Bahamas: BAyesian inference with HAmiltonian Montecarlo for Astrophysical Stochastic background
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915363665477632 |
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| author | Pozzoli, Federico Buscicchio, Riccardo Klein, Antoine Chirico, Daniele |
| author_facet | Pozzoli, Federico Buscicchio, Riccardo Klein, Antoine Chirico, Daniele |
| contents | The LISA datastream will be populated by large instrumental and astrophysical noises, both potentially exhibiting long-term non-stationarities. Modelling and inferring on them is a challenging task, central for accurate signal reconstruction. In this paper, we introduce $\texttt{bahamas}$, a codebase designed to characterize noises and stochastic gravitational wave backgrounds (SGWBs) in LISA. $\texttt{bahamas}$ adopts a time-frequency data representation, based on the Short Time Fourier Transform, to accurately describe the signal temporal evolution and accommodate for the presence of data gaps. In addition, $\texttt{bahamas}$ supports a variety of SGWB spectral models proposed in literature, enabling joint inference on them. Posterior sampling leverages No-U-Turn sampling an efficient variant of Hamiltonian Monte Carlo, inheriting the cross-hardware capabilities provided by NumPyro (CPU/GPU/TPU). We benchmark $\texttt{bahamas}$ performances on a simple test case, and present ongoing developments to appear in future releases. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_22542 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bahamas: BAyesian inference with HAmiltonian Montecarlo for Astrophysical Stochastic background Pozzoli, Federico Buscicchio, Riccardo Klein, Antoine Chirico, Daniele Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena The LISA datastream will be populated by large instrumental and astrophysical noises, both potentially exhibiting long-term non-stationarities. Modelling and inferring on them is a challenging task, central for accurate signal reconstruction. In this paper, we introduce $\texttt{bahamas}$, a codebase designed to characterize noises and stochastic gravitational wave backgrounds (SGWBs) in LISA. $\texttt{bahamas}$ adopts a time-frequency data representation, based on the Short Time Fourier Transform, to accurately describe the signal temporal evolution and accommodate for the presence of data gaps. In addition, $\texttt{bahamas}$ supports a variety of SGWB spectral models proposed in literature, enabling joint inference on them. Posterior sampling leverages No-U-Turn sampling an efficient variant of Hamiltonian Monte Carlo, inheriting the cross-hardware capabilities provided by NumPyro (CPU/GPU/TPU). We benchmark $\texttt{bahamas}$ performances on a simple test case, and present ongoing developments to appear in future releases. |
| title | Bahamas: BAyesian inference with HAmiltonian Montecarlo for Astrophysical Stochastic background |
| topic | Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2506.22542 |