VROOM-SBI: A Fast Simulation-Based Bayesian Inference Methodology for Stokes QU-Fitting in Radio Interferometry
Abstract
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 ∼$500$ 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.
Related
- Solar spicule population properties inferred from IRIS Mg II k off-limb spectra using likelihood-free Bayesian inference
- Fast Bayesian Inference for Long-Duration Gravitational-Wave Signals in 3G detectors
- A space of inference spaces in the space sciences - parametric Bayesian inference in astronomy, cosmology and particle physics
- MUSHY: Multimodal Flow-Based Amortized Bayesian Inference for Spectroscopic Data Fusion
- Axio-Dilaton Dark Energy: A Dynamical Systems and Bayesian Inference Analysis
BibTeX
@misc{pal2026vroomsbi,
title = {{VROOM-SBI: A Fast Simulation-Based Bayesian Inference Methodology for Stokes QU-Fitting in Radio Interferometry}},
author = {Arpan Pal and Preshanth Jagannathan},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2605.27538},
eprint = {2605.27538},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.27538},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - VROOM-SBI: A Fast Simulation-Based Bayesian Inference Methodology for Stokes QU-Fitting in Radio Interferometry
AU - Arpan Pal
AU - Preshanth Jagannathan
PY - 2026
DA - 2026/09/04
PB - arXiv
DO - 10.48550/arxiv.2605.27538
UR - https://arxiv.org/abs/2605.27538
AB - 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 ∼$500$ 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.
ER -