Gaussian process modelling of granulation and oscillations in red giant stars

Journal
Date Issued
2019
Author(s)
Pereira, Filipe
•
Campante, Tiago L.
•
Cunha, Margarida S.
•
Faria, João P.
•
Santos, Nuno C.
•
Barros, Susana C. C.
•
Demangeon, Olivier
•
Kuszlewicz, James S.
•
DOI
Abstract
The analysis of photometric time series in the context of transiting planet surveys suffers from the presence of stellar signals, often dubbed `stellar noise'. These signals, caused by stellar oscillations and granulation, can usually be disregarded for main-sequence stars, as the stellar contributions average out when phase-folding the light curve. For evolved stars, however, the amplitudes of such signals are larger and the timescales similar to the transit duration of short-period planets, requiring that they be modelled alongside the transit. With the promise of TESS delivering of the order of ∼105 light curves for stars along the red giant branch, there is a need for a method capable of describing the `stellar noise' while simultaneously modelling an exoplanet's transit. In this work, a Gaussian process regression framework is used to model stellar light curves and the method validated by applying it to TESS-like artificial data. Furthermore, the method is used to characterize the stellar oscillations and granulation of a sample of well-studied Kepler low-luminosity red giant branch stars. The parameters determined are compared to equivalent ones obtained by modelling the power spectrum of the light curve. Results show that the method presented is capable of describing the stellar signals in the time domain and can also return an accurate and precise measurement of νmax, i.e. the frequency of maximum oscillation amplitude. Preliminary results show that using the method in transit modelling improves the precision and accuracy of the ratio between the planetary and stellar radius, Rp/R⋆. The method's implementation is publicly available.1
Funding(s)
Volume
489
Issue
4
Start page
5764
Uri
Url
Issn Identifier
0035-8711
Ads BibCode
2019MNRAS.489.5764P
Rights
open.access
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