$\texttt{bayes_spec}$: A Bayesian Spectral Line Modeling Framework for Astrophysics
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929574056558592 |
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| author | Wenger, Trey V. |
| author_facet | Wenger, Trey V. |
| contents | $\texttt{bayes_spec}$ 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00924 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | $\texttt{bayes_spec}$: A Bayesian Spectral Line Modeling Framework for Astrophysics Wenger, Trey V. Instrumentation and Methods for Astrophysics Astrophysics of Galaxies $\texttt{bayes_spec}$ 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. |
| title | $\texttt{bayes_spec}$: A Bayesian Spectral Line Modeling Framework for Astrophysics |
| topic | Instrumentation and Methods for Astrophysics Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2411.00924 |