Short timescale variability in the Gaia era

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Thesis

Reference

Short timescale variability in the Gaia era

ROELENS, Maroussia

Abstract

This doctoral research is focused on the detection and characterization of short timescale astronomical variability, i.e. luminosity variations on timescale shorter than 12h, as part of the whole data processing and analysis for the European Gaia space mission. First I worked on the prediction of Gaia capabilities for identifying short timescale variability from Gaia photometry, by means of variogram analysis, and via light-curve simulations of various short timescale variable types. Then I investigated real Gaia photometry, looking for bona fide short timescale variable candidates from the first 22 months of Gaia data. This exploratory work resulted in a list of 3018 suspected periodic, short timescale variable candidates, published as part of the Gaia Data Release 2 (April 2018). Finally, I was also involved in various observational programs, contributing to Gaia transient events confirmation and open cluster photometric surveys. Moreover I explored the properties of the Deeming period search technique.

ROELENS, Maroussia. Short timescale variability in the Gaia era. Thèse de doctorat : Univ. Genève, 2018, no. Sc. 5229

DOI : 10.13097/archive-ouverte/unige:106990 URN : urn:nbn:ch:unige-1069903

Available at:

http://archive-ouverte.unige.ch/unige:106990

Disclaimer: layout of this document may differ from the published version.

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Short timescale variability in the Gaia era

T

HÈSE

présentée à la Faculté des sciences de l’Université de Genève pour obtenir le grade de Docteur ès sciences,

mention Astronomie et Astrophysique

par

Maroussia R

OELENS de

Saint-Etienne (France)

Thèse N^o5229

GENÈVE

Observatoire de Genève 2018

Ce travail de thèse a donné lieu à des publications dont la liste se trouve à la page 207.

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Au cours de cette thèse au sein du Département d’Astronomie de l’Université de Genève, mes travaux de recherche ont porté essentiellement sur la détection et la caractérisation des phénomènes de variabilité photométrique à courte échelle de temps, c’est-à-dire des variations de luminosité sur des échelles de temps plus courtes que 12h, dans le cadre du traitement et de l’analyse des données de la mission spatiale europénneGaia(Gaia Collaboration et al. 2016b).

Dans un premier temps, j’ai travaillé sur la prédiction des performances deGaia en termes d’identification et de caractérisation de sources variables à courte échelle de temps (Chapitre 3), en exploitant les séries temporelles photométriques deGaia, grâce la méthode du variogramme, ou fonction de structure (Hughes et al. 1992; Eyer & Genton 1999; Roelens et al. 2017). Pour ce faire, diverses courbes de lumière reproduisant la loi de balayage spécifique de Gaia, et cor- respondant à différents types de variables à courte échelle de temps, ont été simulées, à partir de données et de modèles tirés de la littérature et issus de campagnes de suivi photométrique telles queCatalina,OGLEouASAS. Grâce à cette étude, j’ai pu montrer que la méthode du variogramme appliquée à la photométrie de Gaia obtenue à la fin de la mission nominale devrait permettre d’identifier la grande majorité des phénomènes de variabilité à courte échelle de temps (périodiques ou non-périodiques) observés parGaiaau cours de cette période, avec une contamination limitée provenant de faux positifs, et une contamination plus importante mais justifiable due aux phénmoènes de variabilité de périodes plus longues (de l’ordre de quelques jours) et de grande amplitudes. De plus, cette approche permet d’obtenir des informations précieuses sur les échelles de temps caractéristiques des variations de lumière considérées, informations pouvant être avantageusement combinées aux résultats de méthodes classiques de recherche de période (périodogramme de Fourier par exemple) pour mieux caractériser les sources étudiées, ou encore pouvant faciliter le suivi photométrique au sol de ces candidats variables.

Par la suite, forte de l’expérience acquise grâce à ces simulations et prédictions, je me suis tournée vers l’analyse de données réelles, avec l’exploration de la photométrie intermédiaire de Gaiarésultant des 22 premiers mois d’observation à la recherche de candidates variables à courte échelle de temps (Chapitre 4). Cette étude, exploitant l’ensemble des données astrométriques, photométriques et spectrophotométriques deGaiadisponibles alors, est basée non seulement sur la technique du variogramme précédemment utilisée, mais aussi sur la recherche de période par la méthode des moindres carrés (Zechmeister & Kürster 2009), ainsi que sur l’analyse de diverses grandeurs statistiques telles que l’écart inter-quartile (IQR, Inter-Quartile Range) et la corrélation entre les différentes bandes photométriques deGaia. La sélection de candidats variables à courte échelle de temps de bonne qualité a été effectuée de façon progressive, étape par étape. Au cours de ce processus, j’ai eu la chance de pouvoir faire le suivi photométrique de quelques dizaines de candidats variables préliminaires, depuis deux télescopes au sol, ce qui a fourni de précieuses indications pour améliorer et valider le critère de sélection final. Ce travail d’exploration des données a ainsi abouti à une liste de 3018 candidats variables à courte échelle de temps, focalisée sur les phénomènes de variations périodiques, publiée dans le cadre de la deuxième Data Release

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Résumé

de la missionGaia(GaiaData Release 2) le 25 avril 2018.

Tout au long de mon doctorat, j’ai participé à divers programmes d’observations astronomiques, notamment sur le télescope suisse Euler et sur le télescope flamand Mercator, impliquant à la fois suivi photométrique et mesures spectroscopiques. Une partie de ces observations a été dédiée à l’étude des candidats variables à courte échelle de temps, mentionnée plus haut (Chapitre 5).

J’ai également contribué à la confirmation d’événements variables transitoires détectés par Gaia, dans le cadre du programme des GaiaScience Alerts (Chapitre 6, Section 6.2), ainsi qu’à des observations photométriques d’amas stellaires ouverts pour l’astéroseismologie, et enfin au suivi spectroscopique d’étoiles variables de type Céphéides pour en étudier la cinématique. Durant cette période, j’ai eu l’opportunité de tester le nouveau systême de réduction automatisée des observations photométriques, développé à l’observatoire de Genève par Sergi Blanco-Cuaresma.

Enfin, une partie de mes travaux de recherche a porté sur l’étude des propriétés de la méthode de Deeming pour la recherche de période (Deeming 1975), afin de trouver une approche nouvelle permettant d’estimer de façon simple le rapport signal-à-bruit dans un signal périodique, basée uniquement sur son périodogramme de Deeming (Chapitre 6, Section 6.1).

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This doctoral research is focused on the detection and characterization of short timescale astronomical variability, i.e. luminosity variations on timescale shorter than half a day, as part of the whole data processing and analysis for the EuropeanGaiaspace mission (Gaia Collaboration et al.

2016b).

During the first part of my PhD, I worked on the prediction of theGaiacapabilities for identifying and further characterizing short timescale variability candidates from theGaiaphotometry (Chapter 3), by means of structure function (or variogram) analysis (Hughes et al. 1992; Eyer &

Genton 1999; Roelens et al. 2017). For this purpose, various light-curve data sets have been simulated, covering different short timescale variable types, based on information found in the literature from several photometric monitoring surveys suchCatalina,OGLEorASAS, and reproducing the Gaiapeculiar scanning law. From this study, we evidenced that the variogram analysis ap- plied to end-of-mission Gaiaphotometry should enable to recover the vast majority of the short timescale variability phenomena observed by Gaia, be they periodic or transient, with a limited contamination from constant sources resulting in false positives, and more significant but justifiable contamination from longer period and high amplitude variable objects. Moreover, this approach brings valuable information on the typical timescales of the considered variation, which could be fruitfully combined with e.g. Fourier-based period search methods to further characterize the investigated sources, or be exploited to help designing ground-based follow-up of the identified candidates.

As a logical next step, I then reinvested the knowledge and experience acquired through those simulations and predictions into the investigation of intermediate real Gaiaphotometry, looking for bone fide short timescale candidates from the first 22 months of Gaiadata (Chapter 4). This analysis took advantage of the wholeGaiaastrometry, photometry and spectrophotometry available at that time, and made use not only of the variogram approach explored previously (never- theless with a few adaptations to fit the best possible to the specificities of theGaiaintermediate photometry), but also Least-Square period search (Zechmeister & Kürster 2009) and other statistical metrics such as the Inter Quartile Ratios and the correlation values between the differentGaia passbands. The selection of bona fide short timescale variable sources fromGaiawas performed in a progressive and step by step manner, and during this process I had the great chance to perform some photometric follow-up of a few tens of preliminary candidates, which gave invaluable clues for improving and validating the final selection criteria. This exploratory research work resulted in a list of 3018 short timescale candidates, oriented towards suspected periodic phenomena, published as part of theGaiaData Release 2 on April 25^th2018.

Throughout my PhD, I was involved in various observational programs at the Swiss Euler telescope and at the Flemish Mercator telescope, including both spectroscopic and photometric follow-up. A part of those activities were dedicated to theGaiashort timescale variable candidates monitoring mentioned above (Chapter 5), but I also contributed to theGaiaScience Alerts program forGaiatransient events confirmation (Chapter 6, Section 6.2), to open cluster photometric surveys

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Summary

for asteroiseismology, and to spectroscopic follow-up for investigating the kinematics of Cepheid variable stars. During this period I had the opportunity to test the newly implemented automated pipeline for photometric reduction, developed by Sergi Blanco-Cuaresma as part of his postdoc at the Geneva observatory. Finally, I also explored the properties of the Deeming period search technique (Deeming 1975), trying to find a new simple estimator of the signal-to-noise ratio within a periodic signal based only on its periodogram (Chapter 6, Section 6.1).

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Après quatre ans de recherche, la rédaction de ce manuscrit et la défense de ma thèse con- stituent, comme pour beaucoup de doctorants je crois, l’aboutissement d’un projet professionnel et personnel réfléchi de longue date. Toutefois, le fait de mener à bien un tel projet est simplement inenvisageable seul, sans soutien d’autrui et la participation de nombreuses personnes tout au long de l’aventure, acteurs bienveillants présents à nos côtés, que ce soit au niveau scientifique ou amical. Avant toute forme d’exposé sur la recherche et les travaux effectués -nos travaux devrais-je dire- au cours de ces années passées à l’observatoire de Genève, je souhaite avant tout remercier toutes celles et tous ceux qui m’ont aidée à arriver où je suis aujourd’hui.

En premier lieu, je tiens à remercier mes directeurs de thèse, Laurent Eyer et Nami Mowlavi, pour leur aide, leur confiance, et la liberté qu’ils m’ont accordée dans mes travaux de recherche au cours de cette période. Un grand merci également aux membres du jury, Anthony, Simon et Corinne, pour leur temps, leur aide et leurs conseils pour mener au mieux ma barque dans la dernière ligne droite jusqu’au port. Je souhaite adresser un remerciement tout particulier à Corinne, pour les opportunités qu’elle m’a offerte et pour son “coaching” au cours de mes quatre années passées à l’observatoire.

Je tiens à exprimer toutes ma gratitude et ma reconnaissance envers chacun des membres de l’équipe Gaia et de l’équipe de variabilité stellaire au sein de l’observatoire, particulièrement Sergi, Isabelle, Grégory et Olivier M. pour leurs conseils, leur bienveillance, leur amitié, leur soutien et nos conversations sur l’actualité et les aventures du quotidien qui vont beaucoup me manquer ; sans oublier Lorenzo, Berry, Krzys, Leanne, Marc A., Thierry, Jon, Sophie, Lovro et Fabio. De façon générale, j’adresse un grand merci à tous les membres de l’observatoire, de Sauverny et d’Ecogia, ainsi qu’à tous les membres de la CU7 et du DPAC Gaia, ce fut un vrai plaisir et une grande chance pour moi de pouvoir vous rencontrer et vous côtoyer pendant ces quatre années. Je ne peux évidemment pas ne pas adresser un mot particulier à la joyeuse équipe des astro-musiciens (Thibaut, François, Florian, Arthur, Olga, Marc T., Manuela et Nathan) : nos répétitions (presque) hebdomadaires ont toutes été pour moi un merveilleux moment de partage et de musique, et l’aventure de la fête de Noël 2016 restera pour longtemps dans ma mémoire !

Enfin, je désire exprimer tout mon amour et ma reconnaissance à mon mari Maxime, pour son soutien indéfectible tout au long de ma thèse (même pendant les runs d’observation et les dures semaines de préparation de la soutenance !), ainsi qu’à l’ensemble de ma famille, en particulier mes parents qui m’ont toujours encouragée dans la voie que j’avais choisie et qui ont toujours cru en moi. Vous m’avez donné la force, le courage et la pugnacité d’aller au bout de mes idées, tout au long du chemin et malgré les embûches. Sans vous je n’en serai jamais arrivée là où j’en suis aujourd’hui, et pour cela je ne vous remercierai jamais assez.

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Remerciements

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Résumé i

Summary iii

Remerciements v

List of Figures xv

List of Tables xvii

1 Short timescale variability across the sky 1

1.1 What is short timescale variability? . . . 1

1.2 Short timescale variable types across the sky . . . 5

1.2.1 Pulsating short period variables . . . 5

1.2.2 Short period eclipsing binary systems . . . 17

1.2.3 Short timescale variability and cataclysmic variables . . . 20

1.2.4 Short timescale variability of other origin . . . 23

2 TheGaiamission 25 2.1 Overview and objectives . . . 25

2.2 TheGaiaspacecraft and payload . . . 27

2.3 Scanning the sky withGaia. . . 31

2.4 TheGaiaData Release Scenario . . . 35

2.5 The Gaia Data Processing and Analysis Consortium . . . 38

3 Predictions on the detectability of short timescale variability withGaia 43 3.1 The variogram method . . . 43

3.2 Simulations of short timescale variable light-curves . . . 46

3.2.1 Periodic variations . . . 47

3.2.2 Transient variations . . . 51

3.3 Detectability results in an ideal situation . . . 52

3.4 Detectability results in theGaia-like context . . . 61

3.5 Conclusion . . . 73

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Contents

4 TheGaia Data Release 2: first sample of short timescale variable candidates identified

byGaia 77

4.1 Short timescale candidate selection and characterization . . . 78

4.1.1 Input data . . . 78

4.1.2 Variogram analysis . . . 80

4.1.3 High frequency search . . . 86

4.1.4 Other statistics and parameters . . . 88

4.2 Bona fide short timescale candidate selection: a step by step process . . . 91

4.2.1 Suspected periodic candidate selection . . . 91

4.2.2 Environment filtering . . . 94

4.2.3 Other spurious variability removal from light-curve visual inspection . . . . 95

4.2.4 Eclipsing binary candidates removal . . . 98

4.3 TheGaiaData Release 2 short timescale sample . . . 100

4.3.1 The short timescale products for GDR2 . . . 100

4.3.2 Properties of the GDR2 short timescale, suspected periodic candidates . . . . 101

4.3.3 Conclusion . . . 112

5 Ground-based photometric follow-up of preliminaryGaiashort timescale variable can- didates 115 5.1 Observing facilities and photometric analysis tools at the Geneva Observatory . . . 115

5.2 The bright short timescale variable candidates sample: test case for ground-based follow-up . . . 119

5.3 The Gaia preliminary short timescale candidates follow-up: on the way to GDR2 bona fide selection . . . 124

6 Additional research and scientific activities 137 6.1 Signal-to-noise estimation for Deeming periodograms . . . 137

6.1.1 The Deeming periodogram: properties for evenly sampled signals . . . 139

6.1.2 The Deeming periodogram for irregularly sampled signals . . . 145

6.2 GaiaScience Alerts follow-up . . . 159

7 Conclusion and perspectives 165 A Example light-curves 169 A.1 Example of galaxy light-curve fromGaia . . . 169

A.2 Example ofGaiaDR2 short timescale candidate light-curves . . . 171

A.3 Gaia and MAIA light-curves for preliminary short timescale variable candidates monitored from ground . . . 175

B Signal-to-noise estimation from Deeming periodograms 183 B.1 Mean periodogram calculation principle used in the mean-based noise estimators, for noisy sine waves . . . 183

B.2 Deeming estimators validity domain: scatter plots . . . 185

B.2.1 First data set . . . 185

B.2.2 Second data set . . . 188

B.2.3 Third data set . . . 191

B.3 Deeming estimators validity domain: color maps . . . 194

B.3.1 First data set . . . 194

B.3.2 Second data set . . . 198

B.3.3 Third data set . . . 202

C Publications related to this thesis 207

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References 271

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Contents

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1.1 TESSandLSSTphotometric passbands . . . 4

1.2 One classification variability tree . . . 6

1.3 Examples of non-radial pulsating modes . . . 8

1.4 Pulsating stars across the HR diagram . . . 10

1.5 Example ofδScuti star light-curve . . . 11

1.6 Example ofβCephei star light-curve . . . 12

1.7 Examples of RR Lyrae star light-curves . . . 13

1.8 Illustration of the Blazhko effect in RR Lyrae stars . . . 13

1.9 Example of EC14026 pulsator light-curve . . . 15

1.10 Light-curves of the 14 first BLAPs discovered . . . 16

1.11 Example of ZZ Ceti star light-curve . . . 17

1.12 The three eclipsing binary geometries . . . 18

1.13 Typical eclipsing binary light-curves . . . 19

1.14 Typical supernovae light-curves . . . 21

2.1 Gaiaposition and orientation at L2 . . . 26

2.2 Gaiaphotometric passbands . . . 26

2.3 Gaiaexploded view . . . 28

2.4 Gaiaschematic view of the payload module . . . 29

2.5 Gaiafocal plane . . . 30

2.6 Gaiaobservation principle . . . 32

2.7 Gaiasky coverage over the nominal mission . . . 32

2.8 GaiaDPAC organisation . . . 39

2.9 GaiaCU7 processing flow . . . 41

3.1 Typical variogram plots for periodic and non-periodic variability . . . 45

3.2 Examples of light-curve templates for the simulated short period variable types. . . 49

3.3 Probability distribution in the period-amplitude diagrams, for the eight simulated periodic variable types. . . 50

3.4 Gaiaper-CCD error law . . . 51

3.5 Examples of transient light-curve templates . . . 52

3.6 Examples of simulated continuous light-curves and variograms for short timescale periodic types (1). . . 54

3.7 Examples of simulated continuous light-curves and variograms for short timescale periodic types (2). . . 55

3.8 Examples of simulated continuous light-curves and variograms for transient variability. . . 56

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List of Figures

3.9 Maximum variogram value as function of the amplitude for the continuous periodic

simulations . . . 56

3.10 Detection timescale as function of the maximum variation rate, for the continuous data set . . . 57

3.11 Typical timescale as function of the simulation period, for the periodic variable types and the continuous data set . . . 58

3.12 Maximum variation rate as function of the amplitude, for the transient simulations in the continuous data set . . . 59

3.13 Typical timescales as function of the event durations, for the transient continuous simulations . . . 60

3.14 Examples of simulated Gaia-like light-curves and variograms for short timescale periodic types (1). . . 63

3.15 Examples of simulated Gaia-like light-curves and variograms for short timescale periodic types (2). . . 64

3.16 Maximum variogram value (unweighted) as function of the meanGaiaGmagnitude for theGaia-like data set . . . 65

3.17 Examples of simulated Gaia-like light-curves and weighted variograms for short timescale periodic types (1). . . 67

3.18 Examples of simulated Gaia-like light-curves and weighted variograms for short timescale periodic types (2). . . 68

3.19 Maximum variogram value (weighted) as function of the meanGaiaGmagnitude for theGaia-like data set . . . 69

3.20 Detection timescale as function of the maximum variation rate, for theGaia-like data set . . . 71

3.21 Typical timescale as function of the simulation period, for the periodic variable types and theGaia-like data set . . . 72

3.22 Examples of simulatedGaia-like light-curves and weighted variograms for transient variability. . . 74

3.23 Typical timescale as function of the event durations, for the transientGaia-like simulations . . . 74

4.1 GaiaCU7 photometric operator chain . . . 78

4.2 GFoV mean magnitude distribution for all sources, and sources with per-CCD data 81 4.3 Variogram detection threshold definition forGaiaDR2 . . . 84

4.4 Example ofOGLElonger period variables flagged as short timescale candidate . . . 85

4.5 Period recovery for the sources of the reference crossmatched sample . . . 88

4.6 FAP distribution for the reference crossmatched sample of known variables . . . 89

4.7 Statistical parameter distributions for the reference crossmatched sample, along the suspected periodic candidate selection process . . . 93

4.8 Example of spurious short timescale candidate, contaminated by nearby similar source. . . 94

4.9 Example of spurious short timescale candidate, contaminated by nearby bright source. 95 4.10 Example of spurious short timescale candidate, with anti-correlated G, G_BP and G_RP light-curves. . . 97

4.11 Spearman correlation 2D distributions for the preliminary short timescale candidate sample . . . 98

4.12 Example of short timescale candidate illustrating the need of extra time series cleaning 99 4.13 Gaialight-curves of the PCEB NN Ser . . . 103

4.14 Literature andGaialight-curves of the PCEB CSS J210017 . . . 105

4.15 Literature andGaialight-curves of the CV CSS J231330 . . . 106

4.16 Sky distribution of the GDR2 short timescale candidates . . . 107 xii

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4.17 Frequency - amplitude distribution of the GDR2 short timescale candidates . . . 108

4.18 HR diagram of the short timescale candidates with reliable photometry and parallaxes109 4.19 Gaialight-curves and variogram of the aliased short timescale candidate STS56466930 . . . 110

4.20 Gaialight-curves and variogram of the curious short period eclipsing binary STS56378276 . . . 111

5.1 ECAM and MAIA photometric SNR as function of the aperture radius . . . 119

5.2 STSb52895392GaiaGCCD light-curve and variogram . . . 121

5.3 STSb52895392 ECAMRGdifferential light-curve over one night . . . 122

5.4 STSb52895392 ECAMRGfull differential light-curve . . . 122

5.6 STSb59867871 ECAMRGdifferential light-curve . . . 125

5.8 STSb30343015 ECAMRGdifferential light-curve . . . 126

5.10 STSb58858857 ECAMRGfinding chart and line cut profile . . . 127

5.11 STSp18071705GaiaGCCD light-curve and variogram . . . 130

5.12 STSp18071705 MAIARdifferential light-curve . . . 130

5.13 STSb20889717 MAIARsingle science and co-added image . . . 131

5.17 STSp18535013 MAIARfinding chart . . . 134

5.18 STSp35921151 MAIARfinding chart . . . 134

6.1 Simulated light-curve and Deeming periodogram for an evenly sampled, monoperiodic sinusoidal signal . . . 141

6.2 Illustration of the frequency leakage phenomenon . . . 142

6.3 Deeming periodogram for an evenly sampled, monoperiodic sinusoidal signal, at frequencies outside the Nyquist frequency range. . . 142

6.4 Possible noise estimators, for evenly sampled, purely noisy signals . . . 144

6.5 Distributions for the four tested noise estimators, for a given input noise level, for evenly sampled, purely noisy signals . . . 144

6.6 Simulated light-curve and Deeming periodogram for an evenly sampled, noisy, monoperiodic sinusoidal signal . . . 146

6.7 σestimators in the case of monoperiodic sinusoidal signal with Gaussian noise regularly sampled, testing different values of input noise. . . 147

6.8 Distributions for the four tested noise estimators, for a given input noise level, for evenly sampled, noisy sinusoidal signals . . . 147

6.9 Example of simulated monoperiodic sinusoidal signal with random time sampling . 150 6.10 Validation of the noise estimator σ_power,mean for randomly sampled, purely noisy signals. . . 151

6.11 Example of simulated noisy monoperiodic sinusoidal signal with random time sampling . . . 152

6.12 Validation of theσ_power,meanestimator in the case of monoperiodic sinusoidal signal with Gaussian noise randomly sampled . . . 153 6.13 Validation of the correctedσestimatorσcorr for noisy sine waves randomly sampled. 154

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List of Figures

6.14 Validation of the correctedσestimatorσ_corrfor noisy sine waves randomly sampled

(2). . . 154

6.15 Example of full-shifted spectral window, unnormalized and normalized, for random time sampling. . . 156

6.16 Validation of the new correctedσ estimator σ_corr for noisy sine waves randomly sampled. . . 157

6.17 Excerpt of theGaiaScience Alerts index . . . 161

6.18 Gaia16aliSDSSand ECAM images . . . 163

6.19 Gaia16aliGaiaand ECAM light-curve . . . 163

6.20 Gaia16aljSDSSand ECAM images . . . 164

6.21 Gaia16aljGaiaand ECAM light-curve . . . 164

A.1 Example of galaxy within the short timescale candidate sample . . . 170

A.2 Gaialight-curves and variogram of the CV AAVSO 161069 . . . 171

A.3 Gaialight-curves and variogram of the aliased short timescale candidate STS60777895 . . . 172

A.4 Gaia light-curves and variogram of the low-amplitude short timescale candidate STS60810512 . . . 173

A.5 Gaialight-curves and variogram of the smooth sinusoidal-like short timescale candidate STS56455173 . . . 174

A.6 STSp45685316GaiaG-CCD light-curve and variogram . . . 176

A.7 STSp45685316 MAIARdifferential light-curve . . . 176

B.1 Calculation principle of noise estimators in the case of evenly sampled, noisy monoperiodic sine wave . . . 184

B.2 Calculation principle of noise estimators in the case of randomly sampled, noisy monoperiodic sine wave . . . 184

B.3 Validity domain forPestim- scatter plots for the first data set . . . 185

B.4 Validity domain forAestim- scatter plots for the first data set . . . 186

B.5 Validity domain forA_estim/σ_corr - scatter plots for the first data set . . . 187

B.6 Validity domain forPestim- scatter plots for the second data set . . . 188

B.7 Validity domain forA_estim- scatter plots for the second data set . . . 189

B.8 Validity domain forAestim/σcorr - scatter plots for the second data set . . . 190

B.9 Validity domain forPestim- scatter plots for the third data set . . . 191

B.10 Validity domain forA_estim- scatter plots for the third data set . . . 192

B.11 Validity domain forAestim/σcorr - scatter plots for the third data set . . . 193

B.12 Validity domain forP_estim- color maps of relative error for the first data set . . . 195

B.13 Validity domain forAestim- color maps of relative error for the first data set . . . 196

B.14 Validity domain forA_estim/σ_corr - color maps of relative error for the first data set . . 197

B.15 Validity domain forP_estim- color maps of relative error for the second data set . . . 199

B.16 Validity domain forAestim- color maps of relative error for the second data set . . . 200 xiv

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B.17 Validity domain forA_estim/σ_corr - color maps of relative error for the second data set 201 B.18 Validity domain forPestim- color maps of relative error for the third data set . . . 203 B.19 Validity domain forA_estim- color maps of relative error for the third data set . . . . 204 B.20 Validity domain forA_estim/σ_corr - color maps of relative error for the third data set . 205

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List of Figures

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2.1 Gaiaend-of-nominal-mission astrometric performances . . . 35

2.2 Gaiaend-of-nominal-mission spectroscopic performances . . . 35

2.3 Gaiaend-of-nominal-mission photometric performances . . . 36

3.1 List of periodic short timescale variable types simulated . . . 47

3.2 List of transient variable types simulated . . . 47

3.3 Comparison between the ideal and theGaia-like short timescale detectability results 62 3.4 Short timescale variability detection: results of the variogram analysis (with the weighted variogram formulation) for different detection criteria and different detection timescale limits. . . 70

4.1 Excerpt of thevari_short_time_scaletable in the GDR2 archive . . . 102

5.1 Observing log for the photometric follow-up of the GaiaDR2 short timescale variable preliminary candidates . . . 128

6.1 Statistics for the four tested noise estimators, for a given input noise level, for evenly sampled, purely noisy signals . . . 143

6.2 Statistics for σ estimators, in the case of monoperiodic sine with Gaussian noise ∼ N(0,3)regularly sampled. . . 145

6.3 Validity domain for all studied estimators in the frame of Deeming analysis, for randomly sampled noisy monoperiodic sine wave - first data set . . . 158

6.4 Validity domain for all studied estimators in the frame of Deeming analysis, for randomly sampled noisy monoperiodic sine wave - second data set . . . 159

6.5 Properties of Gaia16ali and Gaia16alj . . . 162

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List of Tables

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Chapter 1 Short timescale variability across the sky

At least a millenium ago (and even before), humans have realized that the light coming from the shining and fascinating objects hung on the celestial sphere sometimes changed over time, giving birth to the notion of astronomical variable source. But what is hidden behind this -apparently- simple concept? Nowadays, after centuries of astrophysical variability studies, we know that this idea of “source showing brightness changes over a human lifetime” engulfs a huge diversity of celestial objects, from stars to galaxy nuclei, over a wide range of luminosities and colors, exhibiting spectacular brightness variations as well as tiny deviations, over timescale ranking from seconds to centuries.

The cornerstone of this thesis work is the investigation of short timescale variability. However, defining short timescale variability is not straightforward. When going through the literature, one can quickly realize that the definition of what is “short” or “fast” among the astronomical variability phenomena observable across the sky really depends on the adopted scope. Thus, a variation period of e.g. 1h will be among the longer timescales studied for pulsating white dwarf experts, whereas Cepheids experts will find that a period of1d is a very short one!

In this Chapter, I define the scientific frame within which this thesis work is achieved, precising what is included in “short timescale variability”, be it in terms of characteristic timescale or types of astronomical sources. In Section 1.1 we give our definition of short timescale variability, and expose the actual situation and scientific interest relatively to those specific objects. Section 1.2 is dedicated to a -non-exhaustive- list of the variability types entering our short timescale variability definition, as well as their main properties and importance in the astrophysical field.

1.1 What is short timescale variability?

Various definitions of an astronomical variable source can be found in literature. In Richter et al. (1985), a variable star is simply defined as a “star showing brightness changes in the optical, over timescales of decades at most”. Yet an astonishing diversity of objects can fit this state- ment. Nowadays, hundred thousands of variable stars have been discovered, spread all over the Hertzsprung-Russell (HR, Russell 1914) diagram. Various phenomena can be at the origin of the variability, be it within the star itself or related to its environment as seen from the Earth. The known variable stars are classified depending on variability causes and stellar properties, with a classification involving tens of different variable types, covering wide ranges of amplitudes -from a few millimagnitudes to a few magnitudes- and timescales -from a few tens of seconds to thousands of days. Note that, all along this thesis, the word “amplitude” refers to the peak-to-peak magnitude difference from the light-curve of the considered variable. For a review of those different variability types, see e.g. Eyer & Mowlavi (2008).

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1.1. What is short timescale variability?

However, the definition of Richter et al. (1985) engulfs only a part of the variability seen in the sky. First of all, it mentions only the brightness changes in the optical, however variability has been observed at other wavelengths. Hence, some peculiar binary systems (see Section 1.2.2 for more details on the binary systems in general) are known to emit strong and variable radiation in X-ray: these are the so-called X-ray binaries, with one component being a star (be it degenerate dwarf, Main Sequence dwarf or giant), and the other one being a black hole or a neutron star (i.e.

the collapsed core of a massive star). Besides, other astronomical objects than stars can exhibit brightness changes with time. One can mention e.g. asteroids, which are oftenly non spherical and with an asymmetric surface. As they are rotating on themselves, their brightness as seen from Earth varies depending on the side oriented towards the observer. Note also that they can eventually be source of variability in the sense that they can be eclipsed by another object (generally another gravitationally bound asteroid companion), or eclipse another source along their orbital movement. Another very interesting type of non-stellar variable sources is the class of Active Galactic Nuclei (hereafter AGN). An AGN is a compact and very bright region at the center of a galaxy, showing stochastic variability over the entire electromagnetic spectrum, on timescale ranking from hours to years. The extra radiation observed in such objects is thought to result from the accretion of matter onto a Super Massive Black Hole. The variability seen could result from the presence of an accretion disk around this black hole, from jets or from occultations of those central components.

Since the first reported discoveries of variable stars, such as the supernovae of 1006, 1572 and 1604 (pointed at this time as “new very bright stars”), or the first periodic variable star Mira in 1639, the field of astronomical variability has never stopped developing, going deeper and further in our understanding of the underlying phenomena, to the point of becoming a fully-fledged domain of research in astronomy. Indeed, the interest of studying variability across the sky lies in the diversity of known variable types. Hence, variability can be intrinsic to the variable source, allowing an insight to its internal properties (structure, magnetic field, age, radius, luminosity, metallicity, evolutionary phase if relevant...), or extrinsic to the source, then giving information on the geometry of systems (e.g. in the case of eclipsing binaries) or on the interactions between astronomical sources. Additionally, some classes of variable stars have very specific and useful class-wise properties, e.g. the Cepheids which are pulsating giant stars whose variability periods are tightly related to their luminosities via a well-known and calibrated period-luminosity relation (see Leavitt & Pickering 1912), hence providing a valuable cosmic distance scale. All in all, the different variability types enable to probe all the stages of stellar evolution, and relate to several fields of astronomy, from stellar evolution to physics of degenerate matter to accretion processes.

We emphasize that, all along this thesis work, we consider only thephotometric variability, in theoptical wavelengthdomain. Note that some variable sources exhibit spectroscopic variability features, eventually with no associated photometric signature, but those cases are note treated in the frame of this thesis.

The primary aim of this thesis is to focus on a specific category of variable sources among this great variability zoo: theshort timescale variability. We define short timescale variability as any type of variability in the optical, be it periodic or transient, with characteristic timescale(s) of variation below half a day, eventually extending the definition up to one day. More precisely, we are particularly interested in variables with the shortest characteristic timescales, from a few tens of seconds to two or three hours. It is important to say that this definition engulfs, of course, the periodic variables with periods below12h and the non-periodic variables of duration below this limit, but not only. It also includes some variables with longer periods or durations, showing steep and significant magnitude variations in some parts of their light-curves, i.e. with a relatively high variation rate (in terms of average magnitude change per unit time). A more quantitative

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definition of short timescale variability, via the variogram analysis, is given in Chapter 3.

A variety of variable types are known to exhibit such rapid variations in their optical light- curves, again involving diverse amplitudes up to a few magnitudes, with different variability characteristics and phenomena at the origin of variation, from pulsations to flares to eclipsing systems. However, a relatively small number of those short timescale variables have been discovered until now, compared to longer timescale variables such as Cepheid or Mira pulsating stars. This is a direct consequence of the observational constraints associated to the photometric search of short timescale variability, first in terms of time sampling since high cadence monitoring is re- quired, then in terms of photometric precision prerequisite which can be quite high, particularly for detecting low amplitude variables. Both conditions were relatively hard to achieve with photo- electric detectors. Besides, the rarity of some short timescale variability phenomena, e.g. because occurring during relatively short phases of stellar evolution, makes things more difficult. How- ever, over the last decade, significant technological improvements, with the revolution of Charged Coupled Device (CCD) cameras, allowed a major progress in this domain, making short timescale variability domain accessible more easily. As a consequence, the early 21^st century saw the ad- vent of high cadence photometric monitoring surveys, which enabled noteworthy increase in the number of known short timescale variable sources. We can mention space surveys, such asKepler (Borucki et al. 2010) orCoRoT (Baglin et al. 2006; Barge et al. 2008), as well as ground-based surveys such as the Rapid Temporal Survey (RATS, Ramsay & Hakala 2005; Barclay et al. 2011), the OmegaWhite survey (Macfarlane et al. 2015; Toma et al. 2016), the Optical Gravitational Lensing Experiment (OGLE, Udalski et al. 1992), the Palomar Transient Factory (PTF, Law et al. 2009), the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS, Chambers et al. 2016) or theCatalinasurvey (Drake et al. 2009, 2014c).

In the near future, the GaiaESA mission (described in details in Chapter 2) will drastically change the landscape in the short timescale variability field. Launched in December 2013,Gaiais to scan the whole sky, down to magnitudeG≈20mag (withGtheGaiawhite-light band covering the330−1050 wavelength range, see Chapter 2), with high photometric precision, down to the millimagnitude level for bright stars. It follows a peculiar semi-regular time sampling, with groups of nine successive measurements over 40s (about4.85s between each other), each group being separated from the following one by1 h 46 minor4 h 14 min, and with about 70 of those groups per source on average over the 5 year nominal mission. Thanks to its specific cadence and amazing performances, Gaiawill enable to probe stellar variability on timescales as short as a few tens of seconds, all over the sky (and not only a restricted area of the celestial sphere, contrary to most of the surveys mentioned above), including very bright as well as faint sources. This will allow an unprecedented and homogeneous census of short timescale variability in our Galaxy and beyond, dramatically increasing the number of discoveries of such sources.

The goal of this thesis is toidentify and characterize short timescale variability phenomena observed byGaia, taking advantage of thewholeGaiadata setavailable at the time of the thesis, essentially astrometry, photometry and spectrophotometry. This analysis is part of the global Gaia Variability Processing, within theGaiaData Processing and Analysis Consortium (DPAC) activities and more precisely within the Coordination Unit 7 (CU7) group. For more details on GaiaDPAC and on CU7, see Chapter 2.

A bit less than four years after the start of the routine operations,Gaiaalready proved its efficiency for variability studies.

Since July 2014, theGaiaScience Alerts (GSA) system (Hodgkin et al. 2013; Wyrzykowski 2016), based on a rapid analysis of theGaiadaily data products, triggered the detection, classification and follow-up of transients sources observed by the satellite, typically sources experiencing a sudden brightening from one observation to the following, or relatively bright sources appearing in an

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1.1. What is short timescale variability?

Figure 1.1: Left: TESS photometric passband, from Ricker et al. (2014). Right: LSST passbands, from Ivezic et al. (2008), together with the atmospheric transmission at airmass1.2(dotted line).

area of the sky already scanned and where nothing significant had been detected before¹. At the moment of writing this manuscript, about 6 transit alerts are published every day, for a total of more than 4200 alerts since the beginning of the GSA activities. The ground-based confirmation follow-up of the GSA transients, as soon as possible after the alert, is possible thanks to a network of contributing institutes and observers. Whenever an alert is published, if one of the GSA col- laborators has available telescope time at a place from where the alert is visible, then a bit of the time is dedicated to photometric observation of the alert, to see if there is still something visible at the position of the alert, and confirm the transient event (or not). If the transient is confirmed, the observer can publish an Astronomical Telegram² (hereafter ATel) announcing the confirmation. As part of my PhD, I had the opportunity to follow-up severalGaiaScience Alerts, between September 2014 and November 2016, which resulted in the publication of associated ATels. More details are given in Chapter 6.

Even though, initially, theGaiadata release scenario did not plan any variability result publication before the third Data Release, the first GaiaData Release (GDR1), on September 14^th 2016, already proved the efficiency ofGaiain terms of variability studies, since the CU7 Variability Pro- cessing resulted in a list of 3194 Cepheids and RR Lyrae stars in the South Ecliptic Pole region, including 398 new discoveries (Eyer et al. 2017; Clementini et al. 2016). Note that, though Cepheid stars have generally periods longer than1d, RR Lyrae stars can have periods down to a few hours, and thus can enter the short timescale variable category.

The secondGaiaData Release, on April 25^th 2018, goes much further in terms of variability results, with more variable candidates of more variable types, not only Cepheid and RR Lyrae stars but also Long Period Variables (LPV),δ Scuti stars, short timescale variables, stars showing solar-like magnetic activity or spots at their surface (Holl et al. 2018)

In the coming years, other high cadence photometric surveys will be implemented, offering great opportunities for the short timescale variability search.

One can mention the Transiting Exoplanet Survey Satellite (TESS, Ricker et al. 2014), selected in 2017 by NASA as an Astrophysics Explorer Mission, and which has been launched on April 18^th 2018. The TESSsatellite is designed as a planet finder, targeting, among others, small transiting exoplanets such as Super-Earths. It will perform a 2-year long survey in the red and near-infrared

1For more information on the Gaia Science Alerts, see the GSA website http://gsaweb.ast.cam.ac.uk/

alerts/home.

2http://www.astronomerstelegram.org

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wavelengths (see left panel of Figure 1.1), observing hundreds of thousands preselected bright nearby stars, typically main-sequence stars of spectral type F5 to M5 with IC . 10 −13mag, covering90% of the sky, with a cadence of2min. The expected photometric performances ofTESS are 200parts-per-million (ppm) atI_C ≈10mag and 1% atI_C ≈16mag. Though it is not its main scientific objective,TESSwill produce very valuable photometric data for short timescale variables search, its sampling allowing to probe variability down to timescales of a few tens of minutes.

From the ground-based side, the Large Synoptic Survey Telescope (LSST, Ivezic et al. 2008), a large wide-field8.4m, USA-supported telescope, which will be settled at Cerro Pachón (Chile), will also bring invaluable clues for variability studies, and particularly for short timescale variables investigation. Over its planned 10 year mission, LSST is expected to observe 37 billion sources down tor ∼ 24.4mag, covering about half of the sky (i.e. a 30,000 deg² area at declinationδ <

+34.5^◦). The telescope is designed to obtain multi-band photometry, covering the full passband 320-1050nm (see right panel of Figure 1.1), for a total of about 1000 observations per object over the 10 years, with an integration time around 30s. This represents one observation every3d on average for a single source. For comparison,Gaiatriggers one observation per source every26d on average. LSST first light is predicted for 2021, and full operations should start in 2022. With short exposures, wide and deep coverage of a significant part of the sky, multi-band photometric coverage, and additionally a high expected photometric accuracy around5mmag, theLSSTdesign makes of this telescope a powerful instrument for rapid variability search.

1.2 Short timescale variable types across the sky

In this section, I present a non-exhaustive list of the inventoried short timescale variable stars, which we expect to detect with Gaia. I emphasize on the fact that this list is mostly focused towards photometric, stellar variability. As mentioned previously, in this work I do not consider spectroscopic variability nor photometric variability in other wavelengths. However we keep in mind that non-stellar variable sources can exhibit fast variability, e.g. some AGNs, and of course they are not excluded from our theoreticalGaiashort timescale targets.

Before starting our short timescale variable list, I remind the general classification principles of astronomical variable sources, as described in Eyer & Mowlavi (2008). Their variability tree is presented in Figure 1.2.

The first division in variable sources classification is made between theextrinsicandintrinsic variability. As already mentioned, extrinsic variability corresponds to brightness variations due to geometrical effects, e.g. to the rotation of the source, or to the presence of something occulting the object along the line of sight. On the other hand, intrinsic variability corresponds to brightness variations due to physical changes occurring in the source itself. The second division depends on the type of the considered astronomical object: star, galaxy or asteroid. The third level of classification defines categories as function of the origin of the observed variability. The different origins of extrinsic variability are: rotation, eclipses by a companion (or eventually a further transiting ab- ject along the line of sight), and microlensing. As concerns intrinsic variability, eruptive stars are separated from cataclysmic systems, pulsating stars and secular variability. For obvious reasons given the name, secular variability is not treated in the frame of short timescale variability analysis. The fourth (and last) division groups sources of similar nature, for example in their chemical composition and evolutionary phase, as well as sources with similar photometric behavior.

1.2.1 Pulsating short period variables

•Theory of pulsating stars

By definition, pulsating stars are stars showing periodic expansion and contraction of their surface, at a given stage of their stellar evolution process, a phenomenon which alters their light

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6 CHAPTER 1. SHORT TIMESCALE VARIABILITY ACROSS THE SKY

Figure 1.2: Variability tree from Eyer & Mowlavi (2008). Short timescale variables types present in this tree are framed in cyan.

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output. In short, stellar pulsations occur when a small instability within the star drives it away from its hydrostatic equilibrium, and when this instability is maintained by certain conditions in the destabilized stellar layer.

Various mechanisms can trigger stellar pulsation (see e.g. Richter et al. 1985; Kurtz 2006; Han- dler 2013)³.

The mechanism most commonly associated to stellar pulsation is theκ mechanism (Richter et al. 1985), where the pulsation is supported by the absorption characteristics of the partial ionization zones in the outer layers of the star. Going towards the interior of any star, the temperature increases, and so does the ionisation of matter, e.g. helium He, eventually becoming fully ionized.

A partial ionization zone is an intermediate region, above which a certain chemical element is neutral or at a given level of ionization (e.g. where all He is in the He II, or He⁺, ionization state), and below which this chemical element is at the higher ionization level (e.g. where all He is in the He III, or He ²⁺, ionization state). The letterκ is the symbol of stellar opacity, quantifying how radiation diffuses from interiors outward. At the temperatures inside a star, electron scattering and free-free absorption associated to the free electrons contained in ionized matter dominates the opacity. Outside partial ionization zones, if a slight compression occurs, due to a small distur- bance in the star, then the temperature locally increases, causing a decreasing in the opacity since κ ∝ T^−3.5 according to the Kramers’ law. Consequently, the outward radiation flow increases, hence more energy is lost onto the upper layer than received for the lower layer, which results in a global loss of energy, and then in a decrease of the temperature and increase of opacity: the considered layer goes back to equilibrium. This phenomenon is referred to as radiative damping.

But what happens then in a partial ionization zone, e.g. where He II and He III coexist? If the same slight compression occurs is such a layer of the star, then the energy which would normally heat the zone mostly goes into increasing the He ionization. Consequently, this time the temperature does not substantially increase, and the opacity increases, hence no immediate outward counter- measure opposed to the contraction takes place. Moreover, because the considered layer becomes less transparent to radiation, more heat is retained beneath, causing a temperature increase, and then a pressure increase in the layer below (not in the partially ionized one), which is pushed out- wards. As a result, the partial ionization zone expands, cools down and become more transparent to radiation, enabling energy to escape from beneath: lower pressure drops, and the partially ionized layer falls inwards again. In that case, the star is unstable to pulsation, and any small variation is reinforced: the pulsation grows until the energy input by theκmechanism reaches a limit, then the star pulsates at a stable period and amplitude, as long as the layer responsible for pulsation keeps the same dimensions and properties. Obviously, a star can host various zones of different ionization for all chemical elements (though in most stars H and He dominate), each likely to trigger pulsation. Besides, the appearance of a significant and stable oscillation via theκmechanism is very dependent on the depth of the partial ionization zone: if the triggering zone is too deep in the star, it cannot drive matter movement against overlying layers ; if it is too close to the surface, then there is not enough matter above to drive a real pulsation.

Other mechanisms can also trigger stellar pulsations, such as themechanism, working in a similar way as theκmechanism, but this time where the temperature variations are influing on the nuclear reaction rate (of symbol ), and thus on the energy generation within the star. Pulsation can also result from the convection driving: here, the base of the convection zone blocks the flux from the interiors of the star for some time, driving to a certain storage of energy excess, which causes the expansion. This expansion enables to release the energy stored, and then the pulsating layer goes back towards the interior. In this mechanism, the convection zone behaves like a valve.

3Note that the description of stellar pulsation in this Section is also based on the lecture on pulsating stars pro- posed by Dave Kilkenny in the frame of the Optical Astronomy Courseshttp://optical-astronomy.education/

index.html, which is available online via the aforementioned website

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1.2. Short timescale variable types across the sky

Figure 1.3: Schematic description of surface distortions produced by pulsation modes with0≤l≤ 2, from Handler (2013). Outward moving areas are colored in dark grey, inward moving areas are colored in light grey. The pole and equator are indicated.

Finally, pulsations can also appear in intrinsically stable, non self-excited stars, such as the Sun, due to stochastic excitation of pulsation. Those stochastically excited stars pulsate because the strong convective motion in their outer surface layers generate acoustic noise in a broad frequency range, which excites the oscillations. As a consequence of the numerous convective elements on the surface, this excitation is somehow random.

Pulsating stars can pulsate in differentpulsation modes, which are usually divided in two groups: the radial pulsation modes and thenon-radial pulsation modes. In the first case, the star remains spherical, simply changing its volume, with matter moving along its radii. As in any oscillating systems, radial pulsations can occur in different modes, characterized by aradial order n corresponding to the number of nodes (i.e. number of lines with no movement) in the radial direc- tion, not counting the node at the center of the star. Whenn = 0, the star pulsates infundamental mode ;n= 1corresponds to thefirst overtonepulsators,n= 2to thesecond overtonepulsators etc...

In the case of non-radial pulsations, the motion of matter is not only along the radius of the star, whose shape can deviate from a sphere. Non-radial pulsation modes are characterized by two different numbers: thedegree lcorresponding to the number of nodes on the surface of the star, and mthe number of surface nodes running through the poles of the star. Figure 1.3 illustrates some non-radial pulsation modes. Both radial and non-radial pulsation modes can coexist, the complete pulsating mode of the star being then described by the three spherical components n, l andm.

In that sense, radial pulsation modes are a special subset of the non-radial pulsation modes, with l= 0. Note also that several different modes can be excited at the same time in the pulsating star.

Aside from their identification by the pulsational quantum numbersn,landm, non-radial pulsation modes are also classified intopressure (p)-modesand gravity (g)-modes, depending on the restoring force, namely the force attempting to bring the star back to its equilibrium. Radial pulsations are always p-modes: actually, in the case of radial compression, gravity tend to accelerate the inwards movement (as it varies as the inverse of the distance to the center), whereas pressure tends to stop it. On the other hand, gravity is the restoring force (through Buoyancy) in the case

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of purely transverse motion. Hence, non-radial pulsations (which can involve a superposition of radial and transverse movements) can be p- or g-modes.

A whole domain of research in astronomy is dedicated to the analysis of pulsating stars: the asteroseismology, whose key idea is to probe stellar interiors (chemical composition, structure) and deduce the stellar parameters of the considered source by detecting its pulsation modes and matching them to theoretical models (see e.g. Bradley 1998, for asteroseismology of pulsating white dwarfs).

When talking about pulsating stars, one cannot miss the concept of theinstability strip, which is by definition a specific region of the HR diagram occupied by pulsating stars. Figure 1.4 shows the position of different types of pulsating stars in the HR diagram, together with the “classical”

instability strip where e.g. Cepheid and RR Lyrae variable stars lie. This instability strip crosses the Main Sequence as well as the giants area, and groups pulsating stars whose pulsation is driven by theκmechanism associated to the partial ionization zone of He (where He II and He III coexist).

The blue edge of the instability strip is possibly determined by the He content of the outer layers and by the mass of the concerned stars, whereas its red edge is probably related to the occurence of convection beyond the pulsating layer, hence cancelling theκmechanism effect (Iben 1974). How- ever, we should not speak about THE instability strip, but rather ONE instability strip, in the sense that other instability strips have been be defined, each associated to a different partial ionization zone driving the pulsation (see e.g. Saio 1993 for the Z-bump instability strip, or Van Grootel et al.

2012 for the hydrogen instability strip of ZZ Ceti stars).

•Pulsating short period variables along the Main Sequence

As can be seen in Figure 1.4, various pulsating variable classes lie along the Main Sequence.

In the following paragraph, I focus on the types whose members can exhibit pulsation periods shorter than half a day.

TheδScuti starsare short period pulsators, with periods typically between30min and6h, and amplitudes up to a few tenths of magnitude in the optical. They are Population I stars, i.e. hot, luminous, young stars, mostly concentrated in the disk of our Galaxy. Main sequence or slightly evolved stars of spectral from A to F, they lie within the classical instability strip, and exhibit pulsations, generally radial though some non-radial oscillations can be excited, driven by the κ mechanism operating in the He partial ionization zone. High Amplitude δ Scuti stars (HADS), exhibiting radial pulsations with amplitudes greater than0.3mag, are usually distinguished from the “normal”δScuti stars which pulsate with lower amplitudes and several observed modes. An example of phase-foldedδScuti light-curve, from theCatalinasurvey⁴(Drake et al. 2014c), is presented in Figure 1.5. Sometimes engulfed in the same class, SX Phoenicis variable stars are the Population II pendant ofδScuti stars, i.e. very similar in terms of pulsational properties but rather old, less luminous, cooler, with fewer heavy elements, and usually located in globular clusters or in the nucleus of the Galaxy.

In the same region of the HR diagram, at the base of the classical instability strip, we find the rapidly oscillating Ap stars (roAp), discovered about 35 years ago (Kurtz 1982). Those rare chem- ically peculiar stars (i.e. showing overabundances of some rare-Earth elements such as strontium compared to the Sun) pulsate with very short period, from 5to25min, optical amplitudes up to 0.01mag, in p-mode, and oftenly exhibit multi-periodic behavior. After many years of debate, the driving mechanism of the roAp pulsations is thought to be (at least partly) theκmechanism operating in the hydrogen ionization zone. Another suggested excitation mechanism is the turbulent

4Catalinaphotometry from Data Release 2 is available athttp://nesssi.cacr.caltech.edu/DataRelease/

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10 CHAPTER 1. SHORT TIMESCALE VARIABILITY ACROSS THE SKY

Why not of a known pulsation type?

Jeffery & Saio (2016)

Figure 1.4: Schematic HR diagram with the positions of the different classes of pulsating stars, from Jeffery & Saio (2016). The classical instability strip is represented by the black dashed lines.

The colors mark roughly the spectral type. Shading corresponds to the type of modes and driving mechanism: opacity-driven p-modes (\ \ \), opacity-driven g-modes (///), and stochastically excited modes (≡). Vertical shading points the variables pulsating in strange modes, i.e. with pulsations concentrated in very specific regions of the star outer layers and whose pulsational properties differ from the overall properties of the star. Those modes will not be discussed here.

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Figure 1.5: Example of high-amplitude δ Scuti phase-folded light-curve in V band, from the Catalina Data Release 2 (Drake et al. 2014c). The period in the upper right corner of the image is given in days.

pressure in the convection zone (Cunha et al. 2013). Most of the known roAp stars have a strong global magnetic field, which make them interesting test cases for investigating interactions between magnetic field and stellar pulsations. At the moment of writing this manuscript, only about 60 roAp variable stars have been identified so far. With the expected end-of-mission photometric precision,Gaiashould enable to significantly increase the length of this known roAp list, offering great opportunities for investigating those specific and not yet fully understood pulsating stars.

At lower temperatures along the Main Sequence, one can find theγ Doradus variables, with typical periods from8h to3d and low optical amplitudes up to a few hundreds of magnitudes.

Those young pulsating Main Sequence stars generally oscillate in non-radial, g-modes, and oftenly exhibit multiple periods and/or amplitude modulation.

Finally, we can mention theβCephei pulsating stars, whose periods rank between2and12h, and with relatively low amplitudes in the optical domain, up to 0.05mag. Those very bright, early-B type, Main Sequence stars are non-radial p-mode pulsators, and remained pulsating stars without known cause for a long time. Actually, because of their high surface temperature, hydrogen and helium are already fully ionized in the outer layers ofβ Cephei stars , so the classicalκ mechanism on H or He ionization cannot operate. In fact, their oscillations are effectively driven by theκmechanism, but this time associated to the opacity of iron atoms: it is oftenly referred to as the iron bump (or Z bump). Figure 1.6 shows an example ofβ Cephei light-curve from the All Sky Automated Survey⁵(ASAS, Pojmanski 1997).

•Short period evolved pulsating stars

Among all the evolved pulsating stars, the most striking class entering our short timescale variability definition is the RR Lyrae class. RR Lyrae stars are horizontal branch stars, having evolved from the Main Sequence following the end of hydrogen fusion, with periods from5h to1.1d and amplitudes from 0.2 to 2mag. Those radial, metal-poor (population II) pulsators are common in globular clusters and can be detected in nearby galaxies, especially in the Magellanic Clouds.

They lie in the classical instability strip, hence their oscillations are triggered by theκmechanism through partial helium ionization, as δ Scuti stars. Apart from their intrinsic asteroseismologic properties, RR Lyrae stars are of particular interest as cosmic distance estimators, because of their

5TheASASphotometry is available athttp://www.astrouw.edu.pl/asas/?page=catalogues

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1.2. Short timescale variable types across the sky

Figure 1.6: Example ofβCephei star phase-folded light-curve inV band, plotted using photometric data fromASAS(Pojmanski 1997).

well-known and calibrated period-luminosity relation, enabling to derive their distance from their period and apparent magnitude without using astrometry. Hence, similarly to Cepheids, RR Lyrae stars have been extensively studied since the discovery of the class prototype in the 1890’s, with thousands of such variables known in our Galaxy and beyond.

The RR Lyrae variability type is divided in several subclasses, depending on the shape of the light-curves in the optical, as illustrated in Figure 1.7 (from theOGLEAtlas of Variable Stars⁶):

• the RRab stars, pulsating in radial fundamental mode, with a “saw-tooth” shaped light- curve, i.e. a steep brightness rise followed by a slower decline (see left panel of Figure 1.7).

This is the most common type of RR Lyrae stars.

• the RRc stars, pulsating in radial first overtone mode, with a nearly sinusoidal light-curve (see right panel of Figure 1.7), and generally shorter periods than the RRab variables.

• the RRd stars, or double-mode RR Lyrae, pulsating in both fundamental and first overtone radial modes

Moreover, some RR Lyrae stars show modulation in amplitude and shape, whereas the pulsation period remains the same. The origin of this phenomenon, illustrated in Figure 1.8 and known as theBlazhko effect, is still under debate.

•Short period pulsating hot subdwarfs

In this paragraph, I focus on pulsating, compact, low-mass, evolved stars on the Extreme Hori- zontal Branch of the HR diagram. Different classes of hot subdwarf pulsators have been identified until now, either of spectral type sdB (i.e. He-poor subdwarfs in core-helium burning phase) or sdO (i.e. He-rich progeny of the short-lived helium-shell burning phase). See e.g. Randall et al.

(2014) for a review on those pulsating hot subdwarf stars.

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Figure 1.7: Examples of RR Lyrae star phase-folded light-curves inV band, from theOGLEAtlas of Variable Stars. Left: RRab type ; right: RRc type.

Figure 1.8: Illustration of the Blazhko effect in the optical light-curve of the prototype RR Lyrae star, adapted from Kolenberg et al. (2006).