A statistical and machine learning approach to the study of astrochemistry-Reference-Cited by-同舟云学术

A statistical and machine learning approach to the study of astrochemistry

Published:2023 Issue: Volume:245 Page:569-585
ISSN:1359-6640
Container-title:Faraday Discussions
language:en
Short-container-title:Faraday Discuss.

Author:

Heyl Johannes¹^ORCID,Viti Serena²¹^ORCID,Vermariën Gijs²^ORCID

Affiliation:

1. Department of Physics and Astronomy, University College London, Gower Street, WC1E 6BT, London, UK

2. Leiden Observatory, Leiden University, PO Box 9513, 2300 RA Leiden, The Netherlands

Abstract

We use Bayesian inference together with the MOPED compression algorithm to help determine which species should be prioritised for future detections in order to better constrain the values of binding energies in the ISM.

Funder

H2020 European Research Council

Science and Technology Facilities Council

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/FD/D3FD00008G

Reference46 articles.

1. A photodissociation region study of NGC 4038

2. Molecular line emission in NGC 1068 imaged with ALMA

3. Constraining cloud parameters using high density gas tracers in galaxies

4. The chemical footprint of AGN feedback in the outflowing circumnuclear disk of NGC 1068

5. UNDERSTANDING THE FORMATION AND EVOLUTION OF INTERSTELLAR ICES: A BAYESIAN APPROACH

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