$\texttt{bayes_spec}$: A Bayesian Spectral Line Modeling Framework for Astrophysics

Abstract

$\texttt{bayes_spec}$ is a Bayesian spectral line modeling framework for astrophysics. Given a user-defined model and a spectral line dataset, $\texttt{bayes_spec}$ enables inference of the model parameters through different numerical techniques, such as Monte Carlo Markov Chain (MCMC) methods, implemented in the PyMC probabilistic programming library. The API for $\texttt{bayes_spec}$ is designed to support astrophysical researchers who wish to ``fit'' arbitrary, user-defined models, such as simple spectral line profile models or complicated physical models that include a full physical treatment of radiative transfer. These models are ``cloud-based'', meaning that the spectral line data are decomposed into a series of discrete clouds with parameters defined by the user's model. Importantly, $\texttt{bayes_spec}$ provides algorithms to determine the optimal number of clouds for a given model and dataset.