Calibrations and performances of the CHEOPS space mission for exoplanet science

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Thesis

Reference

Calibrations and performances of the CHEOPS space mission for exoplanet science

DELINE, Adrien

Abstract

To date, more than 4000 extrasolar planets, or exoplanets, have been discovered using different detection techniques, including the transit photometry. This method measures the dimming of the light coming from the host star when the planet passes in front of it. The Characterising Exoplanet Satellite (CHEOPS) is a space mission designed to perform photometric observations of bright stars to obtain precise radii measurements of small transiting planets. To achieve such performances, CHEOPS must be well characterised and, for this purpose, an intense on-ground calibration campaign was run on the fully-assembled instrument. In this thesis, I present the different components specifically developed for the characterisation and calibration testing of CHEOPS. I detail several key results obtained from these measurements that suggest CHEOPS is meeting all its requirements. This work contributed to the project along various axes that are critical to understand the data and to achieve optimal scientific performances.

DELINE, Adrien. Calibrations and performances of the CHEOPS space mission for exoplanet science. Thèse de doctorat : Univ. Genève, 2019, no. Sc. 5363

DOI : 10.13097/archive-ouverte/unige:121583 URN : urn:nbn:ch:unige-1215831

Available at:

http://archive-ouverte.unige.ch/unige:121583

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

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U G F S

Département d’Astronomie Professeur Didier Queloz

Calibrations and Performances of the CHEOPS space mission

for exoplanet science

T

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

mention Astronomie et Astrophysique

par

Adrien D

de

Annecy (France)

Thèse N^o5363

G

Observatoire Astronomique de l’Université de Genève 2019

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Résumé

Les planètes extrasolaires, ou exoplanètes, dont l’existence a été prouvée il y a plus de 30 ans, sont des planètes qui orbitent autour d’étoiles autres que le Soleil. Après les premières dé- couvertes, ce domaine de l’astronomie s’est considérablement développé et compte aujourd’hui près de 4 000 explanètes confirmées. Les deux techniques de détection les plus fructueuses sont la méthode desvitesses radialeset celle destransits. La première détecte des variations dans la spectroscopie Doppler dues au mouvement de l’étoile hôte, induit par l’interaction gravitationnelle avec la planète en orbite. La seconde méthode mesure, au cours du temps, la lumière venant de l’étoile (photométrie) et détecte l’atténuation causée par le passage de la planète devant le disque stellaire lors de son orbite. Outre le fait qu’elles fournissent la majorité des nouvelles découvertes, ces deux techniques sont complémentaires car elles permettent aux astronomes d’estimer deux propriétés clés des planètes extrasolaires : la masse, à partir des vitesses radiales, et le rayon, à partir des transits. Si les deux sont disponibles pour le même objet, la masse volumique (densité) peut être déduite et utilisée pour mettre des contraintes sur la composition intérieure de la planète ainsi que sur ses processus de formation et d’évolution.

À ce jour, la plupart des exoplanètes en transit ont été détectées par le télescope spatial Keplerautour d’étoiles qui ne sont pas assez brillantes pour être observées par les instruments de mesure de vitesses radiales installés au sol. Par conséquent, l’estimation de la masse est plus difficile et requiert l’utilisation d’autres techniques (p. ex. les variations de la chronométrie des transits) qui ne s’appliquent pas à tous les systèmes planétaires. Dans le but de faciliter les observations spectroscopiques depuis le sol, le satellite TESS (Transiting Exoplanet Survey Satellite) lancé récemment est en train de sonder 85% du ciel à la recherche de planètes en transit autour d’étoiles plus brillantes.

Dans ce contexte, la mission CHEOPS (Characterising Exoplanet Satellite) est conçue pour jouer un rôle clé en étant capable d’observer, une par une, des étoiles brillantes sur près de 70%

du ciel et de produire des mesures photométriques de haute précision. L’objectif principal de CHEOPS est double : la détection de planètes de la taille de la Terre autour d’étoiles similaires au Soleil, et la mesure précise de la taille de plus grandes planètes (typiquement de la taille de Neptune). Ainsi, CHEOPS sera à la fois capable de confirmer l’existence de candidats

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d’améliorer la précision sur les rayons d’exoplanètes déjà caractérisées. La mission recherchera également de nouvelles planètes avec des tailles allant jusqu’à celle de la Terre. Pour atteindre de telles performances photométriques, la charge utile de CHEOPS doit être bien caractérisée et, dans ce but, une intense campagne de tests de calibration a été menée au sol sur l’instrument intégralement assemblé.

Dans cette thèse, je débute par la présentation de la conception et des performances d’une source de lumière extrêmement stable que nous avons élaborée spécifiquement pour tester CHEOPS. La stabilité obtenue fut meilleure qu’attendue et fit de ce concept une des sources de lumière à large bande les plus stables au monde. Je détaille ensuite le développement et l’assemblage d’un banc de test (incluant la source super stable) qui a été utilisé pour caractériser trois CCD (charge-coupled devices). Les résultats permirent de sélectionner le meilleur de ces détecteurs photométriques afin de l’installer dans l’instrument. Dans le chapitre suivant, j’aborde la campagne de calibration de la charge utile de CHEOPS qui consista en une série de tests dans le but de mesurer la réponse de l’instrument dans différentes conditions (absence de lumière, illumination avec différentes longueurs d’onde, changements de température et de tensions d’alimentation, fréquence de lecture). Je présente le banc de calibration, les algorithmes de traitement des données et les résultats obtenus. Plusieurs fichiers de référence furent produits et délivrés auCentre des Opérations Scientifiques(SOC) de CHEOPS pour la réduction et l’analyse des images brutes durant les opérations. J’ai aussi analysé des séquences de mesures photométriques et démontré que CHEOPS est capable d’atteindre la précision requise par les objectifs de la mission.

Dans le dernier chapitre, je décris le code que j’ai développé pour extraire l’information photométrique des données de CHEOPS et déterminer précisément le rayon planétaire ainsi que d’autres paramètres du transit. J’ai appliqué cet algorithme à une série d’images produites par^CHEOPSim, le simulateur de CHEOPS, et montré que le traitement photométrique de bout en bout des données de CHEOPS fournira une précision sur les rayons planétaires telle que prévue par la mission.

De façon globale, le travail effectué durant cette thèse a contribué à la mission CHEOPS en abordant différents aspects du projet. L’optimisation et la caractérisation au sol de l’instrument, de même que la validation des objectifs basée à la fois sur des mesures et des simulations, ont été des étapes essentielles dans le but d’atteindre des performances photométriques de haute précision lorsque CHEOPS sera dans l’espace.

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Foreword

Extrasolar planets, or exoplanets, are planets that orbit stars other than the Sun and were proven to exist over three decades ago. Since the first discovery, this field of astronomy has grown exponentially and currently stands at about 4 000 confirmed exoplanets. The two most fruitful detection techniques are theradial-velocity method and thetransitmethod. The first detects changes in the Doppler spectroscopy due to the motion of the host star, induced by the gravitational interaction with the orbiting planet. The second method measures, over time, the light coming from the star (photometry) and detects the dimming caused by the planet passing in front on the stellar disk while orbiting its host. In addition to providing the majority of the new discoveries, these two techniques are complementary as they allow astronomers to estimate two key properties of the extrasolar planets: the mass, from the radial-velocity method, and the radius, from the transit method. If both are available for the same object, the bulk density can be inferred and used to constrain the interior composition of the planet and its formation and evolution processes.

To date, most transiting exoplanets have been detected with the Kepler space telescope around stars that are too faint to be followed up with the ground-based radial-velocity instruments. Thus, mass estimation is more difficult and relies on the use of other techniques (e.g. transit-timing variations), which are not applicable to all planetary systems. With the aim of facilitating spectroscopic observations from the ground, the recently launched Transiting Exoplanet Survey Satellite(TESS) is currently performing a photometric survey of 85% of the sky to detect transiting planets around brighter stars.

In this context, the mission of theCharacterising Exoplanet Satellite(CHEOPS) is designed to play a key role by being able to observe individual bright stars over 70% of the sky and provide high-precision photometric measurements. The main purpose of CHEOPS is twofold: the detection of Earth-size planets around Sun-like stars, and the precise determination of the sizes of larger planets (typically the size of Neptune). Thus, CHEOPS will be able to both confirm planet candidates, coming from TESS or ground-based (either photometric or spectroscopic) facilities, and improve the precision on the radii of previously characterised exoplanets. The mission will also search for new planets down to the size of Earth. To achieve such photometric

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on-ground calibration campaign was run on the fully-assembled instrument.

In this thesis, I first present the design and the performances of a super-stable light source that we developed specifically for the testing of CHEOPS. The resulting stability of the source was beyond expectations and made it one of the most stable broadband light sources in the world.

I then detail the components and the integration of a test bench (including the super-stable source) that was used to characterise three charge-coupled devices (CCD). Based on the results, one of the photometric detectors was selected as the flying model and installed in the instrument.

In the following chapter, I cover the on-ground calibration campaign of the CHEOPS payload that consisted of a series of tests to measure the instrument’s responses in different conditions (absence of light, illumination with different wavelengths, changes of temperature and voltages, read-out frequency). I introduce the calibration test bench, the algorithms used to process the data and the obtained results. Several calibration reference files were produced and delivered to the CHEOPSScience Operations Center(SOC) for the reduction and analysis of the raw data during the operations. I also analysed measured photometric sequences and demonstrated that CHEOPS can reach the precision required by the mission objectives.

In the last chapter, I describe the code I constructed to extract photometry from the CHEOPS raw data and accurately determine the planetary radius among other transit parameters. I applied the algorithm on images produced by the CHEOPS simulator ^CHEOPSimand showed that end- to-end photometric processing of CHEOPS data will provide precision on planetary radii as expected by the mission.

Overall, the work carried out during my PhD contributed to the CHEOPS mission by tackling different aspects of the project. The on-ground optimisation and characterisation of the instrument, as well as the validation of requirements with both measurements and end-to- end simulations, were essential steps to achieve high-precision photometric performances once CHEOPS is in space.

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Remerciements

Je sais déjà qu’il m’est impossible de remercier chacune des personnes avec qui j’ai interagi avant et durant ce travail et qui, de fait, m’ont amené là où je suis aujourd’hui. Je voudrais donc commencer par remercier ceux que je vais oublier, parce qu’ils sont tout aussi importants.

J’aimerais bien sûr remercier mon directeur de thèse, Didier Queloz, sans qui cette thèse n’aurait tout simplement pas eu lieu. Merci Didier de m’avoir permis d’accomplir ce travail de doctorat qui, à mi-chemin entre recherche et instrumentation, me convenait particulièrement.

Merci aussi pour tes conseils et ton enthousiasme qui m’ont aider à prendre du recul et progresser.

Je veux aussi m’adresser à David Ehrenreich. David, merci d’avoir encadré mon projet de fin d’études il y a maintenant plus de six ans, mais sans lequel cette thèse n’aurait probablement pas existé non plus. Merci aussi pour ta disponibilité et tes remarques pertinentes. Et merci enfin pour cette offre de post-doctorat (et la confiance dont elle témoigne) qui me permet aujourd’hui de continuer de travailler et évoluer sur ce projet.

Je voudrais également remercier l’équipe en charge de la calibration de CHEOPS avec qui j’ai beaucoup travaillé durant cette thèse : François Wildi, Bruno Chazelas et Michaël Sordet.

François, malgré nos débuts parfois tumultueux, je suis content que nous ayons réussi à nous apprivoiser et à collaborer. Merci d’avoir partagé tes connaissances et ton expertise qui m’ont beaucoup inspiré. Bruno, Michaël, j’ai vraiment apprécié travailler avec vous sur ce projet, parfois dans la douleur, le stress et l’insomnie, mais toujours avec bonne humeur !

Je suis aussi reconnaissant envers les différentes personnes que j’ai pu côtoyer dans le cadre du projet CHEOPS. Merci notamment à Andrea Fortier, Mathias Beck, Chris Broeg, Hector Gonzalez, Thibault Kuntzer, Pascal Guterman, Sergio Hoyer, Magali Deleuil et Willy Benz.

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l’Observatoire de Genève un lieu de travail exceptionnel. D’abord, à son directeur, Stéphane Udry, et son futur directeur, Francesco Pepe, pour leur capacité à instaurer un climat de travail détendu (mais efficace !) au sein de toutes les équipes. Merci aussi à Daniel Schaerer, Corinne Charbonnel et Sylvia Ekström pour leur bienveillance et leur disponibilité pour les doctorants.

Merci aux personnes du groupe Exoplanètes avec lesquelles j’ai pu échanger sur beaucoup de sujets : Ati, Janis, Romain, Manuela, Manu, Gaël, François B., Thibaut R., Aurélien, Monika, Christophe, Nathan, Maxime, Damien, Jean-Baptiste, Xavier, et tous les autres. Merci en particulier à Vincent, Baptiste et Nicolas avec qui j’ai beaucoup apprécié discuter au gré des meetings et autres conférences. Un grand merci également à Helen, Marianne, Uriel et Federica, avec qui j’ai partagé différents bureaux et de très sympathiques moments. Merci aux personnes travaillant sur les aspects de logistique, électronique, mécanique et informatique, pour leur sympathie et leur contribution essentielle au travail de l’Observatoire : Robin, Olivier, Gilles, Luc, Michel, Sam, Adrien, Nigel, Charles, Nicolas Bl., Nicolas Bu., Fabien, Julien, et tous les autres. Et bien sûr, merci auxFilles des fleurspour leurs sourires, leur bonne humeur, et pour le pétillant qu’elles apportent à la vie de l’Observatoire : Jaide, Livia, Myriam, Sophie, Françoise, Franca, Chantal, Mirka, Danuta, Claire, Jailda et Lucette.

Je tiens à remercier l’équipestellaire extended(William, Louis, Patrick, Florian, Stéphane, Thierry, Tim, Olga, Lionel et Arthur) qui m’a accueilli malgré mesdérives planétaireset avec qui je ne compte plus les heures que j’ai passées à la cafétéria à refaire le monde, avec plus ou moins d’entrain, d’énergie et de place au débat !

Anne, merci de m’avoir accompagné tous ces jours sur cette route parfois interminable, pleine de surprises et de bouchons inopinés... Mais surtout, merci pour ta bonne humeur, ta spontanéité et tous ces moments partagés, toujours sympathiques et parfois salvateurs.

Et merci à Anahí, Claude, Maroussia et d’autres avec qui j’ai pu échanger à de nombreuses reprises pendant mon travail de thèse. Merci aussi à Thibaut D., Thibault G., Floriane, Benjamin, Valentin, plus récemment arrivés, et qui contribuent déjà à la bonne ambiance qui règne à l’Observatoire.

Bref, merci à tous de permettre à l’Observatoire d’être un lieu de travail si particulier.

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Je souhaite évidemment remercier mes proches, pour qui ces mots me paraissent un bien maigre témoignage de ma reconnaissance. Tout d’abord, merci à ma famille, aussi complexe et insolite qu’elle m’a donné d’outils pour avancer et réussir dans la vie. Merci également à ma belle-famille pour son inconditionnelle bienveillance et son écoute. Merci aussi à tous mes amis. AuTriangle du Fier, pour tous ces moments partagés et plus ou moins assumés, et pour m’avoir sorti de ma léthargie pendant la rédaction de ce manuscrit. À Seb, pour cette belle amitié qui dure depuis tant d’années. À Tazia, Théo, Lulu, Françou, Tom, Nab et vos petits bouts, pour ces week-ends passés ensemble à discuter de médecine (mais pas que !) et qui me donne à chaque fois un sentiment de vacances. Merci à la Zentraler Omnibusbahnhof, pour cet acronyme d’abord, et pour ces chouettes moments passés ensemble à discuter et rigoler.

À Manu, Pascou et votre super famille, pour votre bonne humeur continuelle et votre simplicité exemplaire. Merci à Yo de nous faire voyager, de la Corse à la Nouvelle Calédonie... Merci aussi aux communistes, pour m’avoir appris à compter jusqu’à cinq en arabe, et pour tout ce temps perdu qu’on aurait pu passer à faire autre chose.

Bref, merci à tous d’avoir été présents avant et pendant ces années, et j’espère encore longtemps après !

Merci bien sûr à toi, Fanny. Les mots me manquent pour esquisser ne serait-ce que les contours de tout ce que tu m’apportes. Ton sourire, ta joie de vivre et ton soutien indéfectible font de moi un homme meilleur que ce que je n’aurais jamais été, et je t’en suis éternellement reconnaissant. Tu es la seule étoile autour de laquelle je veux tourner.

Et enfin, merci à toi. Tu n’as pas encore de prénom mais tu existes déjà bel et bien, et tu m’as permis de pouvoir me recentrer sur l’essentiel lors de moments difficiles. J’ai hâte de redécouvrir la vie avec toi.

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List of Figures

1.1 Illustration of the radial-velocity method to detect exoplanet . . . 4

1.2 Schematic view of a transit light curve . . . 6

1.3 Mass-radius diagram of extrasolar planets . . . 13

1.4 Diagram illustrating the orbital parameters . . . 15

1.5 Schematic representation of the transit and occultation events . . . 17

1.6 Geometry of a transit event with the planet crossing the stellar disk . . . 18

1.7 Normalised transit light curves of HD 209458 b . . . 20

1.8 Phase curve of the planet WASP-43 b . . . 24

1.9 Artist’s view of CoRoT in orbit and the "CoRoT eyes" . . . 31

1.10 Artist’s view of the Kepler spacecraft and location of its field of view. . . 33

1.11 Fields of view observed during the K2 mission . . . 34

1.12 Period-radius diagram of the Kepler and K2 confirmed exoplanets . . . 35

1.13 Artist’s view of the TESS spacecraft and the 26 observation sectors. . . 36

2.1 Expected noise contributions for transit detection around bright stars . . . 44

2.2 Expected noise contributions for transit characterisation around faint stars . . . 45

2.3 Positions of the CHEOPS orbital plane at different times of the year . . . 49

2.4 Observability constraints related to the Earth, the Moon and the Sun . . . 51

2.5 Area where CHEOPS is affected by the South Atlantic Anomaly . . . 51

2.6 CHEOPS sky coverage computed from observability constraints . . . 52

2.7 The CHEOPS spacecraft . . . 54

2.8 The CHEOPS payload . . . 56

2.9 Schematic of the CHEOPS sub-frame window extraction . . . 59

2.10 Daily CHEOPS sky coverage computed from observability constraints . . . 61

2.11 Normalised spectral transmissions of CHEOPS, TESS,GaiaandKepler . . . 65

3.1 Spectral power of the Laser-Driven Light Source from Energetiq . . . 69

3.2 Schematic view of the beam conditioner . . . 70

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3.4 3D views of the mounts holding the input and output fibers . . . 72

3.5 3D view of the attenuator . . . 74

3.6 Outgoing responsivity of the TIA-3000 sensor . . . 76

3.7 3D view and picture of the super-stable source . . . 77

3.8 Analysis of the output flux from the primary source . . . 79

3.9 Block diagram illustrating the working principle of the super-stable source . . . 80

3.10 Output flux and absolute gain as functions of the attenuator position . . . 81

3.11 Relative gain of the feedback loop with respect to the attenuator position . . . 82

3.12 Photometric performances of the super-stable source . . . 83

4.1 Schematic of a back-illuminated CCD . . . 86

4.2 Overview of a typical CCD layout . . . 88

4.3 Back-illuminated CCD from the CCD47-20 sensor family manufactured bye2v . . 90

4.4 Schematic of the CHEOPS CCD . . . 91

4.5 3D views of the Dewar used to test the CHEOPS detectors . . . 92

4.6 3D view and sketch of the integrating sphere . . . 93

4.7 Picture of the monochromator and schematic of one f/#-matcher . . . 94

4.8 The CCD test bench . . . 96

4.9 Bias structure visible before the use of the opto-coupler . . . 97

4.10 Time measurements of the shutter opening and closing . . . 98

4.11 Spectral profiles of the light coming out from the monochromator . . . 99

4.12 Spectral transmissions of the long-pass filters . . . 100

4.13 Illustration of the impact of the brighter-fatter effect . . . 103

4.14 Bias images in ADU for the left and right channels . . . 107

4.15 Dark frame at 40 C and average dark signal versus temperature . . . 108

4.16 Variance-versus-mean curves used to compute the system gain . . . 109

4.17 Variations of the system gain with respect to the temperature of the CCD . . . 109

4.18 Pixel response and non-linearity curves . . . 110

4.19 Non-linearity curves for different CCD bias voltages. . . 110

4.20 Overview of the PRNU images obtained at different wavelengths . . . 111

4.21 LocalPRNU computed for all wavelengths and different window sizes . . . 113

4.22 Relativequantum efficiency measured in nominal conditions . . . 114

4.23 Images from the visual inspection of the flight model #1. . . 115

4.24 System gain values plotted against VOD, VRDand VOG . . . 117

4.25 Variations with VRDof the coefficients from the VODpolynomial fits of the gain . . 118

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4.26 Residuals of the final polynomial fit of the gain for the left and the right channels . 119

5.1 Design of the collimator system . . . 123

5.2 Geometry of both ends of the fiber bundle and its image on the payload CCD . . . 125

5.3 Sketch of the pick-up system used in the point-source part of the FPM . . . 126

5.4 3D side view of the point-source part of the FPM . . . 126

5.5 3D view of the calibration bench as installed in Geneva . . . 128

5.6 Measurements of the non-uniformity of the integrating sphere . . . 129

5.7 Diagram of the calibration test bench . . . 131

5.8 Dark frame measured during preliminary tests with the EQM . . . 133

5.9 Non-linearity curves showing the read-out mode discontinuity . . . 133

5.10 Full-frame photo-response non-uniformity measured with the EQM . . . 134

5.11 Stray light contamination visible on dark frames during payload calibration . . . . 135

5.12 Full-frame image of the reference mask in front of the integrating sphere . . . 136

5.13 Measured redshift of the monochromator. . . 137

5.14 Illustration of the peppering effect . . . 138

5.15 Calibration images of the bias offset in ADU for both channels . . . 141

5.16 Oscillations of the bias levels measured during calibration . . . 142

5.17 System gain values measured during calibration for different CCD temperatures . . 144

5.18 Cubic spline interpolation of the calibration non-linearity measurements . . . 145

5.19 Dark current measured on the redundant channel in nominal conditions . . . 146

5.20 Comparison between different quantum efficiency curves . . . 148

5.21 Coverage of CHEOPS passband with the different filters . . . 149

5.22 Overview of the PRNU results of the payload calibration . . . 151

5.23 Measurement of the payload distortion . . . 152

5.24 PRNU images before and after sphere non-uniformity and distortion corrections . . 153

5.25 Synthetic image of the CHEOPS PSF over the field of view . . . 155

5.26 Evolution of the PSF radius with the FoV and the wavelength . . . 156

5.27 Long-term stability of the four CCD bias voltages . . . 158

5.28 Normalised spectralelectrondistributions of the reference PRNU. . . 160

5.29 Weighted spectralelectrondistributions of the calibration PRNU . . . 162

5.30 Results of the synthesis of the Tungsten lamp PRNU . . . 162

5.31 Raw light curve and flux variations measured by the photometer . . . 164

5.32 Cross-correlation of the CCD and the photometer curves . . . 165

5.33 CCD light curve corrected for the source variability . . . 166

5.34 Temperatures of the optical table and in the room . . . 167

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5.36 Residual CCD flux after correction of the lab temperature correlation. . . 168

5.37 Noise curve of the residual CCD light curve . . . 169

5.38 Photometric precision for two sub-samples of the final light curve . . . 169

5.39 Noise curves of the flux extracted from five 8⇥8-pixel sub-frames. . . 171

6.1 Transit light curves simulated by^CHEOPSimfor stellar types from G0 to M9. . . 175

6.2 Roll angle of the satellite as computed by^CHEOPSimover one orbit . . . 176

6.3 Point spread functions implemented in^CHEOPSim . . . 177

6.4 CHEOPS PSF as simulated from optical model of the telescope. . . 179

6.5 Pointing jitter as simulated for CHEOPS in idealistic conditions . . . 181

6.6 Diagram illustrating the transformation of photons to ADU in CHEOPS . . . 182

6.7 Illustration of the background annulus centered on the PSF . . . 184

6.8 Bivariate Gaussian distributions with standard deviations defined by Eq. 6.3 . . . . 187

6.9 Annulus rotating areas used to compute the background level . . . 189

6.10 Radial profiles of the weighting functions . . . 191

6.11 Background field of BD-082823 surrounding a K5 star with a V-mag of 12. . . 192

6.12 Background light curves obtained for the two cases . . . 193

6.13 Coordinates of the PSF center obtained for the two cases . . . 194

6.14 Phase-folded light curves obtained with our algorithm for the two cases . . . 195

6.15 Illustration of the covariance matrix in the Gaussian processes . . . 200

6.16 Examples of traces of MCMC chains sampling a posterior distribution . . . 203

6.17 Gaussian process correction of a light curve contaminated by background stars. . . 204

6.18 Transit light curve extracted from^CHEOPSimdata with a dense background . . . 205

6.19 Definition of the error and the uncertainty from the marginalised distribution . . . 207

6.20 Transit light curve of the Earth-size planet in front of the Sun-like star . . . 211

6.21 Marginalised posterior over transit parameters for the Earth-size planet . . . 212

6.22 Transit light curve of the Neptune-size planet in front of the K5V dwarf star . . . . 213

6.23 Marginalised posterior over transit parameters for the Neptune-size planet . . . 214

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List of Tables

2.1 Parameters of the CHEOPS acquisitions as a function of exposure time . . . 60 3.1 Results of the noise tests of the feedback loop . . . 78 3.2 Photometric stability performances of the super-stable source . . . 84 4.1 List of the photo-response defects . . . 112 4.2 Values of the CCD parameters at which the system gain has been measured . . . . 116 5.1 Original and new configurations of the clocking and power voltages of the CCD . . 137 5.2 System gain, maximum non-linearity and full-well capacity . . . 143 5.3 List of the "warmest" pixels detected on the CCD . . . 147 5.4 Long-term stability measured during the payload calibration . . . 159 5.5 Coefficients of the linear combination used to fit the source spectrum . . . 161 6.1 Precision of the different centroiding methods with the CHEOPS PSF . . . 188 6.2 Performances obtained with the different photometric apertures . . . 191 6.3 Values estimated by the algorithm from the blank and dark pixels . . . 193 6.4 Errors and uncertainties obtained from simulations using the measured PSF . . . . 207 6.5 SNR onRp/R?obtained for the five cases without and with cosmic rays . . . 209 6.6 Photometric performances achieved with our tool and the DRP . . . 215

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List of Acronyms

ADC Analog-to-Digital Converter

ADU Analog-to-Digital Unit

AOCS Attitude and Orbit Control System

CCD Charge-Coupled Device

CHEOPS Characterising Exoplanet Satellite

CR Cosmic Ray

CTE Charge-Transfer Efficiency CTI Charge-Transfer Inefficiency

DMM Digital Multimeter

DRP Data Reduction Pipeline

EE90 90% of Encircled Energy

EGSE Electrical Ground Support Equipment

EM Engineering Model

EQM Engineering-Quality Model

ESA European Space Agency

f/# f-number

FEE Front-End Electronics

FF Flat Field

FITS Flexible Image Transport System

FM Flight Model

FoV Field of View

FPM Focal Plane Module

FWC Full-Well Capacity

FWHM Full-Width at Half Maximum

GP Gaussian Process

GTO Guaranteed Time Observation

IR Infrared

iwcog Iterative Weighted Center of Gravity

LD Limb Darkening

LDLS Laser-Driven Light Source

LED Light-Emitting Diode

LoS Line of Sight

LTAN Local Time of the Ascending Node

MCMC Markov Chain Monte Carlo

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NGTS Next-Generation Transit Survey

NL Non-Linearity

OAP Off-Axis Parabolic (mirror) PDF Probability Density Function

PID Proportional-Integral-Derivative (controller)

PITL Payload In The Loop

PRNU Photo-Response Non-Uniformity

PSF Point Spread Function

QE Quantum Efficiency

RMS / rms Root Mean Square

RON Read-Out Noise

RV Radial Velocity

SAA South Atlantic Anomaly

SED Spectral Energy Distribution

SNR Signal-to-Noise Ratio

SOC Science Operation Center

SSS Super-Stable Source

SV3 SOC validation cycle 3

TESS Transiting Exoplanet Survey Satellite TTL Transistor-Transistor Logic (signal)

TVC Thermal-Vacuum Chamber

UV Ultraviolet

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Chapter 1 Introduction

1.1 The extrasolar planets

1.1.1 History

The five closest planets of our solar system – Mercury, Venus, Mars, Jupiter and Saturn – have been known since ancient times from naked-eye observations. They were mistakenly believed to be fast-moving stars and were named "planets" after the ancient greek plan¯etes asteres ( ´ ’ ´ ), meaning "wandering stars". In the 16^thcentury, the mathematician and astronomer Nicolaus Copernicus published a heliocentric theory stating that these objects are in fact orbiting the Sun (Copernicus 1543). This model was improved at the beginning of the 17^th century by a German scientist named Johannes Kepler who established the three laws of planetary motion: the orbit of a planet is an ellipse with the Sun at one of the two focuses, the line joining a planet and the Sun sweeps out equal areas in equal intervals of time, and the square of the orbital period of a planet is proportional to the cube of the semi-major axis of the orbit.

After the discovery of Uranus by William Herschel in 1781, these powerful rules were used to predict the future locations of the planet on the sky and led to the detection of irregularities in its orbit. These systematic discrepancies were intensively analysed by Urbain Le Verrier and led him to predict mathematically the position of the object perturbing gravitationally the orbital motion of Uranus (Le Verrier 1846). The astronomer Johann Galle observed the indicated coordinates a few weeks later and discovered Neptune, the last planet of our solar system, less than 1 from the predicted location. As of today, numerous other objects orbiting the Sun have been discovered, such as Ceres (Piazzi 1802), Pluto (Tombaugh 1946) or Sedna

1

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(Brown et al. 2004). This brought the International Astronomical Union (IAU) to clarify, in 2006, the classification of the solar system bodies and to define four categories: the planets, the dwarf planets, thesatellitesand thesmall solar system bodiesthat regroups all the other objects orbiting the Sun. According to this updated definition, a planet is “a celestial body that is in orbit around the Sun, has sufficient mass for its self-gravity to overcome rigid body forces so that it assumes a hydrostatic equilibrium (nearly round) shape, and has cleared the neighbourhood around its orbit”.

With such a richness surrounding our Sun, scientists and philosophers have been speculating on the existence of similar systems around others stars for centuries. The first discoveries of extrasolar planets, also called exoplanets, started to be claimed at the end of 19^th century (See 1896) and, even though they were later discredited, this shows that astronomers believed more and more in their capability of finding new worlds. The first instruments dedicated to the search of extrasolar planets were developed in the 1980s (Vogt 1987) and the newly acquired observations pushed the scientists to suspect the presence of planets around other stars (Campbell et al. 1988). This is finally in 1992 that the first detection of planets outside our solar system was confirmed (Wolszczan & Frail 1992). These two exoplanets are orbiting a star very different from the Sun: the pulsar PSR B1257+12. Pulsars are neutron stars (collapsed cores of a giant stars after their death in a supernova explosion) that rapidly spins while emitting electromagnetic jets, which creates a pulsating signal similarly to the rotating beam of a lighthouse. It is a few years later, in 1995, thatMayor & Queloz(1995) announced the discovery of an exoplanet orbiting the Sun-like star 51 Pegasi. Although the host star has similarities with our Sun, the orbiting companion was shown to fall outside the scope covered by our solar system (four terrestrial inner planets and four outer gas giants). Indeed, this extrasolar object is a giant planet, with a mass of about half the one of Jupiter, located extremely close to its star (10 times closer than Mercury with respect to the Sun). Aside from being a revolution in itself, this discovery forced the scientist to question their model of planetary formation to include this new, and then unexpected, category of planets calledhot Jupiters.

After the confirmation of the existence of exoplanets around main-sequence stars, a new era of astronomy began aiming at searching and studying these stellar companions. In a quarter century, this field literally exploded benefitting from new techniques and facilities, which led to more than 4000 confirmed exoplanets as of today. With such a large sample and the improvement of instrumental precision, astronomers have now steered the area of extrasolar planets from detection to characterisation.

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. . The extrasolar planets 3 1.1.2 Detection techniques

1.1.2.1 Radial velocity

When two bodies are linked gravitationally, their interaction can be represented as a reciprocal force that attracts one toward the other. If these objects are moving around each other, the centrifugal force counters the gravity placing the two bodies in orbit around the center of mass of the system. When observing two celestial bodies in such a binary system, the plane in which the two orbital trajectories are contained can have any orientation with respect to the observer.

If this plane is not perpendicular to the line of sight, the motion of the bodies around each other has a component along the direction to the observer. This oscillating component adds up to the radial velocity (RV) of the center of mass of the system and its amplitude increases with the angle of the orbital plane from the perpendicular position. The radial velocity of an object can be detected byDoppler effect(Doppler 1842). This effect describes that a wave (light or sound) emitted from a receding source is received with a lower frequency (redder or lower pitch) while it is shifted toward higher frequencies (bluer or higher pitch) for approaching sources. This has been used extensively prior to the discovery of exoplanets to detect and study binary stars from their apparent change of color.

In 1952, Struve (1952) foresaw the possible detection of planets with this technique as, according to him, there was a priori nothing preventing giant planets from being closer to their parent star than what is seen in the solar system. Indeed, given the small mass of planets compared to star, the signal was expected to be much smaller but still detectable if the planet was close enough. This technique is an indirect detection of a planet by its effect on the position of its host (see Fig.1.1).

The radial velocity of a star around which orbits a planet is given by:

vr =v +K1[cos(!+⌫)+ecos!] , (1.1) wherevr is the radial velocity,v is the velocity of the system,K1is theradial velocity semi- amplitude,!is theargument of periastron,⌫is thetrue anomalyandeis the eccentricity. The semi-amplitudeK1can be written as a function of the eccentricity, the period and the two object masses (Cumming et al. 1999) :

K1 = 1 p1 e²

✓2⇡G

P

◆1/3 Mpsini

M?+Mp 2/3 , (1.2)

where Gis the gravitation constant, P is the orbital period,iis the inclination of the orbital plane (90 is perpendicular to the line of sight), and Mp andM?are the masses of the planet and the star respectively. The observation of the variations ofvr with time allows to estimate the period and the eccentricity of the orbit. Therefore, knowing P ande, the radial velocity

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Figure 1.1:Illustration of the radial-velocity method to detect exoplanet. The trajectories of the exoplanet and its host around their common center of mass are represented by theyellow dotted lineswith thearrowsshowing the directions of rotation. The star is depicted at two locations on its orbit. Theblueline illustrates the shift of the stellar light to shorter (bluer) wavelengths when the star approaches the observer on Earth (lower left). Theredline is the opposite effect when the star recedes from the Earth and its light is shifted toward longer wavelengths (red-shifted). Source: European Southern Observatory(ESO)

amplitude sets constraints on the relation between the inclination and the masses of the star and the planet. Given that the stellar mass can be estimated independently from its luminosity or its spectral type, theminimummass of the planet,Mpsini, can be obtained from RV measurement.

However, this means that, without being able to determine the inclination of the orbital plane, the exact mass of the planetary companion cannot be assessed.

The radial velocity is estimated from observations of the star via Doppler spectroscopy. The shift in wavelength is measured from the locations of absorption and emission features in the stellar spectrum. Assuming non-relativistic conditions, the relation between the radial velocity and the wavelength shift is then given by:

vr

c = ⁰

0 , (1.3)

where c is the speed of light, is the measured wavelength and 0 is the rest (reference) wavelength.

The radial velocity technique has been a fruitful detection method allowing the discovery of more than 700 extrasolar planets such as the first exoplanet orbiting a Sun-like star (Mayor &

Queloz 1995), planets with very high eccentricities such as the planet HD 80606 b (Naef et al.

2001), planets in the habitable zone such as GJ 581 c (Udry et al. 2007), systems with multiple

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. . The extrasolar planets 5 planets like 55 Cnc (Fischer et al. 2008), HD 10180 (Lovis et al. 2011) and HD 40307 (Tuomi et al. 2013) and the terrestrial planet orbiting the nearest star Proxima Cen (Anglada-Escudé et al. 2016). These achievements have been possible using spectroscopic instruments able to measure radial velocities with a precision that, in 20 years, improved from a few meters per second with Keck-HIRES (Vogt et al. 1994) or Euler-CORALIE (Queloz et al. 2000b), to less than 1 m/s with ESO-3.6m-HARPS (Mayor et al. 2003) to an order of magnitude better with the new VLT-ESPRESSO (Pepe et al. 2010). Other instruments have also been designed for the infrared part of the spectrum like CARMENES (Quirrenbach et al. 2011), SPIRou (Delfosse et al. 2013) or NIRPS (Conod et al. 2016) to detect planets around red dwarfs. The precision limits might be again pushed by a factor of ten with the HIRES (Marconi et al. 2016) spectrograph to be installed on the new European extremely large telescope (E-ELT) in 2024.

1.1.2.2 Transits

Another method for the detection of exoplanets, and also probably one of the most intuitive, is the observation of the eclipse of the host star by its companion. Indeed, depending on the orientation of the orbital plane, the planet can pass periodically in front of its star with respect to the line of sight. During this event, the orbiting companion transits across the stellar disk occulting part the emitted light, which creates a drop of flux that can be detected by an observer (see Fig.1.2). In first approximation, we may consider spherical celestial bodies and a uniform stellar disk and write that the relative flux decrease is proportional to the ratio of the planet-to-star disk areas:

F F0 ⇡

✓Rp

R?

◆2

, (1.4)

whereF0is the stellar flux not obscured by the planet, Fis the flux drop, RpandR?are the planet and star radii. From this equation, one sees directly that the observation of a transit event can provide a relation between the radius of the planet and the radius of the star. Similarly to the mass, the radius of a star can be derived from independent characterisation (luminosity, parallax, spectral type) and thus, the size of the planet can be inferred directly. However, the drop of flux caused by the partial occultation of a star by its planet is very small and difficult to detect. For instance, Jupiter in front of the Sun corresponds to a drop of about 1%, whereas Neptune, the Earth and Mars have an effect of 1.3⇥10 ³, 84⇥10 ⁶and 24⇥10 ⁶respectively.

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Time

Brightness

Figure 1.2: Schematic view of a transit light curve (bottom) and the corresponding positions of the planet (brown) in front of its host star. The dashed line represents the orbital trajectory of the companion. The dotted lines relate the apparent brightness on the light curve to the location of the planet with respect to the stellar disk.

For the transit event to occur, the trajectory of the planet must pass between the stellar disk and the observer and hence the orbital plane must have a specific orientation. The probability of a planet on a circular orbit to be aligned favourably for a transit is given by (Borucki &

Summers 1984) :

p(transit|planet,e=0)= R?

a ⇡0.005✓R?

R

◆ ⇣ a

1 AU

⌘ 1

, (1.5)

where R?is the stellar radius, ais the semi-major axis, R is the solar radius and AU is for astronomical unit(⇡149.6⇥10⁶km).

Despite this relatively low probability, the transit technique has provided, by far, the highest number of exoplanet discoveries with more than 3000 detections. The transit observations started on systems already known to have planets from radial velocity measurements, such as the first transit detection of HD 209458 b achieved independently by Charbonneau et al.

(2000) andHenry et al.(2000). The first planet discovery from the photometric survey OGLE occurred three years later at the end of 2002 (Konacki et al. 2003). In the dynamics of the first transit discoveries, ground-based facilities were built to perform surveys observing wide portions of the sky to detect transiting exoplanets: the HAT project (Bakos et al. 2002;Bakos 2018), WASP (Pollacco et al. 2006;Collier Cameron et al. 2009), KELT (Pepper et al. 2004),

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. . The extrasolar planets 7 MEarth (Charbonneau et al. 2008), NGTS (Wheatley et al. 2013), TRAPPIST (Gillon et al.

2012), SAINT-Ex (Sabin et al. 2018), SPECULOOS (Gillon et al. 2013). These surveys led to many discoveries such as HAT-P-1 b orbiting one member of a stellar binary (Bakos et al.

2007), HAT-P-14 b with a nearly grazing transit (Torres et al. 2010) in a retrograde orbit (Winn et al. 2011), the very short-period planet WASP-19 b (Hebb et al. 2010), WASP-80 b creating one of the largest transit depths ever observed (Triaud et al. 2013), one of the nearest transiting systems GJ 1132 (Berta-Thompson et al. 2015) and the famous multiple system TRAPPIST-1 (Gillon et al. 2016). Instruments for transit detection have also been sent to space to improve the photometric precision by not observing through the Earth atmosphere. Launched in 2006, the spacecraft CoRoT (Baglin et al. 2007;Auvergne et al. 2009) was the first space mission dedicated to exoplanets and discovered a few tens of exoplanets among which the first transiting rocky planet CoRoT-7 b (Léger et al. 2009). The next space missionKepler(Koch et al. 1998;

Borucki et al. 2010) started in 2009 and participated to the explosion of the field with more than 2700 confirmed exoplanets and over 2900 potential ones (candidates). Among those objects, one can mention the smallest discovered exoplanet Kepler-37 b that is slightly larger than the Earth’s Moon (Barclay et al. 2013), the very short period planet Kepler-78 b (Sanchis-Ojeda et al. 2013), the planetary system Kepler-444 almost as old as the Universe (11.2 billion years) (Campante et al. 2015), and the 8-planet system Kepler-90 (Batalha et al. 2013;Cabrera et al.

2014; Schmitt et al. 2014; Shallue & Vanderburg 2018). More recently, the TESS space mission (Ricker et al. 2015) started its science operation in July 2018 and already provided a few new confirmed exoplanets (Gandolfi et al. 2018;Rodriguez et al. 2019;Wang et al. 2019;

Trifonov et al. 2019;Vanderspek et al. 2019). In the future, new space mission dedicated to the photometric observations of transiting exoplanets such as CHEOPS (Broeg et al. 2013;Fortier et al. 2014) or PLATO (Rauer et al. 2014) will come and participate to the improvement and expansion of the knowledge of transiting extrasolar planets.

1.1.2.3 Direct imaging

The direct imaging consists in taking a picture of the system on which the exoplanets are visible.

This is the only detection method that directly visualises the extrasolar companions and hence is a significant technical achievement. First, the contrast between the brightnesses of the planet and the star is extremely low, being of about 10 ⁹for Jupiter and the Sun observed at 10 parsec and 10 ¹⁰ for the Earth. Even though these values depend on the wavelengths considered and can be improved up to 10 ⁵in the infrared, it requires the removal of nearly all the stellar light in order to have a chance to see the planets. This is done using coronagraphs installed on the optical path of telescopes that optically blocks most of the stellar flux before illuminating the detector (Baudoz et al. 2000;Debes et al. 2002;Soummer et al. 2003;Rouan et al. 2000).

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The second considerable challenge for the direct imaging method is the correction of the atmospheric effects. As the light crosses the Earth’s atmosphere, it suffers the perturbations of turbulent layers that change the refractive index and hence degrade the incoming wavefront.

This causes the scintillation of stars as seen with the naked eye and limits the precision of the observations from the ground (seeing-limited images). The technology that corrects the wavefront entering the telescope from the atmospheric perturbations is calledadaptive optics and has been suggested byBabcock(1953) before being intensively developed from the 1990s on (Beckers 1993;Davies & Kasper 2012). Adaptive optics gives very impressive results allowing observations to be made in a regime really close to the diffraction limit of the telescope aperture (diffraction-limited images). Combined with coronagraphs, this technology allowed the direct imaging of various exoplanets including the four-planet system HR 8799 (Marois et al. 2008, 2010) and the gas giant around the young star Pictoris surrounded by a dust disk (Lagrange et al. 2009). Among the instruments that contributed to the discoveries of exoplanets through direct imaging, one can mention the VLT-NACO (Girard et al. 2010), the Keck-NIRC2 (McLean

& Sprayberry 2003), the VLT-SPHERE (Beuzit et al. 2008) and the Gemini-GPI (Macintosh et al. 2006).

1.1.2.4 Astrometry

The astrometry consists in measuring the precise position of the stars on the celestial sphere.

The detection of exoplanets relies on the fact that the stellar host will wobble on the sky plane due to gravitational interaction with the companion. Complementary to the radial velocity, this technique is optimal for orbital planes perpendicular to the line of sight. Depending on the exoplanet catalogue available online, the astrometry has found none, one, or eight planets. In any case, this technique remains much less fruitful than the other ones used in the search for extrasolar planets. The spacecraft Gaia(Perryman et al. 2001) launched in 2013 is aiming at measuring with a very high precision the positions and proper motions of about one billion stars of our galaxy. Based on this new three-dimensional map, astrometry might be able to make high-confidence detections of new exoplanets. This population about to be detected should be typically composed of massive planets (gas giants) located at a distance up to 200 parsec with orbital periods between 0.2 and 6 years.

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. . The extrasolar planets 9

1.1.2.5 Gravitational microlensing

The gravitational microlensing is a technique that is based on the general relativity, and more precisely, on the bending of light rays due to the distortion of space-time by a massive object.

This bending effect caused by gravity is comparable to what is produced by optical lenses and hence is called gravitational lensing. From the observer point of view, the deviation and focusing of the light rays corresponds to an increase of received flux. For this effect to occur, the "lens" must be located in the foreground of a far object and, given that the two systems move independently, the lens will align once with the background source. The light rays emitted by the source will be bent and directed toward the observer, which will trigger a one-time magnificationevent. This phenomenon can occur when a planetary system passes in front of a distant star in the galaxy, with the host star and its planet acting as the lens and the distant star as the background source. The time-varying magnification of the received flux can present various features with some that can be identified unambiguously as a lensing effect caused by a planetary companion. A bit more than 50 exoplanets have been discovered with this technique (Bond et al. 2004;Beaulieu et al. 2006;Han et al. 2017), mostly through the OGLE (Udalski et al. 1993) and MOA (Muraki et al. 1999) projects.

1.1.2.6 Timing

The last method for the detection of exoplanets is the measurement of timing variations of events that have predictable periodic occurrences. Pulsars produce extremely stable periodic signals at very high frequencies. In the presence of orbiting companions, the host is dragged back and forth by gravitational interaction with its planets and the variations of distance to the Earth implies change of light travel time that modifies the arrival time of the signal. Accurate measurements of these time delay oscillations can be used to detect the presence of exoplanets as done for the first exoplanet discovery byWolszczan & Frail (1992) and for a dozen other ones (Bailes et al. 2011;Spiewak et al. 2018). This method applies similarly to pulsating stars (Silvotti et al. 2007) and to eclipsing binaries (Lee et al. 2009;Beuermann et al. 2010).

1.1.3 Characterisation of exoplanets

Given the large number of extrasolar planets that have been discovered with the different methods described previously, we have now the possibility to study statistically the population of exoplanets and infer properties on their natures. The wide diversity of these systems (Udry

& Santos 2007;Howard 2013;Winn & Fabrycky 2015) have provided a sample that covers different types of planets (sizes and masses), types of stars, distances between the hosts and the companions or even the number of stars or planets in a given system. This is both an incredible

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opportunity to understand extrasolar planets and a challenge for the modelling of formation processes or interior compositions.

The first aspect that could be estimated from this large population of exoplanets is the occurrence rate of planets around stars in the solar neighbourhood. In particular, the average number of planets per star was estimated of about 0.4 for planetary sizes larger than the Earth and periods shorter than 50 days (Dong & Zhu 2013; Petigura et al. 2013a,b). For longer periods up to 300 days, the occurrence rate of small planets (0.75-2.5 Earth radii) seems to raise to 0.77 per G- or K-type stars (Burke et al. 2015).Fressin et al.(2013) also calculated that nearly one out of six stars with a spectral type F, G or K has at least one planet with an orbital period up to 85 days and a radius between 0.8 and 1.25 the one of the Earth. When extrapolating these numbers to our entire galaxy, this would mean that the Milky Way contains billions of extrasolar planets. The gaseous giant exoplanets are located at various distances from their host and often contain most of the planetary mass of the systems they belong to. However, they do not dominate in number the planetary population that is composed essentially by celestial bodies ranging from super-Earths to sub-Neptunes (Lovis et al. 2009; Howard et al. 2010;

Mayor et al. 2011;Petigura et al. 2013b;Fressin et al. 2013). Another particularly interesting occurrence rate is about the rocky planets located at a distance from their stellar host that allows the presence of liquid water at their surface. This region, where the Earth also lies, receives just enough flux from the host star to have a surface temperature at which the water can remain in liquid state, neither frozen nor evaporated. It is namedhabitable zoneeven though this does not mean the planet is actually habitable. There is a planetary candidate (still to be confirmed according to Mullally et al. 2018) called Kepler-452 b (Jenkins et al. 2015) that would be an Earth-like object (1.6 R ) being in the habitable zone of a Sun-like star (G2 spectral type).

However, given their cooler temperature, M dwarf stars emit less radiation and have a habitable zone much closer to them. Thus, as the detection of exoplanets is easier for close-in orbits, most of the other ones orbiting in the habitable zone have been found around M dwarf stars.

Furthermore, it has been shown that extrasolar planets located at these distances from their cool hosts are not rare (Bonfils et al. 2013;Dressing & Charbonneau 2015), as in the case of the system TRAPPIST-1 with three of its planets in the habitable zone (Gillon et al. 2017).

Another significant aspect that is challenged from the impressive sample of known exoplanets concerns the planet formation and evolution processes. The architecture of our solar system is made of four inner rocky planets and four outer gas giants, all placed on nearly coplanar circular orbits except for Mercury (e ⇡ 0.2). This is, by far, not representative of the variety of extrasolar systems where one can find very elliptical orbits (e > 0.3) and very strongly irradiated gas giants (hot Jupiters) that have probably not formedin situ. The occurrence of highly inclined orbital planes is not extremely low with even retrograde motion, and some

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. . The extrasolar planets 11 multiple systems are very compact with phenomena of mean motion resonances. The stellar hosts are also very different from the Sun with varying spectral types and metallicity. As a consequence, the models of planetary formation and evolution had to be revised to explain such a diversity. The current state assumes that a star forms from a molecular cloud of gas and dust grains (sub-micron solid particles mainly composed of carbon, silicon and oxygen) that starts to collapse due to gravitational instabilities. During the collapse, the cloud goes through fragmentation and accretion and the initial tiny motion of the cloud induces a global cloud rotation by conservation of angular momentum. This rotation allows some of the gas and dust to remain in equilibrium as the centrifugal force balance the gravity, which creates aprotoplan- etary disk. At the center of the disk, the falling material contributes to the increase of density and temperature where a proto-stellar object forms. Gravitational interactions and collisions within the protoplanetary disk make the dust grains to aggregate into pebbles, rocks, planetes- imals and finally planetary cores, creating planets on timescale from a few (small terrestrial planets) to more than 100 million years (Alexander et al. 2014). Depending on the formation environment and mainly if located beyond the ice line (where volatile compounds condense in solid grains), the core can grow massive as more solid material is present and hence accrete even more gas to form a giant planet eventually. This formation process is calledaccretionand describes this gravitational aggregation of matter (Safronov 1969;Bodenheimer 2006). In the case of the formation of gas giants, another scenario has been proposed that circumvents the initial formation of a core: the direct contraction of gaseous clumps throughgravitational disk instabilities(Chabrier et al. 2014). To explain the existence of hot Jupiters, some systems must have seen their giant planets move from where they formed (outer part of the disk) to where they stand. This planetarymigrationcan be explained by either interaction with the gas disk (Goldreich & Tremaine 1980;Lin & Papaloizou 1993;Papaloizou & Terquem 2006),planet- planet scattering(Weidenschilling & Marzari 1996) or evenLidov-Kozaioscillations (Fabrycky

& Tremaine 2007). Using the ALMA sub-mm interferometer (ALMA Partnership et al. 2015), the DSHARP project was able to take unprecedented images of various protoplanetary disks including HT Lup, HD 142666, HD 143006, AS 209 and HD 163296 (Andrews et al. 2018).

These highly-detailed pictures show the spatial distribution of densities and temperatures of the particles composing the disk and can be analysed in terms of symmetry, spacings, spatial scales or other morphological aspects. The observations and studies of the properties of the circumstellar disks are used to establish the initial conditions of planetary systems and improve formation and evolution models.

One of the key aspects of the exoplanet characterisation is the estimation of their interior and atmospheric compositions. From the radial-velocity measurements, one can obtain an estimate of the mass of the planet (or the minimum mass at least) and, in parallel, if the

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planet transits, photometric observations can be done to measure its radius. Using these two numbers, we can infer the bulk density and hence give constraints on the material composing the interior structure. They can also be used to compute themass-radius diagram(see Fig.1.3) on which some theoretical chemical compositions of the planet interior can be represented.

These theoretical curves are based on imaginary models and provide hints on the nature of the planets allowing one to quickly see if the object is likely terrestrial (high density) or surrounded by a large atmosphere (low density). The pure water curve (100% H2O) is often represented as the density limit for a planet with no atmosphere, indicating that any object above this line necessary has a large gaseous envelope. Obviously, reality is more complicated and the planetary interiors are modelled by more complex structures accounting for layers of different chemical compositions and densities. With these realistic descriptions, we try to find which one best fits the information we have on a given planet and attempt to describe what would be the content of the planetary interior (Fortney et al. 2010a;Baraffe et al. 2014). As shown on Fig.1.3, the composition are difficult to disentangle given the errorbars we have on the estimates of the radius mostly and this highlights the importance of a more precise characterisation of the size of the exoplanets to infer their internal composition. This information can then be used to understand the formation and evolution processes depending on the nature of the planets (terrestrial, gaseous envelope) and their chemical interior compositions. Another observational technique is particularly suited to determine the planet composition of the atmosphere: the transit spectroscopy (Seager 2008). It consists in making spectroscopic observations of the companion while it is transiting to measure the planetary radius as a function of wavelength.

When crossing the planet atmosphere, the photons emitted by the star are absorbed by chemical components that are located up to a given altitude depending on the local atmospheric structure (density, pressure, temperature). As absorptions by atoms and molecules create well-known spectral features, a given element in the upper atmosphere will induce an increase of the apparent planet radius at specific wavelengths. The analysis of the variations in planet radius with respect to the wavelength can be combined with atmospheric models assuming equilibrium conditions (pressure-temperature profile), and allow the retrieval of the atmospheric structure of the exoplanets (Madhusudhan 2019). Being able to identify precisely the chemical elements in the atmosphere of an exoplanet can be used to infer the local composition of the disk in which it formed.

Calibrations and performances of the CHEOPS space mission for exoplanet science

Thesis

Reference

Calibrations and performances of the CHEOPS space mission for exoplanet science

Calibrations and Performances of the CHEOPS space mission

for exoplanet science

T

Adrien D

Résumé

Foreword

Remerciements

Contents

List of Figures

List of Tables

List of Acronyms

Chapter 1

Introduction

1.1 The extrasolar planets