Eryn : A multi-purpose sampler for Bayesian inference

Abstract

In recent years, methods for Bayesian inference have been widely used in many different problems in physics where detection and characterization are necessary. Data analysis in gravitational-wave astronomy is a prime example of such a case. Bayesian inference has been very successful because this technique provides a representation of the parameters as a posterior probability distribution, with uncertainties informed by the precision of the experimental measurements. During the last couple of decades, many specific advances have been proposed and employed in order to solve a large variety of different problems. In this work, we present a Markov Chain Monte Carlo (MCMC) algorithm that integrates many of those concepts into a single MCMC package. For this purpose, we have built {\tt Eryn}, a user-friendly and multipurpose toolbox for Bayesian inference, which can be utilized for solving parameter estimation and model selection problems, ranging from simple inference questions, to those with large-scale model variation requiring trans-dimensional MCMC methods, like the LISA global fit problem. In this paper, we describe this sampler package and illustrate its capabilities on a variety of use cases.

21 pages, 7 figures

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• Systematics in tests of general relativity using LISA massive black hole binaries
• Probing fundamental physics with Extreme Mass Ratio Inspirals: a full Bayesian inference for scalar charge
• Signatures of dark and baryonic structures on weakly lensed gravitational waves
• The implications of stochastic gas torques for asymmetric binaries in the LISA band
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• Comparing astrophysical models to gravitational-wave data in the observable space
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• Source Confusion of Massive Black Hole Binaries for the Taiji Mission
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