Palud, Pierre, Bron, Emeric, Chainais, Pierre, Le Petit, Franck, Thouvenin, Pierre-Antoine, Santa-Maria, Miriam G., Goicoechea, Javier R., Languignon, David, Gerin, Maryvonne, Pety, Jérôme, Beslic, Ivana, Coudé, Simon, Einig, Lucas, Mazurek, Helena, Orkisz, Jan H., Ségal, Léontine, Zakardjian, Antoine, Bardeau, Sébastien, Demyk, Karine, de Souza Magalhães, Victor, Gratier, Pierre, Guzmán, Viviana V., Hughes, Annie, Levrier, François, Le Bourlot, Jacques, Lis, Dariusz C., Liszt, Harvey S., Peretto, Nicolas  ORCID: https://orcid.org/0000-0002-6893-602X, Roueff, Antoine, Roueff, Evelyne and Sievers, Albrecht
2025.
BEETROOTS: Spatially regularized Bayesian inference of physical parameter maps. Application to Orion.
Astronomy and Astrophysics
698
, A311.
10.1051/0004-6361/202554266
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Abstract
Context. The current generation of millimeter (mm) receivers is capable of producing cubes of 800 000 pixels over 200 000 frequency channels to cover a number of square degrees over the 3 mm atmospheric window. Estimating the physical conditions of the interstellar medium (ISM) with an astrophysical model on the basis of such large datasets is challenging. Common approaches tend to converge to local minima and end up poorly reconstructing regions with a low signal-to-noise ratio (S/N) in most cases. This instrumental revolution thus calls for new scalable data analysis techniques with more advanced approaches to statistical modeling and methods. Aims. Our aim is to design a general method to reconstruct large maps of physical conditions from the rich datasets produced by new and future instruments. The requirements of the method include the ability to scale to very large maps, to be robust to varying S/N, and to escape from the local minima. In addition, we want to quantify the uncertainties associated with our reconstructions to produce reliable analyses. Methods. We present BEETROOTS, a PYTHON software that performs Bayesian reconstructions of maps of physical conditions based on observation maps and an astrophysical model. It relies on an accurate statistical model, exploits spatial regularization to guide estimations, and uses state-of-the-art algorithms. It can also assess the ability of the astrophysical model to explain the observations, providing feedback to improve ISM models. In this work, we demonstrate the power of BEETROOTS with the Meudon PDR code on synthetic data. We then apply it to estimate physical condition maps in the full Orion molecular cloud 1 (OMC-1) star-forming region based on Herschel molecular line emission maps. Results. The application to the synthetic case shows that BEETROOTS can currently analyze maps with up to ten thousand pixels, addressing large variations among the S/N values within the observations while escaping from local minima and providing consistent uncertainty quantifications. On a personal laptop, the inference runtime ranges from a few minutes for maps of 100 pixels to 28 hours for maps of 8100 pixels. Regarding OMC-1, our reconstructions of the incident UV radiation field intensity, G0, are consistent with those obtained from FIR luminosities. This demonstrates that the considered molecular tracers are able to constrain G0 over a wide range of environments. In addition, the obtained thermal pressures are high in all dense regions of OMC-1 and positively correlated with G0. Finally, the Meudon PDR code successfully explains the observations and the obtained G0 values are reasonable, which indicates that UV photons control the gas physics and chemistry across the rim of OMC-1. Conclusions. This work paves the way toward systematic and rigorous analyses of observations produced by current and future instruments. Subsequent efforts still need to be made in parallelizing the algorithm and thereby gaining two orders of magnitude for the map sizes.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Physics and Astronomy |
| Publisher: | EDP Sciences |
| ISSN: | 0004-6361 |
| Date of First Compliant Deposit: | 12 December 2025 |
| Last Modified: | 12 Dec 2025 15:18 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/183191 |
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