Improving transit characterisation with Gaussian process modelling of stellar variability. (arXiv:2001.07975v1 [astro-ph.EP])
<a href="http://arxiv.org/find/astro-ph/1/au:+Barros_S/0/1/0/all/0/1">S. C. C. Barros</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Demangeon_O/0/1/0/all/0/1">O. Demangeon</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Diaz_R/0/1/0/all/0/1">R. F. Díaz</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Cabrera_J/0/1/0/all/0/1">J. Cabrera</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Santos_N/0/1/0/all/0/1">N. C. Santos</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Faria_J/0/1/0/all/0/1">J. P. Faria</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Pereira_F/0/1/0/all/0/1">F. Pereira</a>
New photometric space missions to detect and characterise transiting
exoplanets are focusing on bright stars to obtain high cadence, high
signal-to-noise light curves. Since these missions will be sensitive to stellar
oscillations and granulation even for dwarf stars, they will be limited by
stellar variability. We tested the performance of Gaussian process (GP)
regression on the characterisation of transiting planets, and in particular to
determine how many components of variability are necessary to describe high
cadence, high signal-to-noise light curves expected from CHEOPS and PLATO. We
found that the best GP stellar variability model contains four to five
variability components: one stellar oscillation component, two to four
granulation components, and/or one rotational modulation component. This high
number of components is in contrast with the one-component GP model (1GP)
commonly used in the literature for transit characterisation. Therefore, we
compared the performance of the best multi-component GP model with the 1GP
model in the derivation of transit parameters of simulated transits. We found
that for Jupiter- and Neptune-size planets the best multi-component GP model is
slightly better than the 1GP model, and much better than the non-GP model that
gives biased results. For Earth-size planets, the 1GP model fails to retrieve
the transit because it is a poor description of stellar activity. The non-GP
model gives some biased results and the best multi-component GP is capable of
retrieving the correct transit model parameters. We conclude that when
characterising transiting exoplanets with high signal-to-noise ratios and high
cadence light curves, we need models that couple the description of stellar
variability with the transits analysis, like GPs. Moreover, for Earth-like
exoplanets a better description of stellar variability improves the planetary
characterisation.
New photometric space missions to detect and characterise transiting
exoplanets are focusing on bright stars to obtain high cadence, high
signal-to-noise light curves. Since these missions will be sensitive to stellar
oscillations and granulation even for dwarf stars, they will be limited by
stellar variability. We tested the performance of Gaussian process (GP)
regression on the characterisation of transiting planets, and in particular to
determine how many components of variability are necessary to describe high
cadence, high signal-to-noise light curves expected from CHEOPS and PLATO. We
found that the best GP stellar variability model contains four to five
variability components: one stellar oscillation component, two to four
granulation components, and/or one rotational modulation component. This high
number of components is in contrast with the one-component GP model (1GP)
commonly used in the literature for transit characterisation. Therefore, we
compared the performance of the best multi-component GP model with the 1GP
model in the derivation of transit parameters of simulated transits. We found
that for Jupiter- and Neptune-size planets the best multi-component GP model is
slightly better than the 1GP model, and much better than the non-GP model that
gives biased results. For Earth-size planets, the 1GP model fails to retrieve
the transit because it is a poor description of stellar activity. The non-GP
model gives some biased results and the best multi-component GP is capable of
retrieving the correct transit model parameters. We conclude that when
characterising transiting exoplanets with high signal-to-noise ratios and high
cadence light curves, we need models that couple the description of stellar
variability with the transits analysis, like GPs. Moreover, for Earth-like
exoplanets a better description of stellar variability improves the planetary
characterisation.
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