TL;DR
This paper introduces an updated version of the pyaneti exoplanet modeling code with advanced multidimensional Gaussian process techniques for better analysis of stellar signals in radial velocity data, improving exoplanet detection and characterization.
Contribution
The new pyaneti version incorporates multidimensional Gaussian processes for modeling stellar signals, supports multi-band and single transit analysis, and enhances performance, with code publicly available.
Findings
Validated new routines with tests for exoplanet detection
Enhanced modeling of stellar signals in radial velocity data
Code improvements enable more accurate exoplanet characterization
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 multi-band 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…
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