RadVel: The Radial Velocity Modeling Toolkit

TL;DR

RadVel is an open-source Python toolkit that facilitates modeling and analyzing radial velocity data for exoplanet detection using Bayesian methods and MCMC sampling, with user-friendly interfaces and extensive visualization features.

Contribution

It introduces a flexible, extensible Python package for radial velocity modeling that integrates Bayesian inference, MCMC convergence diagnostics, and comprehensive visualization tools.

Findings

Provides robust confidence intervals via MCMC sampling.

Enables Bayesian model comparison for exoplanet detection.

Offers user-friendly command-line and Python interfaces.

Abstract

RadVel is an open source Python package for modeling Keplerian orbits in radial velocity (RV) time series. RadVel provides a convenient framework to fit RVs using maximum a posteriori optimization and to compute robust confidence intervals by sampling the posterior probability density via Markov Chain Monte Carlo (MCMC). RadVel allows users to float or fix parameters, impose priors, and perform Bayesian model comparison. We have implemented realtime MCMC convergence tests to ensure adequate sampling of the posterior. RadVel can output a number of publication-quality plots and tables. Users may interface with RadVel through a convenient command-line interface or directly from Python. The code is object-oriented and thus naturally extensible. We encourage contributions from the community. Documentation is available at http://radvel.readthedocs.io.