Unmasking the physical information inherent to interstellar spectral line profiles with machine learning - I. Application of LTE to HCN and HNC transitions

Aug 5, 2026

Physical and chemical properties such as kinetic temperature, volume density, and molecular composition are encoded in submillimeter spectral line profiles of interstellar clouds. A bottom-up machine learning strategy is introduced to extract these physical conditions directly from line profiles without radiative transfer equations. Simulations target dense molecular clouds and star-forming regions using HCN and HNC isomers over multiple rotational transitions and frequencies, parameterizing a data distribution from line intensities and widths. ML models are trained, tested, and compared to infer excitation conditions and the HNC/HCN abundance ratio, with results consistent with LTE analysis for the cold source R CrA IRS 7B.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Abstract

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University of Groningen Unmasking the physical information inherent to interstellar spectral line profiles with machine learning Mendoza, Edgar; Dallaolio, Pietro; Coelho, Luciene S. ; Peregrín, Antonio; López-Domínguez, Samuel; Van Der Tak, Floris F.S. ; Carvajal, Miguel Published in: Astronomy & Astrophysics DOI: 10.1051/0004-6361/202452397 IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2025 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Mendoza, E. , Dallaolio, P. , Coelho, L. S. , Peregrín, A. , López-Domínguez, S. , Van Der Tak, F. F. S. , & Carvajal, M. (2025) . Unmasking the physical information inherent to interstellar spectral line profiles with machine learning: I. Application of LTE to HCN and HNC transitions. Astronomy & Astrophysics , 698, Article A286 . [https://doi.org/10.1051/0004-6361/202452397](https://doi.org/10.1051/0004-6361/202452397) Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) . The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment. Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.) Download date: 01-01-2026 Aht©&tpT:,/698doAuith,oA2rg/ors861020.12(2005152/500) 04-6361/202452397 &~~t~~ronomy~~t~~rop~~h~~ys~~i~~cs Unmasking the physical information inherent to interstellar spectral line profiles with machine learning I. Application of LTE to HCN and HNC transitions Edgar Mendoza 1 , ⋆, Pietro Dall’Olio 1 , 2 , Luciene S. Coelho3, Antonio Peregrín4 , 5, Samuel López-Domínguez6 , Floris F. S. van der Tak7, and Miguel Carvajal 1 , 8 , ⋆ 1 Dept. Ciencias Integradas, Facultad de Ciencias Experimentales, Centro de Estudios Avanzados en Física, Matemática y Computación, Unidad Asociada GIFMAN, CSIC-UHU, Universidad de Huelva, Spain 2 Lyon College, Batesville, AR, USA 3 Planetário Juan Bernardino Marques Barrio, Instituto de Estudos Socioambientais, Universidade Federal de Goiás, Brazil 4 Centro de Estudios Avanzados en Física, Matemática y Computación, Universidad de Huelva, Spain 5 Andalusian Research Institute in Data Science and Computational Intelligence, Universidad de Huelva, Spain 6 Dept. Tecnologías de la Información, Escuela Técnica Superior de Ingeniería, Centro de Estudios Avanzados en Física, Matemáticay Computación, Universidad de Huelva, Spain 7 SRON Netherlands Institute for Space Research & Kapteyn Astronomical Institute, University of Groningen, 9747 AD Groningen, The Netherlands 8 Instituto Universitario Carlos I de Física teórica y computacional, Universidad de Granada, Spain Received 27 September 2024 / Accepted 25 April 2025 ABSTRACT Context. Physical and chemical properties, such as kinetic temperature, volume density, and m

FAQs

What physical conditions can be inferred from interstellar spectral line profiles?

Kinetic temperature, volume density, and molecular composition can be extracted because these properties are encoded in the line spectra at submillimeter wavelengths.

How does the proposed machine learning approach differ from traditional radiative transfer methods?

It performs a bottom-up extraction of physical conditions from observed line profiles directly, without using radiative transfer equations.

What do the results show when ML estimates are compared with LTE analysis?

For the cold source R CrA IRS 7B, the excitation temperature and abundance ratio inferred from ML using two lines agree with LTE analysis.

Aug 5, 2026

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