786 A,˚ and NUV 1771 -2831 A˚ for both imaging and slitless spectroscopy (Martin et al. 2005). The photon counting detectors on GALEX are designed to look at very faint objects in the sky, with mAB ∼25 in the deepest modes and a hardware count rate limit of 18 100,000 cps (mAB ∼ 7.5). Because the detectors are so sensitive, the telescope must always be pointed away from the Earth and the Sun, and also avoid very bright UV objects. The detectors are solar blind as a requirement for reliable ultraviolet photom- etry19, and are designed to image a very wide 1.25◦ ﬁeld of view, with 4” to 6” spatial resolution (Morrissey 2006).

2.2.2 GALEX Complete All-Sky UV Survey Extension (CAUSE) After the end of its planned surveys, GALEX began a privately funded mission extension phase, the Complete All-Sky UV Survey Extension (CAUSE). Since a signiﬁcant fraction of the Galactic Plane had not yet been surveyed (Figure 2.8), its highest priority goal was to complete this survey and pursue Milky Way science related to pre- and post-MS stellar evolution, the transfer of matter between stars and the ISM and characterization of stellar variability. Other high priority science goals include exploring the transient UV sky, searching for shock breakout supernova events, discovering tidal disruption of stars by massive black holes and exploring the low mass universe21. CAUSE functioned via funding from scientiﬁc or philanthropic institutions or individuals. A month of observations cost approximately $120K. The costs supported

18cps: counts per second 19GALEX Basics webpage, retrieved May 12, 2016, from http://www.galex.caltech.edu/ about/basics.html 20Retrieved May 12, 2016, from http://www.galex.caltech.edu/researcher/techdoc-ch1.html 21GALEX CAUSE webpage, retrieved May 16, 2016, from http://www.galex.caltech.edu/ cause/ 22Retrieved May 16, 2016, http://www.galex.caltech.edu/cause/

15 Figure 2.7: A cross section of the GALEX instrument showing the 50 cm primary mirror and its components. The light path is shown with blue lines. Image taken from GALEX ofﬁcial webpage20. a structure of operations team producing standard pipeline products for observations, which can be proprietary for up to one year.

2.3 The GCK Catalog

GALEX CAUSE Kepler (GCK) is the UV point source catalog created by Olmedo et al. (2015) from the GALEX CAUSE observations acquired by Cornell University, led by Ph.D. James Lloyd. In this section, we review the construction of the GCK catalog, as this work follows the same methodology for the construction of the MGCK database.

2.3.1 GALEX CAUSE proposal on the Kepler Field The many planets confirmed and set as candidates by Kepler, described earlier, turned the community attention to the stars in the field observed by the telescope. The next step following these discoveries is to analyze the stars in the Kepler field, characterize them and understand how different are the stars without planets from those that harbor them; and how the later can affect the planets they host. Multiwavelength studies help understand how variable phenomena behave in different energy ranges. Driven by this and that only around 30% of the Kepler field was previously observed by GALEX, Cornell University funded with a $100k contribution, 300 GALEX CAUSE orbits to

16 Figure 2.8: GALEX CAUSE Galactic Plane Coverage. In green are depicted the Scan Mode observations (note the Kepler field between 70 and 80 degrees), in brown are the Pointed Mode Observations and in Grey, major Milky Way Features. Updated to September 10, 2012. Image taken from GALEX CAUSE webpage22. complete the spatial coverage of the Kepler field through August and September 2012. This project was led by P.I. James Lloyd (Lloyd 2012). As stated in the observational proposal (Lloyd 2012), GALEX CAUSE relaxed pointing constraints allowed the extension of the photometric of the Kepler field to the NUV to probe the ages of stars. Planets transiting young stars are particularly compelling, as diagnostics on the formation mechanisms of planets are lost as planets age and cool. Further, the wide field of view (1.2 degrees) and photon counting nature of GALEX enables a survey of the Kepler field in the time domain. This UV observations enables the study of the Kepler sources UV variability, alongside the Visible variability, and new studies on the relationships among stellar age, activity and rotation.

2.3.2 Observations As described in Olmedo et al. (2015), the GCK catalog technical details and processing were as follows: The GALEX CAUSE observations of the Kepler ﬁeld are composed of 180 tiles, each one with 20 visits on average. A complete image composed of all the tiles is shown in Figure 2.9. These observations used a drift mode, which scanned a strip of the sky along a great circle, as long as 12◦. The scans required adaptation to work through the standard processing pipeline, and were processed in tile sizes images. The short scans (1-3 and 13-15) resulted in 9 images and the long scans (4-12), in 14 images (Figure 2.10) leading to the 180 tiles, each tile with an average of 17 visits, resulting in a total of ∼3200 images. The GCK data were processed with the GALEX scan mode pipeline and subsequently delivered to Cornell in the form of packs of 5 images per visit for each tile, described in Table 2.1 of the GALEX Technical Documentation23. Due to being affected by an artifact known as doubling of sources (or ghosting of sources), 450 images

23Retrieved November 2, 2016, from http://www.galex.caltech.edu/wiki/Public: Documentation/Appendix A

17 were discarded. Some tiles lack a single good visit because they were affected by a spacecraft’s maneuver. For this reason, the 13th image of scans 4, 5, 6, 9 and 10, were not included in the catalog construction, leaving a total of 175 tiles and representing a loss of no more than 0.5% coverage of the Kepler ﬁeld.

Table 2.1: Files of GCK dataset

Fits name Image type Units Description nd-counts.fits count image photons/pix The raw number of counts per pixel, not corrected for the exposure time or flat field. nd-rrhr.fits effective exposure map s pix−1 High resolution relative response image. Effective exposure time per pixel. nd-int.fits intensity map photons s−1 pix−1 The intensity map in units of counts/sec. This is the cnt map divided by the rrhr map. nd-flags.fits artifact flags image ... Flag map indicating regions of the map likely contami- nated by artifacts or regions where various types of artifacts have been removed. xd-mcat.fits catalog of sources ... Table of sources extracted by GALEX reduction pipeline. Contains positions, flux, magnitude, and major and minor axes, etc.

2.3.3 Assembly of a catalog of NUV sources The GCK was constructed in four stages: Image co-adding, background estimation, source extraction and photometry, and catalog clean-up.

Image Co-adding Prior to co-adding all epochs for a given tile, each image was visually inspected, discarding visits presenting the source doubling issue. The images already had astrometric solution and each epoch for a given tile were aligned, thus co-adding only required an arithmetic sum.

1. Construction of the co-added count image as the arithmetic sum of individual count intensity images (nd-counts.ﬁts).

2. Construction of the co-added effective exposure image as the arithmetic sum of individual effective exposure images (nd-rrhr.ﬁts).

3. Construction of a combined flag images as the logic OR of the individual flag images (nd-flags.fits).

4. Calculation of the ratio of the co-added count and effective exposure images to obtain the ﬁnal intensity image.

18 Background and threshold estimations To detect the sources using SEXtractor, background and threshold images are required. Background estimation: The background image was constructed using a σ-κ clip- ping method. For 128 pix wide square bins in the count image, the local background histogram was built assuming Poisson distribution (due to the low NUV background count rates), and the probability Pk(x) of observing k events for a mean rate x was cal- −3 culated. The pixels with a probability Pk(x) < 1.35 ×10 (equivalent to a 3σ level) were iteratively clipped out until convergence were reached. Then, a 5 × 5 median filter was applied to decrease the bias due to bright sources. The bin mesh was upsampled to the original resolution and divided by the effective exposure time image to produce the final background image, which was subtracted from the intensity image to produce the background-subtracted intensity image. Weight threshold image: This image provided the threshold for potential detections. For its construction, the count image was divided in 128 × 128 pix bins. In each bin the value of k, in counts/pix, which corresponds to a probability of 3σ, was computed and stored to produce a threshold map. This map was upsampled to the original resolution and divided by the effective exposure image, in order to obtain a threshold image in counts s−1 pix−1. The final step was to compute a weight threshold image by dividing the background-subtracted intensity image by this last threshold image.

Source extraction and photometry The processes of detection of the sources and their photometric measurement were carried out with the software SExtractor (Bertin & Arnouts 1996) working in dual mode: the weight threshold image is used for detecting sources, while their photometry is computed on the background-subtracted intensity image. SExtractor parameters THRES TYPE and DETECT THRESH were set to “absolute” and “1”, so all pixels with values above 1 in the weight threshold image were considered as possible detections. SExtractor was executed for each of the 175 tiles in the GCK data, delivering the photometry of each source. The photometric error dm is calculated following the GALEX pipeline:

p(f + sΩ)t df = , dm = 1.086 · df/f , (2.1) t where f is the ﬂux from the source in counts s−1, s is the sky level in counts s−1 pix−1, Ω is the area over which the ﬂux is measured, and t is the effective exposure time in seconds.

Artifact Identiﬁcation In the GALEX imagery, various artifacts are present, some of which are not automati- cally detected by the GALEX pipeline. The worrisome non-ﬂagged artifacts are large

19 diffuse reflections within the field, caused by surrounding very bright stars. The artifacts’ morphology are quite different; the most common are long thin cones, halos, and horseshoe-shaped extended reflections. These artifacts bias the background and affect source detection, mainly producing false positives. In order to remove extended objects and spurious detection, caused by non-flagged artifacts, they used suitable criteria to remove them and prevent the loss of genuine sources. They were designed based on the geometric characteristics of the aperture fitted by SExtractor to the detected source:

1. Semiminor axis > 6000; 2. Eccentricity > 0.95; 3. Signal to Noise Ratio (SNR) < 1.05; 4. SNR < 1.5 and Semimajor axis > 1000.

These criteria were defined through a trial and error process: 1 and 2 criteria are intended to remove large and/or extended sources; 3 discards too low SNR detections and 4 removes detections at the border of the images. The data reduction pipeline produces a flag image per tile, indicating the pixels where the intensity image is affected by some artifacts or where artifacts were removed. Each detected source has an associated artifact flags keyword, produced by a logical OR of the artifact flags that were used to compute its photometry. Some flags do not affect the scan mode, like the one that indicates being close to the detector’s edge, that is because, in the scan mode, the detectors edge crosses nearly the entire field for a fraction of the integration time. Sources affected by artifacts that modify their flux value were discarded.

2.3.4 The GCK UV source catalog The resulting 175, one for each tile, catalogs were then combined to produce a single point source catalog for the whole GCK ﬁeld, named the ”GCK catalog”. In sources with multiple detections (due to small overlap between tiles), the measurement with the highest SNR was retained. GCK catalog contains 660,928 point NUV sources. The NUV brightness distribution is shown in Figure 2.11 with the blue line. The GCK catalog can be considered complete up to ∼22.5 NUV mag, where the sources start to decline. The typical error for sources with NUV < 22.6 mag is less than 0.3 mag. GCK catalog was cross-matched with the KIC using a 2.5” search radius; this resulted in 475,164 GCK objects with a KIC counterpart. The same exercise was executed with the KOI catalog, ﬁnding 2614 candidate host stars and 327 stars hosting 768 planets in common.

20 50◦

45◦ 2000 δ

40◦

20h 00m 19h 30m 19h 00m 2000

Figure 2.9: Intensity image mosaic of GALEX CAUSE NUV observations. The blue square corresponds to the position of the central CCD of the Kepler telescope. Image taken from Olmedo et al. (2015).

21 Figure 2.10: Scan mode observations of GCK survey covering the Kepler ﬁeld. The ﬁeld of view of GALEX and a full moon are shown for comparison. The numbers on the upper right edges correspond to the scan numbers. The plus symbols corresponds to the edges of Kepler detectors. Image taken from Olmedo et al. (2015).

106

105

104 N

103

102

101 12 14 16 18 20 22 24 26 NUV magnitude

Figure 2.11: Distribution of NUV detections according to its magnitude. Blue line indicates GCK catalog distribution and red line the matched objects with KIC. Same for the dotted lines, but for the central CCD of the Kepler ﬁeld. Image taken from Olmedo et al. (2015).

22 Chapter 3

Multi-visit GALEX CAUSE Kepler Catalog (MGCK)

In this chapter we describe the procedure used to create the Multi-visit GALEX CAUSE Kepler Catalog (MGCK) and how the detections made by the GCK catalog were used to identify the sources in each of the GALEX images through the 45 days of observations of GALEX of the Kepler Field. After specifying the criteria taken to eliminate the effects of artifacts and bad visits, and the procedure used to calculate ﬂux and ﬂux errors, we review the multi-visit catalogs keywords and describe the MGCK data quality.

3.1 Detection and photometry

For the identification of point sources in the different individual visits we relied in the detection of point sources provided by the GCK catalog. This produced a deep UV point source catalog of the GALEX observations that cover the Kepler field and approximately 10 square degrees more (Kepler had spaces between detectors covered by GALEX CAUSE). To create the multi-visit catalog we used, as in GCK, SExtractor; which is a free software that builds a catalog of objects from astronomical images (Bertin & Arnouts 1996). SExtractor was used in dual mode, in which a threshold image is used as input to perform the sources detection. Because we have the GCK catalog, we already know the position of all of the co-added sources and use them as references to perform the SExtractor photometry in each of the visit’s images. This guarantees that the maximum number of detections were secured in each of the visits. The main advantage of using GCK as a reference is that it includes faint sources which are harder to detect in single visits but easier when coadded. If we would have performed the detections without GCK as a reference, in cases where sources appear in just one visit, we wouldn’t be able to tell with certainty if the source is present in the rest of the images but too faint to be detected or whether it is a unique event. SExtractor uses two files for its proper operation: the configuration file and the

23 catalog parameter file. Many of the configuration file parameters (default.sex) are used with their default values. The catalog parameter file (default.param) is the list of parameters that will be listed in the output catalog for every detection. In this way, SExtractor only retrieves the parameters needed. The parameters modified for the configuration file are shown in Table 3.1 and the parameters required from the catalog parameter file are listed in Table 3.4.

Table 3.1: Values set for parameter keywords from the conﬁguration ﬁle used as input to SExtractor.

Keyword Value Description Background BACK_TYPE MANUAL AUTO o MANUAL BACK_VALUE 0.00 Constant value to be subtracted from the images or zero if already subtracted. BACK_FILTERSIZE 5, 5 BACKPHOTO_TYPE GLOBAL BACKPHOTO_THICK 24 Extraction DETECT_MINAREA 10 Minimum number of pixels above threshold trig- gering detection. DETECT_TYPE CCD Linear detector like CCDs or NICMOS. THRESH_TYPE ABSOLUTE Absolute threshold level (in Analog to Digital Units (ADUs) or in surface brightness). DETECT_THRESH 1.0 Detection threshold (ADUs or relative to Back- ground RMS). FILTER N If true, filtering is applied to the data before extraction. FILTER_NAME gauss_30_7x7.txt Name of the file containing the filter definition. DEBLEND_MINCONT 0.04 Minimum contrast parameter for deblending, a zero value means that even the faintest local profile peaks will be considered as separate objects. CLEAN Y (Y or N) if true, a spurious detections “cleaning” of the catalogue is done before being written to disk. CLEAN_PARAM 1.0 Cleaning efficiency. MASK_TYPE CORRECT Method of masking of neighbours for photometry, CORRECT: replace by values of pixels symetric with respect to the source center. Weight

WEIGHT_TYPE MAP_WEIGHT, The input weight-map is in units of relative MAP_WEIGHT weights. WEIGHT_GAIN Y Weight maps are considered as gain maps. Continued on next page 24 Table 3.1 – Continued from previous page Keyword Value Description Flags FLAG_IMAGE flags.fits File name(s) of the “flag-image(s)” FLAG_TYPE OR Combination method for flags on the same object: Arithmetical OR. Photometry PHOT_AUTOPARAMS 2.5, 3.5 MAG AUTO controls: scaling parameter k of the st 1 order moment, and minimum radius (Rmin) for a Kron ellipse. PHOT_APERTURES 2, 3, 5, MAG AUTO minimum (circular) aperture diame- 8, 12, 17, ters. 23 PHOT_AUTOAPERS 0.0,0.0 Minimum aperture circular diameters in case the radius of the Kron aperture goes below the Rmin. SATUR_LEVEL 1000000000 Pixel value (in ADUs) above which it is considered saturated. MAG_ZEROPOINT 20.08 Zero-point offset to be applied to magnitudes. MAG_GAMMA 4.0 γ of the emulsion (takes effect in PHOTO mode only but need to be specified in any other mode). BOR- RAR? GAIN 1 “Gain” (conversion factor in e–/ADU) used for error estimates of CCD magnitudes . PIXEL_SCALE 1.5 Pixel size in arcsec. PHOT_FLUXFRAC 0.2, 0.5, Fraction of FLUX AUTO defining each element of 0.8, 0.9, the FLUX RADIUS vector. 0.95 Association ASSOC_TYPE NEAREST ASSOCiation method. Retrieve the closest match. ASSOC_RADIUS 2.0 Search radius (in pixels) for ASSOC. ASSOCSELEC_TYPE MATCHED Keep only SExtractor detections that were matched with at least one ASSOC- list member. Other INTERP_MAXXLAG 1 Max. x gap (in pixels) allowed in interpolating the input images. INTERP_MAXYLAG 1 Max. y gap (in pixels) allowed in interpolating the input images. INTERP_TYPE NONE Interpolation method from the variance-maps or weight-maps.

25 3.1.1 Weighting Although the noise is often fairly constant in astronomical images, in some cases, like vignetted or composite images, this approximation is not good enough. This is the case for the GALEX CAUSE observations. SExtractor is able to handle variable noise level through the weight images. These images have the same dimensions of the counts images (where the objects are detected and measured) and describe the noise intensity in each pixel, stored in units of absolute variance (in ADU2). The weight-map (MAP_WEIGHT) used in MGCK (and in GCK) is an image data product created from GALEX pipeline which also uses SExtractor, and contains the map in units of relative weights.

3.1.2 Artifact flags One way to indicate the quality of the detections are the error or artifact flags, which indicate if it is near the border, where the detector efficiency drops, or if it near a very bright source and others cases where the detections and its photometry can be compromised. Consult Table 3.2 and 3.3 for a complete listing. In MGCK two kinds of error flagging exist, the internal, processed by SExtractor, and the external flags. The internal flags are always computed and saved as a short integer. This number contains the flags indicating the artifacts in Table 3.2, which are expressed as a sum of powers of two.

Table 3.2: SExtractor error ﬂags.

Number Value Flag description 1 1 The object has neighbours, bright and close enough to significantly bias the MAG AUTO photometry, or bad pixels (more than 10% of the integrated area affected). 2 2 The object was originally blended with another one. 3 4 At least one pixel of the object is saturated (or very close to). 4 8 The object is truncated (too close to an image boundary). 5 16 Object’s aperture data are incomplete or corrupted. 6 64 A memory overflow occurred during deblending. 7 128 A memory overflow occurred during extraction.

The external flags are computed when an input flag-map is specified. The flag-map values for pixels that coincide with the isophotal area (the pixels above the specified threshold) of a given source are then combined and stored as a long integer in IMAFLAGS_ISO. To perform the combination, we use the logical OR operator, which gives the arithmetic (bit-to-bit) result of the flag-map isophotal area pixels. The Inter- nal and external flags can be found in the MGCK catalog, in the fields named SE_flags and artifact_flags, respectively.

26 Table 3.3: GALEX CAUSE pipeline artifacts ﬂags.

Number Value Name Artifact description 1 1 edge Detector bevel edge reflection (NUV only). 2 2 window Detector window reflection (NUV only). 3 4 dichroic Dichroic reflection. 4 8 varpix Variable pixel based on time slices. 5 16 brtedge Bright star near field edge (NUV only). 6 32 detector rim Proximity (> 0.6 deg fld ctr). 7 64 dimask Dichroic reflection artifact mask flag. 8 128 varmask Masked pixel determined by varpix. 9 256 hotmask Detector hot spots. 10 512 yaghost Possible ghost image from YA slope.

3.1.3 Cross-identification: Association After the detection of the sources in each of the visits, SExtractor performed a cross- identification with GCK to identify the sources and associate them to a GCK identifier. The association was carried out through the input sources coordinates that SExtractor will look for in each of the corresponding images and were named after the location in the field, going from ‘scan01image01’ to ‘scan15image09’. The association was done with a ‘Nearest’ (ASSOC_TYPE) type of association, in which the closest (in distance) match is retrieved, provided that it lies within 2 pixels of maximum distance (ASSOC_RADIUS) between detection’s barycenter and current detection, and keeping only the detections matched with at least one ASSOC list member (ASSOCSELEC_TYPE).

3.1.4 Photometry The photometry was performed in each source for the isophotal area (isophotal magnitude), that is, only the flux in the pixels above the threshold is taken into account for the magnitude. To control the elliptical aperture, the k factor and the minimum radius (Rmin) are specified with the keyword PHOT_AUTOPARAMS; in case the aperture falls below the Rmin, the default PHOT_AUTOAPERS are used. The radius size is directly proportional to the k factor. The photometry was also performed for fixed apertures with diameters of 2, 3, 5, 8, 12, 17 and 23 pixels (fixed-aperture). The input parameters control the threshold above which the flux is measured with the keyword DETECT_THRESH, which is 1 Analog to Digital Units (ADU) relative to the background. The keyword GAIN specified the way SExtractor convert counts received in the detector to flux. MAG_ZEROPOINT is a background calibration, used as zero point and thus, it is subtracted from the estimated magnitude. Another photometry related output parameter (flux_radius) estimated was the

27 radius at which the light drops to given percentages: 20, 50 80, 90 and 95%. These parameters, the isophotal magnitude and the ﬁxed-aperture with its respective errors, can be found in magnitudes and ﬂux form in the keywords of the MGCK.

3.1.5 Input ﬁles The raw data input for the creation of MGCK were acquired from the Mikulski Archive for Space Telescopes (MAST) webpage1. The images are, as described in GALEX documentation2:

1. Weight Threshold Image (J2000) (wt): Weight/threshold image (J2000): Used by SExtractor for thresholding sources. Pixels > 1 are above the detection threshold. This image is derived by dividing the background substracted intensity by a detection threshold image.

2. Background Substracted Intensity Image (J2000) (intbgsub): Intensity map image with the background removed (used for source detections). “-intbgsub.fits” is equal to “-int.fits” minus “-skybg.fits”.

3. High-Resolution Relative Response Image (J2000) (rrhr): Effective exposure time per pixel upsampled from the -rr.ﬁts image.

4. Artifact Flag Image (J2000) (flag): Identifies predetermined regions which may introduce systematic errors in data extraction. These artifact regions are caused primarily by reflections of known bright stars.

3.1.6 Extra processing Some keywords are present in the GALEX Team pipeline in addition to those provided by SExtractor. The former keywords were calculated afterwards, and a few others required subsequent analysis. In this section, we describe how they were calculated.

1. fov_radius Is the source’s distance from the center of the ﬁeld of view of the image where it was detected. It was calculated with the python function astropy.coordinates.Sky- Coord.separation(), which uses the astronomic coordinates of the sources and the center of the tiles. The last parameter was obtained previously while making GCK.

1MAST webpage, http://galex.stsci.edu/GR6/?page=scanmode#ftp 2http://www.galex.caltech.edu/wiki/Public:Documentation/Appendix-A#Direct- Imaging-Data-Products

28 2. nuv_exptime The effective exposure time of the source. This is obtained from the rrhr images, taking the value of the nearest pixel to the source coordinates.

As the input images to SExtractor were background subtracted, the output keywords that required the background estimation were miscalculated. Additional pro- cesing was done in the following keywords to take background into account.

1. nuv_magerr and nuv_flux_err The photometric error in magnitudes is calculated the same way as in the GALEX pipeline3:

p(f + sΩ)t df = , dm = 1.086 · df/f , (3.1) t where f is the flux from the source in counts sec−1 obtained from the int images, s is the sky level in counts sec−1 pix−1 obtainden from the skybg images. Ω is the area over which the flux is measured and t is the effective exposure time in seconds (nuv_exptime). df is the flux photometric error in count−1.

2. nuv_bkgrnd_flux and nuv_bkgrnd_mag The ﬂux level of the background at the centroid of the source in the skybg image. Is obtained from the nearest pixel to the source coordinates and divided by the area of one pixel (2.24”).

3. nuv_s2n The signal to noise ratio for the NUV flux measurement of the source. This keyword was not calculated by SExtractor and is added in our GCK pipeline. The calculation is simply the ratio between the NUV flux and the NUV flux error.

3.2 Catalog description

The resulting catalog, MGCK, contains 660,428 light curves with 16 visits in average over 45 days. The start, end date and duration of each visit are speciﬁed in three of the catalog’s keywords. For an overall inspection and comparison of these dates, consult Fig.3.1, in which the dates per scan and the integration time per tile are given. Note how the observations are almost continuous but for three empty spans.

3http://asd.gsfc.nasa.gov/archive/galex/Documents/GALEXPipelineDataGuide.pdf

29 Integration time per scan and visit 450 15 14 400 13 12 350 11 10 300 9 8 250 7 200 6 Scannumber 5 150 4 [s] time Integration 3 100 2 1 50

08-03 08-08 08-13 08-17 08-22 08-27 09-01 09-05 09-10 09-15 09-20 Date

Figure 3.1: Visits integration time and date per scan and tile. The left axis indicates the scan number, each square represents a scan tile, the color indicates the integration time in seconds and the bottom axis indicates the date.

3.2.1 Output files Each of the 660,428 objects detected in the MGCK catalog was stored as a Pandas DataFrame containing the information of its light curve. Pandas is a Python pack- age dedicated to database management which provides data structures and functions to work with structured data. A DataFrame represents, as stated in McKinney (2013), a tabular, spreadsheet-like data structure containing an ordered collection of columns, each of which can be a different value type (numeric, string, boolean, etc.). We chose this file type for the catalog storage because a single source light curve or a sample of sources can be called without the need of calling any others in which we are not interested, which is necessary due to the size of the MGCK dataset. The sources filenames and the main identifier are the GCK id, a sexagesimal equatorial position-based source name (i.e., GCK Jhhmmss.ss +ddmmss.p), for example, J18443923+4322283.p. The DataFrame has one main identifier, which is the same as the filename and two indexes, the row number and the visit number, both numbers may differ (and will do in most of the cases), as visits are not sequential due to the fact that some were discarded. The keywords of MGCK are described in the Table 3.4.

Table 3.4: MGCK keywords.

Keyword Units Description alpha_j2000 [degrees] Right ascension (RA) of barycenter (J2000). delta_j2000 [degrees] Declination (DEC) of barycenter (J2000). nuv_mag [magnitude] NUV apparent magnitude. nuv_magerr [magnitude] NUV apparent magnitude error. Continued on next page 30 Table 3.4 – Continued from previous page Keyword Units Description nuv_flux [erg s−1cm−2] NUV flux. nuv_fluxerr [erg s−1cm−2] NUV flux error. exp_start [BJD] Exposition start. exp_end [BJD] Exposition end. exp_time [s] Expostition duration. variable_flag - Flag for visit with significative variation (value 1), otherwise zero. nuv_s2n - NUV signal to noise ratio. nuv_bkgrnd_mag [magnitude] NUV background magnitude. SE_flags - SExtractor flags coded as a sum of powers of two (see table 3.2). artifact_flags - Artifact flags coded as a sum of powers of two (see table 3.3). a_image [pixel] Profile RMS along major axis. b_image [pixel] Profile RMS along minor axis. kron_radius - Kron radius in units of major axis A or minor axis B. theta_image [degrees] Position angle (CCW/x). erra_image [pixel] RMS position error along major axis. errb_image [pixel] RMS position error along minor axis. errtheta_image [degrees] Error ellipse position angle (CCW/x). ellipticity - 1− B_IMAGE/A_IMAGE. How stretched the object is. fwhm_image [pixel] FWHM assuming a gaussian core. x_image [pixel] Object position along x. y_image [pixel] Object position along y. xpeak_image [pixel] x-coordinate of the brightest pixel. ypeak_image [pixel] y-coordinate of the brightest pixel. flux_max [counts] Peak flux above background. sexnumber - Running object number. class_star - S/G classifier output. fov_radius [degrees] Distance from center of FOV. flux_radius20 flux_radius50 Radius for 20, 50, 80, 90, and 95 percentage flux_radius80 [pixel] fraction of light . flux_radius90 flux_radius95 mag_aper2 mag_aper3 mag_aper5 Magnitude for fixed circular apertures with mag_aper8 [magnitude] diameters of 2, 3, 5, 8, 12, 17 and 23 pixels. mag_aper12 mag_aper17 mag_aper23 Continued on next page

31 Table 3.4 – Continued from previous page Keyword Units Description magerr_aper2 magerr_aper3 magerr_aper5 Magnitude RMS error for fixed circular magerr_aper8 [magnitude] apertures with diameters of 2, 3, 5, 8, 12, 17 magerr_aper12 and 23 pixels. magerr_aper17 magerr_aper23 flux_aper2 flux_aper3 flux_aper5 Flux in fixed circular apertures with flux_aper8 [count] diameters: 2,3,5,8,12,17 and 23 pixels. flux_aper12 flux_aper17 flux_aper23 fluxerr_aper2 fluxerr_aper3 fluxerr_aper5 Flux error in fixed circular apertures with fluxerr_aper8 [count] diameters: 2,3,5,8,12,17 and 23 pixels. fluxerr_aper12 fluxerr_aper17 fluxerr_aper23

3.3 Catalog quality

One indicator of the photometric quality of the MGCK catalog is the Signal-to-Noise Ratio (SNR), which quantify of how much the signal can be differentiated from noise and background level. Figure 3.2 shows the number of sources as a function of the SNR in the GCK catalog with a given percentage of visits SNR above 3, as this percentage grows, the number diminishes. This is due to the intrinsic brightness of the sources; and the results are in accordance with GCK’s NUV magnitudes (see Figure 7 from Olmedo et al. 2015). In the histogram of Figure 3.3 we show the amount of sources as a function of number of visits. In purple color we show the number of sources regardless of the SNR. In red the number of sources with all visits with SNR above 3. Sources that only have one visit or not all of its visits with SNR above 3 are not necessarily considered low- quality sources or not sources at all. We found that there are real sources in MGCK with only one visit’s SNR above 3, and its GCK SNR above 3, with a typical value of 50, mostly attributed to only that one visit. Examples of this kind of sources will be given in the next chapter. Of course, we cannot study the UV variability of a source with just one visit, at

32 Histogram of Objects by GCK SNR

% and N sources GCK SNR = 3 30000 0% - 660490 25% - 270711 50% - 226402 25000 75% - 192055 100% - 150875

20000 N

15000

10000

5000

0 0 5 10 15 20 25 30 GCK SNR

Figure 3.2: Number of sources as a function of the SNR in the GCK catalog with a given percentage of visits’ SNR above 3. The totality of the sources is indicated with purple color no matter what SNR have their visits, meanwhile, blue, green, yellow and red, indicates a minimum 25%, 50%, 75% and 100% of its visits with SNR above 3, respectively, as indicated by the inset legend square. The intermittent vertical line denotes GCK’s SNR equal to 3. least 5 visits are needed to establish a proper flux average and then find the visits, if any, in which the source flux deviates significantly from that average. The number of objects with less than 5 visits is 10, 560, that is 1.59% of the total sources. These sources are not useful for variability studies, nevertheless can give an insight of how much their UV flux is compared to its visible radiation. To verify the consistency of MGCK with GCK, we made a histogram of the number of detections by NUV magnitude (Figure 3.4). We can appreciate how the detections number start falling at magnitude 21 meanwhile, GCK starts falling at magnitude 22.5, this difference is because GCK incorporates the coaddition of several images. This verifies the concordance of both catalogs.

33 Histogram of objects by number of visits 70000

All vis. SNR >= 3 No. Visits : n No. Visits : n 60000 1: 963 1: 0 2: 1709 2: 0 3: 4588 3: 1367 4: 3300 4: 721 5: 1959 5: 0 50000 6: 3364 6: 0 7: 5754 7: 0 8: 9609 8: 0 9: 15578 9: 543 10: 27966 10: 3746 11: 34168 11: 4938 40000 12: 40156 12: 5160 13: 52356 13: 9175 14: 61929 14: 12743 15: 67412 15: 15198

N 16: 60385 16: 13011 17: 66737 17: 16799 30000 18: 65649 18: 18709 19: 61799 19: 20409 20: 40883 20: 14988 21: 21798 21: 8289 22: 12428 22: 5079 Total: 660490 Total: 150875 20000

10000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Number of visits

Figure 3.3: Distribution of sources per number of visits. In purple color is shown the number of sources indistinctly from its visit’s SNR and, with red color, the number of sources with all visits with SNR above 3. In contrast to Figure 3.2, the SNR referes to MGCK objects individual visits’ SNR and not to GCK catalog object’s SNR. The inset gives the exact numbers.

34 Histogram of visits by magnitude 106

105

104 N

103

102 12 14 16 18 20 22 24 NUV magnitude

Figure 3.4: Distribution of visits by its NUV magnitude. The red line indicates visits with a SNR equal or greater to 3.

Chapter 4

Sources with Signiﬁcant Variability

The first application of the MGCK catalog that we decided to make was the search of sources with significant variability. The algorithm created for this objective is described in the following section; then we discuss the identification of sources with artifacts and systematics errors. Illustrative examples of sources with intrinsic significant variability are described in the last sections of this chapter and include variable stars, flare events and objects of unknown nature. In the cases where Kepler light curve was available, we show it simultaneously with the GALEX light curve. The reduced Kepler data used here were collected from the MAST webpage1. Having identified MGCK Objects with Significant Variability (MOSVs), the next step was to classify the sources. For convenience, this was done by visual inspection only for the brightest sources, that is, those with GCK UV magnitude below 20, which are 135 objects. For the objectives of this work, the number of examples shown in sections 4.2 and 4.3 is enough to illustrate the variety of fluctuating objects that can be studied with MGCK.

4.1 Variability detection algorithm

Examining by eye all 660,400 sources included in the MGCK catalog was, of course, not a viable option for the detection of sources with significant variability, i.e. sources in which the difference between the maximum and the minimum flux of the light curve is several times the typical error. We developed a Variability Detection Algorithm (VDA) for that goal, bearing in mind that the average number of visits per object is 17 and are separated by an average time span of 5 hours (0.208 JD), being 1.18 hours (0.049 JD) the shortest span and 4.79 JD the longest. For each source, the VDA first calculates the Median Absolute Deviation (MAD) of the fluxes dataset in the following way:

MAD = median(|Xi − median(X)|) , (4.1)

1https://archive.stsci.edu/kepler/

37 where X is the fluxes dataset and Xi is a visit’s flux. Then the relative MAD deviation of each visit was calculated: MAD MAD = i , (4.2) ri MAD where MADi = |Xi − median(X)| , (4.3) is the absolute deviation from the median flux for each visit. Those visits with a MADri value bigger than 10 were marked as “visit with significant variability”. This analysis resulted in 31,865 MGCK objects with at least one significant visit.

4.1.1 Discrimination of artifacts and systematic errors

We inspected the GALEX images of a sub-sample of these (those with mNUV ≤ 20) sources by eye and it was quickly evident that the variability of some sources was caused by artifacts or other errors present in the GALEX images. We detected that some artifacts are extended and affect several sources, in a way that mark a trend in their flux variability. This behavior can be seen in Figure 4.1; in the fifth visit an artifact affects two sources, while this is not the case for the rest of the visits. The image artifact produces a spurious increase of the flux in the two sources, that the VDA can catalog as a variable. We, therefore, developed an algorithm to detect and discard these cases. Note, however, that the algorithm can not detect spurious variability caused by an image artifact that affects a single source, as is seen in Figure 4.2.

Vis.:01 Vis.:02 Vis.:03 Vis.:04 J19341223+423348421 visits SNR.:2.19 SNR.:4.36 SNR.:5.69 SNR.:4.33 SNR.:18.98s09i11

SNR.:11.1Vis.:05 SNR.:4.93Vis.:10 SNR.:3.99Vis.:11 SNR.:4.56Vis.:12 SNR.:3.29Vis.:13

SNR.:4.17Vis.:14 SNR.:3.2Vis.:16 SNR.:2.43Vis.:17 SNR.:1.93Vis.:18 SNR.:3.74Vis.:19

SNR.:4.31Vis.:20 SNR.:2.21Vis.:21 SNR.:3.29Vis.:22 SNR.:5.11Vis.:23 SNR.:3.14Vis.:24

SNR.:3.53Vis.:25 SNR.:2.41Vis.:26

Figure 4.1: GALEX images for source GCK J19341223 +4233484. The ﬁrst box in the upper left is the GCK coadded image of the following GALEX images; the labels indicate the source name, number of visits and the S/N in the GCK coadded image. The rest of the boxes correspond to the GALEX visits; and the labels indicate the visit number and its MGCK SNR. The central red ellipse is the integration ﬂux area and the others are the nearest sources within a 2 arcmin radius.

38 J18551535+4007383 Vis.:01 Vis.:02 Vis.:03 Vis.:04 22 visits SNR.:15.93 SNR.:19.15 SNR.:18.68 SNR.:14.69 s01i06 SNR.:98.26

Vis.:05 Vis.:06 Vis.:10 Vis.:11 Vis.:12 SNR.:16.13 SNR.:15.46 SNR.:19.12 SNR.:22.6 SNR.:15.57

Vis.:13 Vis.:15 Vis.:16 Vis.:17 Vis.:18 SNR.:22.15 SNR.:22.68 SNR.:23.17 SNR.:22.21 SNR.:46.22

Vis.:19 Vis.:20 Vis.:21 Vis.:22 Vis.:23 SNR.:22.99 SNR.:23.63 SNR.:22.1 SNR.:22.05 SNR.:18.32

Vis.:24 Vis.:25 Vis.:26 SNR.:15.21 SNR.:15.67 SNR.:16.21

Figure 4.2: Same as in Fig. 4.1 for source with an artifact in visit 18 (see Fig. 4.4).

To spot false positives caused by these trends, we compared the normalized flux (to the average value) of the object of interest with that of surrounding objects. If two consecutive visits value increased or decreased in all surrounding objects, it most prob- ably means that the variable flux is due to artifacts. Therefore, we have a false positive if the variability coincides with the significant flux variation visit, i.e. all surrounding objects also present this variation in the same date. We have developed an algorithm to identify the above mentioned false positives. The implement method searches sources within 2 arcmin from the object of interest (or the 5 nearest sources if there weren’t enough within that distance) and compared its normalized flux values with each other and the MOSV’s flux. For all contemporary visits of the surrounding objects, we calculated the normalized flux average and the standard deviation. If the visit with significative variability is outside 2σ, it is safe to assume its variation is intrinsic. In Fig. 4.3 we show the case of source GCK J18530452 +4712041. The red thick line is the MOSV light curve, the colored lines are the surrounding objects’ light curves, the black line is the mean normalized flux and the dotted line its standard deviation. We can see how most of the surrounding objects vary very little with the exception of the object denoted by the blue curve located at 1.57 arcmin from the variable source. It is important to note, however, that the surrounding source is very faint and the apparent variability is not real. After discarding all the objects that presented instrumental artifacts, the final sample of MOSVs consists of 5,592 objects presenting intrinsic variability.

39 GCK J18530452+4712041 0 000 0-0.324 1.04 / -0.609 1.29 / 0.006 1.33 / -0.201 1.46 / -0.436 1.57 / -0.309 1.87 / -0.499 1.99 / 0.184 2.0 / -0.101 Mean value

Normalized flux Normalized

0 0 0 0 0 0 Time (JD)

Figure 4.3: Normalized light curves for the object of interest (red, thick line) and its surrounding objects within 2 arcmin (colored lines). The black continuous line is the mean normalized ﬂux value for all sources per visit, without taking into account the object of interest; the dotted line represents 2 standard deviations. Near the 40th day, the Signiﬁcant Visit can be seen exceeding this line.

4.1.2 False positives

Despite being discriminated by the VDA, some MOSVs are still false positives due to the nature that causes its flux variation. Two furhter types of false positives where identified: (1) sources affected by and image artifact that did not affect other sources and (2) sources that are located at the border of the images. The first case of false positive is commonly caused by the presence of narrow donut-shaped spot overlapped with the source in one or more visits, producing a brightness increase in the light curve. This flare-like variation is detected by the VDA but not dismissed as an error in the previous phase because it doesn’t affect more than one source. An example is shown in figure 4.4 for source GCK J18551535+4007383, in which the 18th visit is identified as Significant Visit, but by inspecting the GALEX images (Figure 4.2), an artifact can be seen on it; the same artifact also appears in the 20th visit, but does not affect the integrated flux value.

40 GCK J18551535+4007383 -

) 2

Flux (erg/s/cm Flux

- - - - - - Time (JD)

Figure 4.4: UV Light curve of source with an artifact. The vertical axis indicates the Flux in erg/s/cm2 (note the offset) and the horizontal axis represents the date in Julian Days. The zero point of the Julian days scale is set by the visit in which signiﬁcant variability was found. The red line represents the average value, the broken and dotted line represent one and two standard deviations, respectively. A bright star in the same image may cause a donut shaped artifact overlapped with a source in a visit (visit 18 in image 4.2). This increase in luminosity produce a light curve with a ﬂare-like variation and is thus detected by the algorithm, which is a false positive.

The second kind of systematic error is caused by the position of the source in the tile: if it falls near the edge, it is possible that, in one or more visits, the inte- grating radius includes area with missing data and causes a drop in the flux. This transit-like variation is detected by the VDA, but not discarded when it does not affect more than one visit. An example is shown in figures 4.5 and 4.6 for source GCK J19460924+3951504, in which the 5th visit clearly corresponds to an incomplete field. From the 135 inspected sources, 55 were false positives: 39 due to artifacts and 16 due to incomplete fields.

41 GCK J19460924+3951504 -

) 2

Flux (erg/s/cm Flux

- - Time (JD)

Figure 4.5: Light curve (as in Fig. 4.4) of a source with an incomplete ﬁeld. A source that falls on the border of an image may be in an incomplete ﬁeld in one of its visits which may produce a light curve with a transit-like variation. The GALEX images of this source are shown in 4.6.

J19460924+3951504 Vis.:01 Vis.:02 Vis.:03 Vis.:04 20 visits SNR.:25.1 SNR.:26.53 SNR.:28.18 SNR.:26.7 s09i14 SNR.:101.94

Vis.:05 Vis.:07 Vis.:10 Vis.:11 Vis.:13 SNR.:17.22 SNR.:28.01 SNR.:27.1 SNR.:27.08 SNR.:29.26

Vis.:14 Vis.:15 Vis.:16 Vis.:17 Vis.:18 SNR.:26.32 SNR.:27.49 SNR.:25.75 SNR.:25.62 SNR.:26.64

Vis.:19 Vis.:20 Vis.:22 Vis.:24 Vis.:25 SNR.:27.32 SNR.:24.09 SNR.:24.8 SNR.:25.92 SNR.:24.39

Vis.:26 SNR.:25.39

Figure 4.6: Same as in Fig. 4.1 for a source with an incomplete field as can be observed in visit 5. Visit 25 also presents an incomplete field but doesn’t affect the flux calculated in the area inside the ellipse.

42 GCK J20050418+4453299 -

) 2

Flux (erg/s/cm Flux

- - - - - - - - Time (JD)

Figure 4.7: Unresolved adjacent sources light curve (as in Fig. 4.4). The GALEX images of this source are shown in 4.8.

We have identified a third phenomenon that can create false variability to a source in the catalog. It happens when SExtractor algorithm cannot distinguish among two very nearby objects and only one is the source of variable flux. It may happen in a very crowded field, but it also happens when there is a sudden appearance of a UV source, which can be present in only one or few images. An example of this event is shown in figures 4.7 and 4.8. Work is in progress to update the algorithm so it can detect double or multiple nearby sources.

Vis.:01 Vis.:02 Vis.:03 Vis.:09 J20050418+4453299 SNR.:12.67 SNR.:12.68 SNR.:13.95 SNR.:13.46 17 visits s15i09 SNR.:111.45

Vis.:11 Vis.:12 Vis.:15 Vis.:16 Vis.:18 SNR.:11.52 SNR.:13.74 SNR.:14.49 SNR.:14.66 SNR.:14.31

Vis.:19 Vis.:20 Vis.:21 Vis.:22 Vis.:23 SNR.:11.66 SNR.:11.43 SNR.:13.32 SNR.:14.94 SNR.:13.01

Vis.:26 Vis.:28 Vis.:29 SNR.:13.05 SNR.:87.39 SNR.:63.16

Figure 4.8: Same as in Fig. 4.1 for unresolved adjacent sources.

43 4.2 Examples of UV variable sources

Some of the sources we found with the VDA were well known variable stars. These sources are useful to assert the reliability of our catalog. We describe some examples below. In what follows we also make use of the simultaneous optical photometry of the Kepler space telescope.

4.2.1 Eclipsing binary system A good example of variability is an eclipsing binary. Its variation is due to mutual eclipses in the line of sight of the observer, when one of its components passes in front of the other. As an illustrative example we show in Fig. 4.9 the case of V481 Lyr, an Algol type star, whose variation in the visible and UV appear well correlated.

KIC 7938468 - GCK J18474643+4343414

Normalized flux Normalized

GALEX Kepler − − − Time (BJD)

Figure 4.9: UV (blue points) and Visible (red line) normalized light curves for the eclipsing binary of Algol type V481 Lyr (detached). The left axis indicates the normalized ﬂux. The horizontal axis corresponds to the date in Barycentric Julian Days, the day 0 matches one of the UV variations of interest.

4.2.2 Cepheid stars The second object that we analyzed is the Cepheid star V1154, GCK J194815 +430736, the only conﬁrmed Cepheid in the Kepler ﬁeld, it was observed by Kepler in long cadence (∼30 min) and have a period of ∼4.925 days (Derekas et al. 2016). Cepheids are pulsating stars that dim and brighten as it surface expands and contracts within a

44 short period of time, following a specific pattern2. In figure 4.10 we compare GALEX and Kepler light curves, which show a correlation between the visible and the UV variations. However, the UV relative flux variation is much larger with respect to the optical flux.

KIC 7548061 - GCK J19481547+4307366 GALEX Kepler

2.0

1.5 Normalized flux Normalized

1.0

0 10 20 30 40 Time (BJD) +2.4561444141e6

Figure 4.10: Cepheid Cygni V1154 GALEX-Kepler light curve (as in Fig. 4.9).

4.2.3 Cataclysmic variable V344 Lyr (GCK J18443923 +4322283), is a Dwarf Nova3, a subclass of Cataclysmic Variables, which display recurrent outbursts in the Kepler light curve as well as in the MGCK light curve. Cannizzo et al. (2012) indicate the presence of superoutburst after the occurrence of several outbursts, the former have a duration longer than 10 days meanwhile the latter are shorter. In ﬁgure 4.11 we appreciate the excellent consistency of the UV and visible light curve, both in the outburst phase and in the quiescent state. The second point of the MGCK light curve indicates the presence of an outburst not observed by Kepler. In Fig. 4.12 we show the star’s ﬂux variation through the GALEX observations.

2http://www.space.com/15396-variable-stars.html 3According to SIMBAD astronomical database: http://simbad.u-strasbg.fr/simbad/sim- id?Ident=KIC+7659570&NbIdent=1&Radius=2&Radius.unit=arcmin&submit=submit+id

45 KIC 7659570 - GCK J18443923+4322283 40 3.5

35 3.0

30 2.5

2.0 GALEX 20 Kepler 1.5 normalized flux normalized

15 flux normalized

1.0

10 Kepler GALEX

5 0.5

0 0.0 0 10 20 30 40 Time (BJD) +2.4561448184e6

Figure 4.11: Similar to Fig. 4.9 for Dwarf Nova V344, but left and right axis indicates the normalized ﬂux for GALEX and Kepler, respectively.

J18443923+4322283 Vis.:01 Vis.:02 Vis.:04 Vis.:06 18s01i02 visits

Vis.:10 Vis.:13 Vis.:15 Vis.:16 Vis.:17

Vis.:18 Vis.:19 Vis.:20 Vis.:21 Vis.:22

Vis.:23 Vis.:24 Vis.:25 Vis.:26

Figure 4.12: Same as in Fig. 4.1 for Dwarf Nova V344.

4.2.4 Flare events Another kind of UV variable sources are those that show ﬂare events, and they emerge, usually in a single GALEX visit, as a source with a ﬂux several times larger than the median value of the rest of the visits.

46 The presence of a UV flare agrees very well with an increase in the optical flux. It is important to note that Kepler optical data, which have a ∼30 min long cadence provides a much better sampling than GALEX. The visible relative variation is not as prominent as the UV, and the visible events in most of the cases present a typical flare shape previously observed in Kepler’s flare star candidates: a steep increase followed by a longer exponential-like decrease, as can be seen in the light curve 4.13 from Shibayama et al. (2013) of the star KIC 4245449. From the following examples, Davenport (2016) has reported flares in sources KIC 7459173, KIC 6421483, KIC 8481420, KIC 3324644, KIC 6205460 and KIC 12121936; meanwhile, Shibayama et al. (2013) have reported superflares in KIC 4449749. KIC 9237305 has not been previously reported with flares. None had before been observed at UV wavelengths.

Figure 4.13: A super ﬂare event in star KIC 424559. The complete Kepler light curve is shown in panel (a) and a zoom of the ﬂare in panel (b). Image taken from Shibayama et al. (2013)

In figure 4.14 we show the light curve of a rotational variable star, KIC 7459173, which presents several flare events in its visible data. One of the GALEX visits coincides with one of these events. In the visible the flux varies about 2% meanwhile in the UV, it shows a difference of 400%. Other examples of stars presenting flare events are shown in Fig. 4.15, like KIC 6421483 an eclipsing binary of Algol type, which presents a unique flare event, whit a flux increase of ∼250% in UV and ∼3% in Visible. In Fig. 4.16, we depict the case of KIC 8481420, a proposed Classical Cepheid by Debosscher et al. (2011), that shows a couple of peaks in its visible light curve with about the same variability as the previous example. In Fig. 4.17, we show KIC 3324644, another Classical Cepheid which also shows an increment in the UV flux, that coincides with a tiny visible bump in a decrement of its periodic variation in the visible light curve. In Fig. 4.18, KIC 4449749, a rotationally variable star shows little visible variation of about 1%, meanwhile in the UV varies ∼1300%, presenting the largest relative variation in our sample. In Fig. 4.19, KIC 6205460, an eclipsing binary of Algol type also shows variations besides its ocultations. In this case, the UV varies ∼100% its

47 median value. In Fig. 4.20, KIC 12121936, a rotationally variable star shows an almost imperceptible visible variation of ∼0.3%, meanwhile in the UV, it varies ∼200%. Finally, in Fig. 4.21, KIC 9237305 shows a visible variation of ∼1.5% have a counter UV counterpart that varies ∼250%.

KIC 7459173 - GCK J19425934+4300549 11 GALEX 1 5 11 Kepler 4 3 1

2 1 11 Kepler GALEX 1 0 -0.3 -0.2 -0.1 0 0.1 0.2 11 normalized flux normalized 1 flux normalized Kepler GALEX

1 1

1 1 Time (BJD) 11

Figure 4.14: Similar to 4.11 for UV ﬂare star candidate GCK J19425934+4300549. In the upper left inserted window this event is zoomed in with the same scales as the left and right axis.

KIC 6421483 - GCK J18521101+4151358 GALEX Kepler Kepler

GALEX - - -

Normalized flux Normalized

Time (BJD)

Figure 4.15: Similar to 4.9 for UV star with ﬂare event candidate GCK J18521101 +4151358. In the upper left inserted window this event is zoomed in ,with left and right axis indicating GALEX and Kepler normalized ﬂux, respectively. The variation in the UV (∼300%) is greater than the Visible (∼6%).

48 KIC 8481420 - GCK J19015310+4431024 GALEX Kepler

Kepler GALEX - - -

Normalized flux Normalized

Time (BJD)

Figure 4.16: Same as in 4.15 for UV ﬂare star candidate GCK J19015310+4431024.

KIC 3324644 - GCK J19033325+3829122 GALEX Kepler Kepler GALEX - - -

Normalized flux Normalized

Time (BJD)

Figure 4.17: Same as in 4.15 for UV ﬂare star candidate GCK J19033325+3829122.

49 KIC 4449749 - GCK J19072524+3932075 14 0 GALEX 0 12 Kepler 0 10 8 0 00 6 0 Kepler

GALEX 4 0 0 2 0 0 0 -0.3 -0.2 -0.1 0 0.1 0.2

Normalized flux Normalized

0 0 0 0 0 0 Time (BJD) 0

Figure 4.18: Same as in 4.15 for UV ﬂare star candidate GCK J19072524+3932075.

KIC 6205460 - GCK J19275528+4133332 0 GALEX Kepler

0 0 Normalized flux Normalized 0 00 00

Kepler GALEX 0

00 -0.5-0.4-0.3-0.2-0.1 0 0.10.20.30.4 0 0 0 0 0 0 Time (BJD)

Figure 4.19: Same as in 4.15 for UV ﬂare star candidate GCK J19275528+4133332.

50 KIC 12121936 - GCK J19480148+5040354 GALEX Kepler

Kepler GALEX - -

Normalized flux Normalized

Time (BJD)

Figure 4.20: Same as in 4.15 for UV ﬂare star candidate GCK J19480148+5040354.

KIC 9237305 - GCK J19525961+4540028

GALEX Kepler Kepler GALEX

- - -

Normalized flux Normalized

Time (BJD)

Figure 4.21: Same as in 4.15 for UV ﬂare star candidate GCK J19525961+4540028.

51 4.3 Examples of UV variable sources of unidentiﬁed nature

Some of the UV variable sources we found are not cataloged in SIMBAD (Wenger et al. 2000) or in the NASA/IPAC Extragalactic Database4 (NED). These objects were not of Kepler interest or fell between the CCD’s gaps, but were observed by GALEX. The following are some examples of unidentiﬁed sources that present interesting behaviors.

4.3.1 One visit event source The first kind of variation we found were sources whose flux was higher than the noise in only one visit. The flux in these single visits can be very high, for example a UV magnitude of 17.84 for the object GCK J19591722+4459129, meanwhile in the rest of the visits the flux is almost zero or zero as can be seen in figures 4.22 and 4.23. That means the flux of only one integration was sufficient to be detected by SExtractor in the coadded GCK catalog. These detections may be produced by outbursts in extragalactic sources.

GCK J19591722+4459129 -

) 2

Flux (erg/s/cm Flux

- - Time (JD)

Figure 4.22: Light curve (as in Fig. 4.4) for one visit event source.

4The NASA/IPAC Extragalactic Database (NED) is operated by the Jet Propulsion Laboratory, Cal- ifornia Institute of Technology, under contract with the National Aeronautics and Space Administration.

52 Vis.:01 Vis.:02 Vis.:03 Vis.:04 J19591722+4459129 21 visits SNR.:3.95 SNR.:2.96 SNR.:2.08 SNR.:1.39 s14i08 SNR.:120.37

Vis.:05 Vis.:06 Vis.:10 Vis.:11 Vis.:13 SNR.:148.29 SNR.:1.6 SNR.:0.72 SNR.:1.94 SNR.:1.39

Vis.:14 Vis.:16 Vis.:17 Vis.:18 Vis.:19 SNR.:1.76 SNR.:1.58 SNR.:0.17 SNR.:1.14 SNR.:1.11

Vis.:20 Vis.:21 Vis.:22 Vis.:23 Vis.:24 SNR.:2.17 SNR.:0.68 SNR.:1.52 SNR.:1.83 SNR.:2.63

Vis.:25 Vis.:26 SNR.:0.53 SNR.:1.74

Figure 4.23: Same as 4.1 for one visit event source.

4.3.2 Outburst variable

The light curve of source GCK J19132680+4852312 in Fig. 4.24 is interesting because the ﬂux increases in ∼9 days and then decreases to the previous low level state in a similar period of time. Its GALEX images are shown in Fig. 4.25. This source is further discussed in Chapter 5.

GCK J19132680+4852312 -

) 2

Flux (erg/s/cm Flux

- - - - Time (JD)

Figure 4.24: Light curve (as in Fig. 4.4) of Source with lasting outburst.

53 Vis.:01 Vis.:02 Vis.:04 Vis.:05 J19132680+4852312 19 visits s09i03

Vis.:07 Vis.:10 Vis.:11 Vis.:12 Vis.:13

Vis.:15 Vis.:16 Vis.:18 Vis.:20 Vis.:21

Vis.:22 Vis.:23 Vis.:24 Vis.:25 Vis.:26

Figure 4.25: Same as in Fig. 4.1 for source with lasting outburst.

4.3.3 Sources showing ﬂare-like events

In this subsection, we illustrate those sources that show a relative stable flux and one or more increment features which usually do not last more than a few visits. We refer to these variations as flare-like events, for their morphology, as their nature is not investigated yet. In Fig. 4.26 we see the light curve of GCK 19345575 +4025510, which presents an outburst in the second to third visits, the time span between them suggests this may not be the maximum flux reached, and another one with a unique point, after which the flux returns to its median value. The GALEX images in Fig. 4.27 corroborates this behavior.

54 GCK J19345575+4025510 -

) 2

Flux (erg/s/cm Flux

- - - - - - Time (JD)

Figure 4.26: Light curve (as in Fig. 4.4) of unidentiﬁed variable source with increment variations.

Vis.:01 Vis.:02 Vis.:03 Vis.:04 J19345575+4025510 19 visits s08i12

Vis.:05 Vis.:07 Vis.:11 Vis.:12 Vis.:13

Vis.:14 Vis.:15 Vis.:16 Vis.:17 Vis.:18

Vis.:19 Vis.:20 Vis.:21 Vis.:22 Vis.:25

Figure 4.27: Same as in Fig. 4.1 for unidentiﬁed variable source with ﬂare like variations.

4.3.4 Sources showing transit-like events Another kind of variations we found were those which showed a relatively stable flux and one or several decrement variations, as can be seen in figures 4.28 and 4.29, for GCK J19534298 +4452094. We, therefore, refer to these variations as transit-like events, as they recall how an obstructing source would be seen; but these, of course, are not periodic to the extent that our observations allow us to say. The first, fourth and

55 ﬁfth visits have about 0.4 of the average ﬂux value. The same case occurs with source GCK J19541531 +4629559 (Fig. 4.30 and 4.31) with a difference of approximate 0.2.

GCK J19534298+4452094 -

) 2

Flux (erg/s/cm Flux

- - Time (JD)

Figure 4.28: The light curve (as in Fig. 4.4) of an unidentiﬁed source with transit like variations.

Vis.:01 Vis.:02 Vis.:03 Vis.:04 J19534298+4452094 SNR.:4.16 SNR.:16.11 SNR.:15.14 SNR.:7.42 17 visits s13i07 SNR.:54.51

Vis.:05 Vis.:09 Vis.:10 Vis.:11 Vis.:12 SNR.:4.33 SNR.:15.64 SNR.:16.33 SNR.:12.2 SNR.:12.1

Vis.:13 Vis.:15 Vis.:16 Vis.:17 Vis.:18 SNR.:16.53 SNR.:9.83 SNR.:15.35 SNR.:14.24 SNR.:13.92

Vis.:19 Vis.:21 Vis.:22 SNR.:14.83 SNR.:15.31 SNR.:14.83

Figure 4.29: Same as in Fig. 4.1 for unidentiﬁed variable source with transit like variations.

56 GCK J19541532+4629559 -

) 2

Flux (erg/s/cm Flux

- - - - - - - Time (JD)

Figure 4.30: The light curve (as in Fig. 4.4) of one unidentiﬁed source.

Vis.:01 Vis.:02 Vis.:03 Vis.:05 J19541532+4629559 SNR.:10.46 SNR.:10.44 SNR.:11.35 SNR.:6.67 19 visits s14i06 SNR.:41.04

Vis.:06 Vis.:10 Vis.:11 Vis.:14 Vis.:16 SNR.:7.78 SNR.:10.19 SNR.:10.36 SNR.:10.78 SNR.:9.86

Vis.:17 Vis.:18 Vis.:19 Vis.:20 Vis.:21 SNR.:10.03 SNR.:9.96 SNR.:2.71 SNR.:9.98 SNR.:8.69

Vis.:22 Vis.:23 Vis.:24 Vis.:25 Vis.:26 SNR.:9.69 SNR.:9.52 SNR.:9.86 SNR.:10.25 SNR.:9.36

Figure 4.31: Same as in Fig. 4.1 for unidentiﬁed variable source with transit like variations.

4.3.5 UV variable - Visible stable source Another interesting case is where a variation in UV is present, while the optical Ke- pler’s light curve doesn’t show any signiﬁcative variation. This is the case for GCK J20034690 +4446429 or KIC 8655053, as can be seen in Fig. 4.32, the Kepler light curve is steady in a normalized ﬂux value, meanwhile the 10th GALEX visit shows an

57 increment of 1300% the median value. Nevertheless, in the GALEX images (Fig. 4.33), it is very clear that the UV outburst is present.

KIC 8655053 - GCK J20034690+4446429 GALEX Kepler Kepler

GALEX - - -

Normalized flux Normalized

Time (BJD)

Figure 4.32: Same as in Fig. 4.15 for star GCK J20034690 +4446429.

Vis.:01 Vis.:02 Vis.:03 Vis.:09 J20034690+4446429 SNR.:4.3 SNR.:4.84 SNR.:6.32 SNR.:5.31 17 visits s15i09 SNR.:32.18

Vis.:11 Vis.:12 Vis.:15 Vis.:16 Vis.:18 SNR.:4.97 SNR.:3.66 SNR.:5.04 SNR.:6.31 SNR.:4.97

Vis.:19 Vis.:20 Vis.:21 Vis.:22 Vis.:23 SNR.:30.03 SNR.:4.66 SNR.:6.84 SNR.:5.19 SNR.:4.69

Vis.:26 Vis.:28 Vis.:29 SNR.:3.99 SNR.:4.06 SNR.:6.17

Figure 4.33: Same as in Fig. 4.1 for star GCK J20034690 +4446429, which presents UV variation without visible counterpart.

58 Chapter 5

Optical spectroscopic follow-up of MGCK sources of unknown nature

Many sources of the MGCK catalog that show signiﬁcant UV variability are not yet present in any other catalog or they have just been known to be emitters in some region of the electromagnetic spectrum (eg, X-ray source, IR source, etc). In order to understand the nature of a small selected sample of this kind of sources, an observational proposal was submitted to the Observatorio Astrof´ısico Guillermo Haro (Guillermo Haro National Observatory, OAGH) located in Sierra la Mariquita, Cananea, Sonora. Four nights were granted, from July 24th to 27th 2016, of which only the 25th had weather conditions that allowed observations. From the 26 proposed sources, we could observe just 4. We carried out the spectroscopic observations with the 2.1-meter telescope and the spectrograph Boller¨ & Chivens, with a 150 l/mm grating and a 250 µm aperture slit. This setup allowed to observe the 4500 - 7300 A˚ spectral range and to achieve a spectral resolution of ∼2.35 A˚ FWHM, and a spectral dispersion of ∼0.7 A˚ /pix.A He-Ar lamp was used for the wavelength calibration.

5.1 Spectroscopic data reduction

To reduce the spectroscopic images, we followed the standard reduction process using the Image Reduction and Analysis Facility (IRAF)1 software. For each of the four observed sources, we had at least three images, most of them with an integration time of 30 minutes. The first step was to crop the image to the region of interest. Then the cosmic rays were manually removed leaving the images as clean as possible. The bias images were then combined into one image which was used as the readout noise and substracted. The following step was to combine the flat field images into one image, which is used to correct the images by the detector efficiency variations. The following steps were to identify the lamp’s lines and then to carry out the

1Webpage: http://iraf.noao.edu

59 wavelength calibration and the background subtraction, for eliminating the sky contribution, from the transversal axis. Then the spectrum was extracted using the IRAF’s apall function. In this unidimensional spectrum, we carefully eliminated the remaining cosmic rays that fell over the spectrum, which are present like prominent singular shaped peaks. Finally, the ﬂux calibration was performed through the creation of a sensibility curve from the standard star observation and then applied to each of the objects images.

5.2 Spectrum and object’s nature inference

The only object whose spectrum achieved a sufficient signal-to-noise to carry on the analysis was GCK J19132680 +4852312. Its final spectrum, flux and wavelength cali- brated, is shown in Fig. 5.1. It presents strong emission lines of hydrogen, and helium features can be noted too. After analyzing its characteristics and compare it to many reference spectra, we propose the GCK J19132680 +4852312 to be a binary star system, known as Cataclysmic Variable (CV). A CV is composed of a white dwarf (primary star), which accretes material from a companion star (secondary star) that fills the Roche lobe (see Fig. 5.2). The material forms an accretion disk around the primary star and, due to thermal instability, quasi-periodic outburst (Breedt et al. 2014) are produced.

2.5 Hβ 4861 6562 ) 1 H FeI 5322 FeI HeI 4921 HeI 5015 HeI HeI 5876 HeI HeI 6678 HeI FeII 5169 FeII 2.0 4686 HeII Å

1 s

2 cm 1.5 (erg

15 10 × 1.0 Flux Flux

0.5 4500 5000 5500 6000 6500 7000 Wavelength (Å)

Figure 5.1: GCK J19132680+4852312 Spectrum observed in the OAGH.

According to Breedt et al. (2014), the CV outburst increases the optical luminosity by 2-6 magnitudes in periods as short as one day, and Campbell (1940) identiﬁed and

60 Figure 5.2: Cataclysmic variable artist’s impression. A cataclysmic variable star is a binary system composed of a white dwarf (bottom) which accretes material from a companion star. Image taken from internet2. classified CV’s outbursts that last up to ten days. In the UV, our light curve shows an outburst that is in agreement with this time interval, as can be seen in Fig. 4.24, where the burst lasts about 15 days. Warner (1995) defines the spectra of a Cataclysmic Variable of Dwarf Nova type at minimum light or quiescence to be characterized by strong Balmer emission lines, with other usually weaker lines of HeI and a few from heavier elements. We compared the spectrum of GCK J19132680 +4852312 with the spectral catalog of cataclysmic variables by Breedt et al. (2014), and found a resemblance to the spectrum of the CV CRTS0438+0040 (Fig. 5.3). This source is an eclipsing system with high inclination. Apart of the overall agreement in the continuum slope, the spectrum of GCK J19132680 +4852312 also presents Hβ and Hα in emission, a strong HeI at λ5876 A,˚ and indi- cation of the presence of other lines in emission around λ5000 A,˚ and at λ6678 A.˚ This result indicates that, while our light curve shows a strong outburst, the spectroscopic observation was carried during a quiescent phase. Another interesting feature of our spectrum is the HeII λ4686 emission line which is not strong as in the spectrum of CRTS0438+0040; Bonnet-Bidaud et al. (2001) consider that this is a property of systems with low magnetic field. We therefore conclude that it is safe to consider GCK J19132680+4852312 to be a Cataclysmic Variable candidate in accordance to the light curve and the spectra. Further photometric observations are needed to determine the frequency of the outbursts and the behavior of the system, as well as spectroscopic observations to improve the SNR and confirm its classification.

61 Figure 5.3: Optical spectrum of the Cataclysmic Variable CRTS0438+0040 from the Catalina Real-time Transient Survey (CRTS). The light gray vertical lines indicate the rest wavelengths of hydrogen Balmer lines (solid lines), the helium I and II lines (dashed lines) and the calcium triplet (dotted lines). Image and text from Breedt et al. (2014)

62 Chapter 6

Conclusions

In this work, we presented the MGCK catalog, a collection of UV multi-visit observations performed by GALEX through 45 days from August 2012 to September 2012. The catalog entirely covers the Kepler ﬁeld and its observation were carried out simultaneously to the Kepler space telescope. It includes 660,490 light curves of sources with a depth limit of 21 NUV magnitudes.

To build the catalog we used a similar data reduction process as described in Olmedo et al. (2015) for GCK catalog, but performing the photometric measurements for each tile in each visit.

We developed a Variability Detection Algorithm to detect UV variable objects and found 5,592 variable sources, which varies by a factor more than 3 σ.

We present the light curves of a sample of representative variable sources of different kind of objects, as, e.g., individual variable stars, interacting and eclipsing binary systems and a few examples of objects of unknown nature.

We carried out the spectroscopic follow-up of one of these latter sources (GCK J19132680 +4852312) and suggested that is a cataclysmic variable of dwarf nova type.

This catalog provides an opportunity to study UV-Visible flux variability for a large number of objects of many different classes. It represents an exceptional tool to study the UV variability in stellar systems, since it covers one of the most studied Galactic stellar field and it usefully complements all existing data at other wavelengths. In particular, it complements the Kepler light curves of 119,076 stars in the field.

The wealth of high-quality data that the MGCK catalog provides will allow a multitude of studies. We illustrate here a few examples of future works:

• We will study the NUV variability of the stars hosting planets and of the whole

63 KOI sample. It is important to study the level of the UV variability of the stellar emission of these samples since it can affect the atmospheres of the surrounding planets and, therefore, also affect their possible habitability.

• We will also study the variability of solar analogs (spectral type G0-G3 in the main sequence evolutionary state) to determine the distribution of the intensity of stellar activity.

• We will analyze the NUV light curves of variable stars of different classes, such as δ Scuti and γ Doradus.

• We will consider to perform the optical spectroscopic follow-up of sources that show interesting morphologies of the NUV light curve and whose nature is still unknown. We will ask for observing time at the OAGH and the SPM Observatory.

• We will upgrade the VDA algorithm in order to detect low-intensity irregulars ﬂare- or transit-like events in the GALEX data. We will achieve this goal by taking into account and remove the possible periodic variability of the sources that can be obtained by the analysis of the optical Kepler light curves.

64 List of Figures

1.1 Variable stars classiﬁcation diagram ...... 3 1.2 HR diagram showing the position of variable sources ...... 4

2.1 Planet transit example ...... 8 2.2 Kepler space telescope ...... 9 2.3 Kepler’s orbit...... 11 2.4 Kepler pointing direction in solar neighborhood ...... 12 2.5 Kepler Field of View...... 13 2.6 K2 Campaings ...... 14 2.7 GALEX instument cross section...... 16 2.8 GALEX CAUSE Galactic Plane Coverage...... 17 2.9 Intensity image mosaic of GALEX CAUSE NUV observations . . . . . 21 2.10 GCK Scan mode observations ...... 22 2.11 GCK NUV Detections distribution ...... 22

3.1 Visits integration time and date per scan and tile...... 30 3.2 Histogram of sources by GCK’s SNR ...... 33 3.3 Histogram of sources by number of visits ...... 34 3.4 Histogram of visits by NUV magnitude ...... 35

4.1 GALEX images for a source affected by an extended artifact...... 38 4.2 GALEX images of source with an artifact ...... 39 4.3 Object of interest and surrounding objects light curves ...... 40 4.4 UV light curve of source with an artifact ...... 41 4.5 Light curve of source with an incomplete field ...... 42 4.6 GALEX images of source with an incomplete field ...... 42 4.7 Source with unresolved adjacent sources light curve ...... 43 4.8 GALEX images for source with unresolved adjacent sources ...... 43 4.9 UV and visible light curves of eclipsing binary V481 Lyr ...... 44 4.10 Cepheid Cygni V1154 GALEX-Kepler light curve...... 45 4.11 Dwarf Nova V344 UV-visible light curve...... 46 4.12 GALEX images for Dwarf Nova V344 ...... 46 4.13 Kepler super flare light curve example ...... 47 4.14 Star with flare candidate GCK J194259+430054 UV-visible light curve . 48

65 4.15 Star with flare candidate GCK J185211+415135 UV-visible light curve . 48 4.16 Star with flare candidate GCK J190153+443102 UV-visible light curve . 49 4.17 Star with flare candidate GCK J190333+382912 UV-visible light curve . 49 4.18 Star with flare candidate GCK J190725+393207 UV-visible light curve . 50 4.19 Star with flare candidate GCK J192755+413333 UV-visible light curve . 50 4.20 Star with flare candidate GCK J194801+504035 UV-visible light curve . 51 4.21 Star with flare candidate GCK J195259+454002 UV-visible light curve . 51 4.22 Light curve of one visit event source ...... 52 4.23 GALEX images of one visit event source ...... 53 4.24 Light curve of source with lasting outburst ...... 53 4.25 GALEX images of source with lasting outburst ...... 54 4.26 Light curve of unidentified variable source with flare like variations . . 55 4.27 GALEX images of unidentified variable source with flare like variations 55 4.28 Light curve of unidentified variable source with transit like variations . 56 4.29 GALEX images of unidentified variable source with transit like variations 56 4.30 Light curve of unidentified variable source with transit like variations . 57 4.31 GALEX images of unidentified variable source with transit like variations 57 4.32 Light curve of UV variable source without visible counterpart . . . . . 58 4.33 GALEX images of UV variable source without visible counterpart . . . 58

5.1 GCK J19132680+4852312 Spectrum ...... 60 5.2 Cataclysmic variable artist’s impression ...... 61 5.3 Breedt et al. (2014) Cataclismic Variable espectrum ...... 62

A.1 Pandas DataFrame example ...... 69

66 List of Tables

2.1 Files of GCK dataset ...... 18

3.1 Values set for parameter keywords from the configuration file used as input to SExtractor...... 24 3.2 SExtractor error flags...... 26 3.3 GALEX CAUSE pipeline artifacts flags...... 27 3.4 MGCK keywords...... 30

A.1 Object J19132680 +4852312 DataFrame (part 1 of 8)...... 70 A.1 Object J19132680 +4852312 DataFrame (part 2 of 8)...... 70 A.1 Object J19132680 +4852312 DataFrame (part 3 of 8)...... 71 A.1 Object J19132680 +4852312 DataFrame (part 4 of 8)...... 71 A.1 Object J19132680 +4852312 DataFrame (part 5 of 8)...... 72 A.1 Object J19132680 +4852312 DataFrame (part 6 of 8)...... 72 A.1 Object J19132680 +4852312 DataFrame (part 7 of 8)...... 73 A.1 Object J19132680 +4852312 DataFrame (part 8 of 8)...... 73

Appendix A

MGCK’s DataFrame

In this section, we show an MGCK DataFrame example for source GCK J19132680 +4852312; in Figure A.1 it is as seen in the Python environment, note how we call back just 5 of all of the keywords. The source’s name and the ﬁrst two rows are the indexes, they appear always. The complete DataFrame is shown in Table A, a description of the keywords is found in Table 3.4.

Figure A.1: J19132680+4852312 DataFrame with a few keywords as seen in Python.

69 Table A.1: Object J19132680 +4852312 DataFrame (part 1 of 8).

Object Id visitvisit numberalpha_j2000 delta_j2000nuv_mag nuv_magerr nuv_flux 0 01 288.361656 48.875323 17.6244 0.056536 9.599612 1 02 288.361656 48.875323 17.1957 0.044590 14.246660 2 04 288.361656 48.875323 17.9250 0.066490 7.278054 3 05 288.361656 48.875323 17.8048 0.061205 8.129504 4 07 288.361656 48.875323 17.6790 0.058195 9.128386 5 10 288.361656 48.875323 17.0876 0.043092 15.737670 6 11 288.361656 48.875323 16.5953 0.034436 24.766800 7 12 288.361656 48.875323 15.8653 0.016711 48.514380 8 13 288.361656 48.875323 15.8747 0.016800 48.096400 J19132680+4852312 9 15 288.361656 48.875323 16.1875 0.019903 36.059150 10 16 288.361656 48.875323 16.8277 0.028361 19.995020 11 18 288.361656 48.875323 17.0927 0.031054 15.664580 12 20 288.361656 48.875323 17.3405 0.035689 12.468100 13 21 288.361656 48.875323 17.3788 0.036596 12.035660 14 22 288.361656 48.875323 17.7732 0.044719 8.370219 15 23 288.361656 48.875323 17.6517 0.058649 9.360934 16 24 288.361656 48.875323 17.5006 0.053293 10.758980 17 25 288.361656 48.875323 17.5279 0.054306 10.491530 18 26 288.361656 48.875323 17.4156 0.051486 11.634870 Continued on next frame

Table A.1: Object J19132680 +4852312 DataFrame (part 2 of 8).

visit nuv_fluxerr exp_start exp_end exp_timevariable_flagnuv_s2n nuv_bkgrnd_mag 0 0.499750 1.344099e+09 1.344099e+09 122.00 0.0 19.208845 26.735394 1 0.584947 1.344188e+09 1.344188e+09 123.00 0.0 24.355461 26.729490 2 0.445599 1.344868e+09 1.344868e+09 123.00 0.0 16.333184 26.747297 3 0.458166 1.344874e+09 1.344874e+09 123.00 0.0 17.743566 26.761279 4 0.489155 1.345193e+09 1.345193e+09 122.00 0.0 18.661526 26.777199 5 0.624471 1.345648e+09 1.345648e+09 123.00 0.0 25.201609 26.752224 6 0.785330 1.345802e+09 1.345802e+09 119.05 0.0 31.536810 26.764748 7 0.746534 1.346050e+09 1.346052e+09 245.05 1.0 64.986123 26.760629 8 0.744011 1.346169e+09 1.346170e+09 250.05 1.0 64.644727 26.756095 9 0.660849 1.346600e+09 1.346602e+09 241.05 0.0 54.564903 26.717364 10 0.522178 1.346772e+09 1.346773e+09 228.10 0.0 38.291594 26.701062 11 0.447930 1.346949e+09 1.346951e+09 230.05 0.0 34.971055 26.715494 12 0.409734 1.347032e+09 1.347033e+09 227.05 0.0 30.429706 26.706194 13 0.405576 1.347091e+09 1.347092e+09 223.05 0.0 29.675459 26.716117 14 0.344666 1.347268e+09 1.347270e+09 220.05 0.0 24.284999 26.743457 15 0.505530 1.347854e+09 1.347854e+09 118.00 0.0 18.517078 26.755018 16 0.527976 1.347996e+09 1.347996e+09 120.00 0.0 20.377769 26.756957 17 0.524638 1.348019e+09 1.348020e+09 120.00 0.0 19.997638 26.720696 18 0.551598 1.348025e+09 1.348025e+09 120.00 0.0 21.093006 26.718820 Continued on next page

70 Table A.1: Object J19132680 +4852312 DataFrame (part 3 of 8).

visit SE_flagsartifact_flagsa_image b_imagekron_radiustheta_imageerra_imageerrb_imageerrtheta_image 0 0.0 256.0 2.515 2.228 3.5 37.0 0.0119 0.0103 44.2 1 0.0 0.0 2.515 2.228 3.5 37.0 0.0118 0.0103 44.2 2 0.0 0.0 2.515 2.228 3.5 37.0 0.0118 0.0103 44.2 3 0.0 0.0 2.515 2.228 3.5 37.0 0.0119 0.0103 44.2 4 0.0 0.0 2.515 2.228 3.5 37.0 0.0119 0.0103 44.2 5 0.0 0.0 2.515 2.228 3.5 37.0 0.0119 0.0103 44.2 6 0.0 0.0 2.515 2.228 3.5 37.0 0.0119 0.0103 44.2 7 0.0 0.0 2.515 2.228 3.5 37.0 0.0114 0.0099 44.7 8 0.0 0.0 2.515 2.228 3.5 37.0 0.0114 0.0099 44.7 9 0.0 256.0 2.515 2.228 3.5 37.0 0.0115 0.0100 44.7 10 0.0 0.0 2.515 2.228 3.5 37.0 0.0115 0.0100 44.7 11 0.0 0.0 2.515 2.228 3.5 37.0 0.0114 0.0099 44.7 12 0.0 256.0 2.515 2.228 3.5 37.0 0.0115 0.0100 44.6 13 0.0 0.0 2.515 2.228 3.5 37.0 0.0115 0.0100 44.7 14 0.0 0.0 2.515 2.228 3.5 37.0 0.0114 0.0099 44.6 15 0.0 0.0 2.515 2.228 3.5 37.0 0.0120 0.0104 44.2 16 0.0 0.0 2.515 2.228 3.5 37.0 0.0119 0.0103 44.2 17 0.0 0.0 2.515 2.228 3.5 37.0 0.0119 0.0103 44.2 18 0.0 256.0 2.515 2.228 3.5 37.0 0.0119 0.0103 44.2 Continued on next frame

Table A.1: Object J19132680 +4852312 DataFrame (part 4 of 8).

visit ellipticityfwhm_imagex_image y_image xpeak_imageypeak_imageflux_max sexnumberclass_star 0 0.114 0.00 768.294 1574.936 768.0 1575.0 0.550616 1327.0 0.35 1 0.114 0.00 768.294 1574.936 768.0 1575.0 0.925018 1327.0 0.35 2 0.114 0.00 768.294 1574.936 768.0 1575.0 0.559032 1327.0 0.35 3 0.114 0.00 768.294 1574.936 768.0 1575.0 0.401853 1327.0 0.35 4 0.114 0.00 768.294 1574.936 768.0 1575.0 0.577528 1327.0 0.35 5 0.114 0.00 768.294 1574.936 768.0 1575.0 0.823803 1327.0 0.35 6 0.114 3.46 768.294 1574.936 768.0 1575.0 1.518474 1327.0 0.40 7 0.114 4.67 768.294 1574.936 768.0 1575.0 2.558361 1327.0 0.56 8 0.114 3.88 768.294 1574.936 768.0 1575.0 2.796804 1327.0 0.19 9 0.114 6.14 768.294 1574.936 768.0 1575.0 1.893921 1327.0 0.45 10 0.114 14.19 768.294 1574.936 768.0 1575.0 1.099278 1327.0 0.35 11 0.114 0.00 768.294 1574.936 768.0 1575.0 0.741677 1327.0 0.35 12 0.114 0.00 768.294 1574.936 768.0 1575.0 0.670852 1327.0 0.35 13 0.114 0.00 768.294 1574.936 768.0 1575.0 0.690261 1327.0 0.35 14 0.114 0.00 768.294 1574.936 768.0 1575.0 0.454230 1327.0 0.35 15 0.114 0.00 768.294 1574.936 768.0 1575.0 0.696758 1327.0 0.35 16 0.114 0.00 768.294 1574.936 768.0 1575.0 0.509254 1327.0 0.35 17 0.114 0.00 768.294 1574.936 768.0 1575.0 0.629862 1327.0 0.35 18 0.114 0.00 768.294 1574.936 768.0 1575.0 0.687305 1327.0 0.35 Continued on next page

71 Table A.1: Object J19132680 +4852312 DataFrame (part 5 of 8).

visit fov_radiusflux_radius20flux_radius50flux_radius80flux_radius90flux_radius95mag_aper2 mag_aper3 mag_aper5 0 0.500668 1.110 2.106 3.224 3.968 4.847 -0.4707 -1.2077 -1.9400 1 0.500668 1.090 2.052 3.403 4.420 5.689 -0.9309 -1.6788 -2.3672 2 0.500668 1.158 2.030 3.279 4.174 5.123 -0.0352 -0.8964 -1.6568 3 0.500668 1.383 2.352 3.803 5.222 6.236 0.1831 -0.6747 -1.5992 4 0.500668 1.204 2.169 3.513 4.487 5.752 -0.2951 -1.0163 -1.8422 5 0.500668 1.225 2.225 3.610 4.486 5.370 -0.8035 -1.5984 -2.3880 6 0.500668 1.149 2.151 3.396 4.202 4.975 -1.4472 -2.1529 -2.9400 7 0.500668 1.208 2.240 3.710 4.730 5.802 -2.0711 -2.8312 -3.5987 8 0.500668 1.164 2.143 3.493 4.489 5.558 -2.1313 -2.8801 -3.6559 9 0.500668 1.255 2.298 3.660 4.595 5.572 -1.7026 -2.4368 -3.2528 10 0.500668 1.170 2.127 3.343 4.190 5.047 -1.1560 -1.9272 -2.7142 11 0.500668 1.207 2.190 3.471 4.386 5.321 -0.8278 -1.6155 -2.4088 12 0.500668 1.194 2.214 3.552 4.523 5.693 -0.5949 -1.3650 -2.1527 13 0.500668 1.179 2.182 3.496 4.254 5.072 -0.5814 -1.3646 -2.1273 14 0.500668 1.175 2.079 3.290 4.175 5.083 -0.1757 -0.9936 -1.8065 15 0.500668 1.149 2.127 3.544 4.463 5.426 -0.3897 -1.1361 -1.8810 16 0.500668 1.236 2.238 3.796 4.789 5.516 -0.3883 -1.1579 -1.9714 17 0.500668 1.143 2.104 3.536 4.630 5.855 -0.5180 -1.2631 -2.0116 18 0.500668 1.142 2.100 3.401 4.415 5.294 -0.6107 -1.3842 -2.1371 Continued on next frame

Table A.1: Object J19132680 +4852312 DataFrame (part 6 of 8).

visit mag_aper8 mag_aper12mag_aper17mag_aper23magerr_aper2magerr_aper3magerr_aper5magerr_aper8magerr_aper12 0 -2.3447 -2.4353 -2.4563 -2.4788 0.135642 0.096863 0.069757 0.059220 0.059818 1 -2.7354 -2.8413 -2.8810 -2.9089 0.106656 0.075705 0.055513 0.047656 0.047100 2 -2.0256 -2.1198 -2.1647 -2.2134 0.168479 0.113376 0.080733 0.070231 0.071779 3 -2.0664 -2.2076 -2.2736 -2.2752 0.183785 0.123893 0.081443 0.067387 0.066895 4 -2.2366 -2.3538 -2.4095 -2.4209 0.147893 0.106457 0.073313 0.062635 0.062518 5 -2.8164 -2.9677 -2.9969 -3.0284 0.115544 0.080215 0.056052 0.046684 0.044927 6 -3.3485 -3.4543 -3.4867 -3.5264 0.086689 0.062721 0.043788 0.036613 0.035625 7 -4.0200 -4.1659 -4.2157 -4.2432 0.044515 0.031384 0.022080 0.018276 0.017279 8 -4.0420 -4.1641 -4.2053 -4.2337 0.043322 0.030704 0.021516 0.018103 0.017309 9 -3.7121 -3.8511 -3.8938 -3.9138 0.054017 0.038556 0.026539 0.021619 0.020592 10 -3.1189 -3.2257 -3.2551 -3.2632 0.073067 0.051284 0.035838 0.030101 0.029454 11 -2.8294 -2.9470 -2.9902 -3.0146 0.082046 0.057159 0.039873 0.033343 0.032676 12 -2.5683 -2.6936 -2.7396 -2.7585 0.093089 0.065418 0.045821 0.038559 0.037951 13 -2.5524 -2.6753 -2.7077 -2.7207 0.094264 0.065832 0.046666 0.039096 0.038536 14 -2.1743 -2.2759 -2.3121 -2.3282 0.114554 0.078714 0.054576 0.047336 0.047854 15 -2.2591 -2.3990 -2.4295 -2.4626 0.144331 0.102623 0.073480 0.063278 0.062379 16 -2.3699 -2.5444 -2.5776 -2.5915 0.141737 0.099655 0.068995 0.058698 0.056546 17 -2.3836 -2.5012 -2.5552 -2.5840 0.133734 0.095126 0.067946 0.058581 0.058252 18 -2.5118 -2.6427 -2.6742 -2.7052 0.128392 0.090077 0.064150 0.055107 0.054154 Continued on next page

72 Table A.1: Object J19132680 +4852312 DataFrame (part 7 of 8).

visit magerr_aper17magerr_aper23flux_aper2 flux_aper3 flux_aper5 flux_aper8 flux_aper12 flux_aper17 flux_aper23 0 0.064474 0.071371 1.542658 3.041530 5.970417 8.667006 9.421890 9.605605 9.806189 1 0.049179 0.052911 2.357091 4.693934 8.848793 12.421340 13.693590 14.203500 14.573550 2 0.077649 0.085940 1.032977 2.283365 4.599689 6.460164 7.045948 7.343274 7.679626 3 0.070969 0.080501 0.844777 1.861573 4.362031 6.707412 7.639022 8.118065 8.129734 4 0.066132 0.073774 1.312269 2.549853 5.456164 7.845725 8.740384 9.200188 9.297036 5 0.046877 0.049952 2.095955 4.358766 9.019629 13.382710 15.384100 15.804420 16.268840 6 0.036439 0.037758 3.792131 7.263912 14.997080 21.846820 24.084210 24.813820 25.736030 7 0.017229 0.017552 6.736424 13.567200 27.510230 40.551690 46.383950 48.559860 49.806980 8 0.017336 0.017659 7.120527 14.191250 28.997020 41.379830 46.305820 48.095630 49.371360 9 0.020755 0.021457 4.797988 9.434958 20.004770 30.537410 34.707540 36.099620 36.771980 10 0.030477 0.032591 2.900064 5.900287 12.180970 17.682560 19.511340 20.046190 20.197590 11 0.033912 0.036376 2.143566 4.427770 9.194016 13.543970 15.093430 15.706390 16.063730 12 0.039807 0.043484 1.729688 3.515581 7.262081 10.649440 11.951850 12.468740 12.687660 13 0.040759 0.044752 1.708282 3.514184 7.094745 10.494710 11.752480 12.108640 12.254560 14 0.051564 0.057827 1.175624 2.497179 5.279595 7.408607 8.135234 8.410487 8.536482 15 0.066912 0.073575 1.431806 2.847214 5.654590 8.010308 9.111393 9.371058 9.661225 16 0.060064 0.066272 1.429916 2.905112 6.145465 8.870582 10.417680 10.741100 10.879160 17 0.061372 0.067300 1.611394 3.200646 6.377247 8.983327 10.010920 10.521930 10.804030 18 0.057398 0.062409 1.754995 3.578257 7.158793 10.109120 11.404780 11.740560 12.079840 Continued on next frame

Table A.1: Object J19132680 +4852312 DataFrame (part 8 of 8).

visit fluxerr_aper2fluxerr_aper3fluxerr_aper5fluxerr_aper8fluxerr_aper12fluxerr_aper17fluxerr_aper23 0 0.192679 0.271280 0.383499 0.472615 0.518964 0.570273 0.644452 1 0.231490 0.327215 0.452323 0.545074 0.593893 0.643197 0.710041 2 0.160253 0.238378 0.341940 0.417778 0.465701 0.525043 0.607722 3 0.142963 0.212371 0.327122 0.416197 0.470544 0.530510 0.602623 4 0.178706 0.249955 0.368332 0.452501 0.503161 0.560242 0.631563 5 0.222998 0.321951 0.465529 0.575283 0.636435 0.682202 0.748303 6 0.302705 0.419519 0.604689 0.736533 0.790066 0.832597 0.894796 7 0.276126 0.392069 0.559314 0.682431 0.737992 0.770388 0.804963 8 0.284050 0.401222 0.574500 0.689781 0.738049 0.767739 0.802788 9 0.238648 0.334966 0.488861 0.607906 0.658086 0.689906 0.726530 10 0.195118 0.278627 0.401971 0.490114 0.529176 0.562567 0.606129 11 0.161944 0.233045 0.337561 0.415838 0.454141 0.490452 0.538055 12 0.148264 0.211771 0.306402 0.378116 0.417665 0.457037 0.508016 13 0.148278 0.213024 0.304867 0.377808 0.417027 0.454456 0.504988 14 0.124008 0.180998 0.265324 0.322919 0.358477 0.399333 0.454549 15 0.190290 0.269052 0.382594 0.466736 0.523351 0.577381 0.654537 16 0.186623 0.266583 0.390431 0.479450 0.542432 0.594063 0.663889 17 0.198434 0.280353 0.398997 0.484579 0.536979 0.594620 0.669532 18 0.207484 0.296793 0.422867 0.512963 0.568707 0.620518 0.694189

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