VROOM-SBI: A Fast Simulation-Based Bayesian Inference Methodology for QU-Fitting
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914606559002624 |
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| author | Pal, Arpan Jagannathan, Preshanth |
| author_facet | Pal, Arpan Jagannathan, Preshanth |
| contents | Bayesian QU-fitting is among the most accurate approaches for line-of-sight Faraday inference, but its per-pixel computational cost has made survey-scale application infeasible. QU-fitting is an alternative to Faraday synthesis with comparable accuracy in recovering line-of-sight Faraday components, but it has historically been computationally prohibitive at survey scale. Fitting to the Stokes spectra in $Q$ and $U$ through Bayesian inference is effective but slow. We introduce \texttt{VROOM-SBI}, which uses simulation-based inference, particularly neural posterior estimation, to speed up inference. Our results are comparable to both Faraday synthesis and QU-fitting, and deliver a speedup of $\sim$$500$ over classical QU-fitting implementations. We provide an open code repository and tools along with trained models via HuggingFace for the four standard depolarization models in common use, trained on VLA L-band frequency coverage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_27538 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VROOM-SBI: A Fast Simulation-Based Bayesian Inference Methodology for QU-Fitting Pal, Arpan Jagannathan, Preshanth Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Bayesian QU-fitting is among the most accurate approaches for line-of-sight Faraday inference, but its per-pixel computational cost has made survey-scale application infeasible. QU-fitting is an alternative to Faraday synthesis with comparable accuracy in recovering line-of-sight Faraday components, but it has historically been computationally prohibitive at survey scale. Fitting to the Stokes spectra in $Q$ and $U$ through Bayesian inference is effective but slow. We introduce \texttt{VROOM-SBI}, which uses simulation-based inference, particularly neural posterior estimation, to speed up inference. Our results are comparable to both Faraday synthesis and QU-fitting, and deliver a speedup of $\sim$$500$ over classical QU-fitting implementations. We provide an open code repository and tools along with trained models via HuggingFace for the four standard depolarization models in common use, trained on VLA L-band frequency coverage. |
| title | VROOM-SBI: A Fast Simulation-Based Bayesian Inference Methodology for QU-Fitting |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2605.27538 |