Abstract
The radial-velocity (RV) method is one of the most successful in the detection of exoplanets, but is hindered by the intrinsic RV variations of the star, which can easily mimic or hide true planetary signals. kima is a package for the detection and characterization of exoplanets using RV data. It fits a sum of Keplerian curves to a timeseries of RV measurements and calculates the evidence for models with a fixed number Np of Keplerian signals, or after marginalising over Np. Moreover, kima can use a GP with a quasi-periodic kernel as a noise model, to deal with activity-induced signals. The hyperparameters of the GP are inferred together with the orbital parameters. The code is written in C++, but includes a helper Python package, pykima, which facilitates the analysis of the results.
Highlights
The radial-velocity (RV) method is one of the most successful in the detection of exoplanets
Gaussian processes (GP) are seen as a promising tool to model the correlated noise that arises from stellar-induced RV variations. (e.g. Haywood et al 2014)
Kima is a package for the detection and characterization of exoplanets using RV data. It fits a sum of Keplerian curves to a timeseries of RV measurements, using the Diffusive Nested Sampling algorithm (Brewer, Pártay, and Csányi 2011) to sample from the posterior distribution of the model parameters
Summary
The radial-velocity (RV) method is one of the most successful in the detection of exoplanets. An orbiting planet induces a gravitational pull on its host star, which is observed as a periodic variation of the velocity of the star in the direction of the line of sight. One of the main barriers to the detection of Earth-like planets with RVs is the intrinsic variations of the star, which can mimic or hide true RV signals of planets.
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