Caractériser les systèmes planétaires de naines rouges actives avec SPIRou

Student : KLEIN Baptiste

Advisor : DONATI Jean-François

Start : Novembre 2017

Group : PS2E

The successful candidate will work with JF Donati (CNRS/IRAP/OMP) on detecting and characterizing planetary systems of nearby M dwarfs by modeling velocimetric data collected within the SPIRou Legacy Survey.

SPIRou is a new-generation near-infrared spectropolarimeter / velocimeter to be installed at the Canada-France-Hawaii Telescope (CFHT) in late 2017. Science wise, SPIRou will focus on two major science topics, (i) the quest for Earth-like planets in the habitable zones (HZs) of very-low-mass stars, and (ii) the study of low-mass star & planet formation in the presence of magnetic fields. SPIRou is ideally phased with other forefront exoplanet space missions like TESS & JWST, aimed at detecting Earth-like exoplanets transiting their hosts, and at looking for the potential presence of biomarkers in their atmospheres.Paragraphe

Since the discovery of 51 Peg b, 3500+ exoplanets and 600+ exoplanetary systems have been detected; however, only a handful of HZ Earth-size planets were found, and most of them are too far for scrutinizing their atmospheres. Detecting Earth twins is still quite tricky given the low-amplitude radial-velocity (RV) fluctuations (or sub-mmag photometric transits) such planets induce in the spectrum of their host stars. M dwarfs are key targets in this respect; beyond dominating the stellar population of the solar neighborhood, they are known to host multiple planets (Gaidos et al 2016) and to feature compact HZs, making their HZ low-mass planets easier to detect than for Sun-like stars.

M dwarfs are however notorious for their magnetic activity, generating spurious RV signals (activity jitter) hampering planet detectability (Newton et al 2016). Modeling the activity of M dwarfs and the underlying magnetic fields is thus crucial for filtering out the RV jitter and for maximizing the detection efficiency of low-mass planets and planet systems (Hébrard et al 2016). Fields of low-mass stars can also have an impact on the orbital evolution of close-in planets and on their habitability, making high-precision RV and spectropolarimetric data of M dwarfs such as those SPIRou will provide quite precious for this quest.

This PhD will focus on studying and modeling the jitter that activity induces in the RV curves of the moderately-active M dwarfs to be monitored with SPIRou as part of the SPIRou Legacy Survey. The goal is to devise and test novel methods using Bayesian inference to accurately filter the jitter and facilitate the detection of low-mass planets and multiple-planet systems, further improving on techniques such as Gaussian Process Regression (GPR, Haywood et al 2014) and Compressed Sensing (CS, Hara et al 2016). The spectropolarimetric content of SPIRou data will also make it possible to investigate how filtering can be enhanced by modeling the large-scale magnetic fields of the host stars along with their surface features using Zeeman-Doppler imaging (ZDI, Hébrard et al 2016).

During first year, modeling tools based on existing methods will be extensively tested on simulated SPIRou-like velocimetric / spectropolarimetric data sets (occasionally augmented with precision photometry) of moderately-active M dwarfs orbited by multiplanet systems, and enhanced so as to achieve the best possible detection rates. During years two and three, and as SPIRou data from the SPIRou Legacy Survey start to flow in (along with TESS photometry whenever available), the optimized modeling tools derived in year one will be exploited to characterize the magnetic fields and activity of SPIRou targets, and to filter out the jitter from their RV curves in order to achieve the most reliable characterization of their planetary systems in the context of Bayesian inference. This work is expected to yield at least three first-author publication throughout the full PhD period.

The successful candidate will need to have a Master degree in Astrophysics, to be well versed in stellar and planetary physics (formation & evolution) as well as in astrophysical spectroscopy, and to have demonstrated skills in scientific programming (C, Python).