| Univ. NICE Prof. D. Mary | Exoplanet Detection (S2, elective, 3 ECTS) – offered through A.Y. 2023/2024 |
| Learning Outcomes: | The Course provides an overview of the main techniques currently used to detect exoplanets (namely, RV, photometric transits astrometry, timing, microlensing andimaging). The tests investigated in the theoretical part will be applied to real data such as HARPS for RV and to data simulated in the context of PLATO, an ESA satellite that will search for transits of rocky exoplanets (launch in 2024) or TESS already launched. The lectures also provides an intensive formation to Matlab |
| Knowledge and Understanding: | With 3432 exoplanets known to date (June 9, 2016), exoplanet detection is an extremely active field of research. Two methods, Radial Velocities (RV) and transits, have brought together more than 90% of these discoveries. This course focuses on these two methods. Students will learn in detail their principles, respective advantages and limitations. Like all exoplanet detection techniques, RV and transit detection algorithms rely on a model of the data. This model encapsulates information on the signature that one wishes to detect (e.g., sinusoids for RV, U-shaped signatures for transits)and the perturbations that affect the data (e.g., photon, detector or stel-lar noises,…). This perturbation always involves random phenomena((e.g., photon noise, read-out detector noises, spatial and temporal fluctuations of the stellar atmosphere). Hence, the data model on which the detection test is built is always asta-tisticalmodel, which involves parameters related both to noise and to the planetary signature. In this framework, current detection algorithms can advantageously be understood and analyzed in the general framework of statistical detection theory. Most transit detection algorithms fall in the category of Matched-Filter detection, and most RV detection methods are based on periodogram analyses. |
| Applying Knowledge and Understanding: | Detection theory provides Astronomers with an arsenal of systematic methods, of tools to analyze and quantify their performances,or of new concepts in multiple testing like the False Discovery Rate or the Higher Criticism. For this reason,the theoretical part of this lecture will heavily build on FC1.6 (Signal-image processing) and FC2.2 (Statistical methods). Students will investigate an arsenal of statistical tools than are routinely used in various other fields like climatology, genetics,econometrics or telecom (although no application to these field will be provided). So, even if RV and transit depend on physical parameters (like orbital and stellar parameters) that will indeed be studied, it should be clear that the heart of this theoretical part is truly statistical methods. |
| Prerequisites | Statistics & Probabilities |
| Program | I) Details of the different techniques used to detect exoplanets II) Development of the statistical approach and probabilistic detections III) Concrete examples, practices IV) Mathlab |
| Description of how the course is conducted | Powerpoints, white board, algorithm developments on Matlab |
| Description of the didactic methods | Both: theory and practice |
| Description of the evaluation methods | Oral |
| Adopted Textbooks | |
| Recommended readings | M. Perryman,The exoplanet handbook, Cambridge Univ. Press, 2011.T.H. Li ,Time series with mixed spec-tra, CRC Press, 2013.Exoplanets Ground instruments (RV) : HARPS(La Sila, Chile, 2004), NESPRESSO(VLT, Chile, 2016), CODEX (ELT,2025-2030).PLATO |
