2021/09/30 by Oscar Barragán, S. Aigrain, Suzanne Aigrain +4 · 98 citations
Neuroscience · Physics and Astronomy · #Astrophysics #Computer science #Gaussian #Gaussian process #Photoreceptor and optogenetics research #Physics #Process (computing) #Quantum mechanics #Series (stratigraphy) #Spectroscopy and Quantum Chemical Studies #Statistical physics #Stellar, planetary, and galactic studies #astro-ph.EP #astro-ph.IM
paper · pdf · doi:10.1093/mnras/stab2889
published in Monthly Notices of the Royal Astronomical Society 509(1), 866-883 (Oxford University Press) · This version looks closer to the published one: MNRAS, 509, 866
openalex publication_date 2021/10/06 · openalex created_date 2021/10/11 · arxiv created 2021/11/22 · arxiv updated 2021/11/23 · openalex updated_date 2026/08/05
ABSTRACT The two most successful methods for exoplanet detection rely on the detection of planetary signals in photometric and radial velocity time-series. This depends on numerical techniques that exploit the synergy between data and theory to estimate planetary, orbital, and/or stellar parameters. In this work, we present a new version of the exoplanet modelling code pyaneti. This new release has a special emphasis on the modelling of stellar signals in radial velocity time-series. The code has a built-in multidimensional Gaussian process approach to modelling radial velocity and activity indicator time-series with different underlying covariance functions. This new version of the code also allows multiband and single transit modelling; it runs on Python 3, and features overall improvements in performance. We describe the new implementation and provide tests to validate the new routines that have direct application to exoplanet detection and characterization. We have made the code public and freely available at https://github.com/oscaribv/pyaneti. We also present the codes citlalicue and citlalatonac that allow one to create synthetic photometric and spectroscopic time-series, respectively, with planetary and stellar-like signals.