Beetroots: spatially-regularized Bayesian inference of physical parameter maps -- Application to Orion
24 pages, 9 figures,1 table
| Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2025 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositório: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/396943 |
| Acesso em linha: | http://hdl.handle.net/10261/396943 http://arxiv.org/abs/2504.08387v1 |
| Access Level: | Acceso aberto |
| Palavra-chave: | ISM: clouds ISM: lines and bands Methods: data analysis Methods: numerical Methods: statistical Photon-dominated region (PDR) |
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Beetroots: spatially-regularized Bayesian inference of physical parameter maps -- Application to Orion |
| title |
Beetroots: spatially-regularized Bayesian inference of physical parameter maps -- Application to Orion |
| spellingShingle |
Beetroots: spatially-regularized Bayesian inference of physical parameter maps -- Application to Orion Palud, Pierre ISM: clouds ISM: lines and bands Methods: data analysis Methods: numerical Methods: statistical Photon-dominated region (PDR) |
| title_short |
Beetroots: spatially-regularized Bayesian inference of physical parameter maps -- Application to Orion |
| title_full |
Beetroots: spatially-regularized Bayesian inference of physical parameter maps -- Application to Orion |
| title_fullStr |
Beetroots: spatially-regularized Bayesian inference of physical parameter maps -- Application to Orion |
| title_full_unstemmed |
Beetroots: spatially-regularized Bayesian inference of physical parameter maps -- Application to Orion |
| title_sort |
Beetroots: spatially-regularized Bayesian inference of physical parameter maps -- Application to Orion |
| dc.creator.none.fl_str_mv |
Palud, Pierre Bron, Emeric Chainais, Pierre Le Petit, Franck Thouvenin, Pierre-Antoine Santa-María, Miriam G. Goicoechea, Javier R. Languignon, David Gerin, Maryvonne Pety, Jérôme Bešlić, Ivana Coudé, Simon Einig, Lucas Mazurek, Helena Orkisz, Jan H. Ségal, Léontine Zakardjian, Antoine Bardeau, Sébastien Demyk, Karine Souza Magalhães, Victor de Gratier, Pierre Guzmán, Viviana V. Hughes, Annie Levrier, François Le Bourlot, Jacques Lis, Dariusz C. Liszt, Harvey S. Peretto, Nicolas Roueff, Antoine Roueff, Evelyne Sievers, Albrecht |
| author |
Palud, Pierre |
| author_facet |
Palud, Pierre Bron, Emeric Chainais, Pierre Le Petit, Franck Thouvenin, Pierre-Antoine Santa-María, Miriam G. Goicoechea, Javier R. Languignon, David Gerin, Maryvonne Pety, Jérôme Bešlić, Ivana Coudé, Simon Einig, Lucas Mazurek, Helena Orkisz, Jan H. Ségal, Léontine Zakardjian, Antoine Bardeau, Sébastien Demyk, Karine Souza Magalhães, Victor de Gratier, Pierre Guzmán, Viviana V. Hughes, Annie Levrier, François Le Bourlot, Jacques Lis, Dariusz C. Liszt, Harvey S. Peretto, Nicolas Roueff, Antoine Roueff, Evelyne Sievers, Albrecht |
| author_role |
author |
| author2 |
Bron, Emeric Chainais, Pierre Le Petit, Franck Thouvenin, Pierre-Antoine Santa-María, Miriam G. Goicoechea, Javier R. Languignon, David Gerin, Maryvonne Pety, Jérôme Bešlić, Ivana Coudé, Simon Einig, Lucas Mazurek, Helena Orkisz, Jan H. Ségal, Léontine Zakardjian, Antoine Bardeau, Sébastien Demyk, Karine Souza Magalhães, Victor de Gratier, Pierre Guzmán, Viviana V. Hughes, Annie Levrier, François Le Bourlot, Jacques Lis, Dariusz C. Liszt, Harvey S. Peretto, Nicolas Roueff, Antoine Roueff, Evelyne Sievers, Albrecht |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.contributor.none.fl_str_mv |
Agence Nationale de la Recherche (France) Centre National de la Recherche Scientifique (France) Centre National D'Etudes Spatiales (France) Ministerio de Ciencia e Innovación (España) National Science Foundation (US) NASA Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72] |
| dc.subject.none.fl_str_mv |
ISM: clouds ISM: lines and bands Methods: data analysis Methods: numerical Methods: statistical Photon-dominated region (PDR) |
| topic |
ISM: clouds ISM: lines and bands Methods: data analysis Methods: numerical Methods: statistical Photon-dominated region (PDR) |
| description |
24 pages, 9 figures,1 table |
| publishDate |
2025 |
| dc.date.none.fl_str_mv |
2025 2025 2025 |
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info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 Publisher's version info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
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http://hdl.handle.net/10261/396943 http://arxiv.org/abs/2504.08387v1 |
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http://hdl.handle.net/10261/396943 http://arxiv.org/abs/2504.08387v1 |
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Inglés |
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Inglés |
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info:eu-repo/semantics/openAccess |
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openAccess |
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EDP Sciences |
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EDP Sciences |
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reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC instname:Consejo Superior de Investigaciones Científicas (CSIC) |
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Beetroots: spatially-regularized Bayesian inference of physical parameter maps -- Application to OrionPalud, PierreBron, EmericChainais, PierreLe Petit, FranckThouvenin, Pierre-AntoineSanta-María, Miriam G.Goicoechea, Javier R.Languignon, DavidGerin, MaryvonnePety, JérômeBešlić, IvanaCoudé, SimonEinig, LucasMazurek, HelenaOrkisz, Jan H.Ségal, LéontineZakardjian, AntoineBardeau, SébastienDemyk, KarineSouza Magalhães, Victor deGratier, PierreGuzmán, Viviana V.Hughes, AnnieLevrier, FrançoisLe Bourlot, JacquesLis, Dariusz C.Liszt, Harvey S.Peretto, NicolasRoueff, AntoineRoueff, EvelyneSievers, AlbrechtISM: cloudsISM: lines and bandsMethods: data analysisMethods: numericalMethods: statisticalPhoton-dominated region (PDR)24 pages, 9 figures,1 tableThe current generation of millimeter receivers is able to produce cubes of 800 000 pixels by 200 000 frequency channels to cover several square degrees over the 3 mm atmospheric window. Estimating the physical conditions of the interstellar medium (ISM) with an astrophysical model on such datasets is challenging. Common approaches tend to converge to local minima and typically poorly reconstruct regions with low signal-to-noise ratio (S/N). This instrumental revolution thus calls for new scalable data analysis techniques. We present Beetroots, a Python software that performs Bayesian reconstruction of maps of physical conditions from 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 also assesses the ability of the astrophysical model to explain the observations, providing feedback to improve ISM models. We demonstrate the power of Beetroots with the Meudon PDR code on synthetic data, and 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. The application to the synthetic case shows that Beetroots can currently analyse maps with up to ten thousand pixels, addressing large variations of S/N, escaping from local minima, and providing consistent uncertainty quantifications. On a laptop, the inference runtime ranges from a few minutes for 100-pixel maps to 28 hours for 8100-pixel maps. The results on the OMC-1 maps are consistent with independent estimations from the literature, and improve our understanding of the region. This work paves the way towards systematic and rigorous analyses of observations produced by current and future instruments.This work received support from the French Agence Nationale de la Recherche through the DAOISM grant ANR-21-CE31-0010, and from the Programme National “Physique et Chimie du Milieu Interstellaire” (PCMI) of CNRS/INSU with INC/INP, co-funded by CEA and CNES. It also received support through the ANR grant “MIAI @ Grenoble Alpes” ANR-19P3IA-0003. This work was partly supported by the CNRS through 80Prime project OrionStat, a MITI interdisciplinary program, by the ANR project “Chaire IA Sherlock” ANR-20-CHIA-0031-01 held by P. Chainais, and by the national support within the programme d’investissements d’avenir ANR-16-IDEX-0004 ULNE and Région HDF. JRG and MGSM thank the Spanish MCINN for funding support under grants PID2019-106110G-100 and PID2023-146667NB-I00. MSGM acknowledges support from the NSF under grant CAREER 2142300. Part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). D.C.L. acknowledges financial support from the National Aeronautics and Space Administration (NASA) Astrophysics Data Analysis Program (ADAP).Peer reviewedEDP SciencesAgence Nationale de la Recherche (France)Centre National de la Recherche Scientifique (France)Centre National D'Etudes Spatiales (France)Ministerio de Ciencia e Innovación (España)National Science Foundation (US)NASAConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/396943http://arxiv.org/abs/2504.08387v1reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-106110GB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-146667NB-I00https://doi.org/10.1051/0004-6361/202554266Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3969432026-05-22T06:33:51Z |
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