Heyl, J;
Butterworth, J;
Viti, S;
(2023)
Understanding molecular abundances in star-forming regions using interpretable machine learning.
Monthly Notices of the Royal Astronomical Society
, 526
(1)
pp. 404-422.
10.1093/mnras/stad2814.
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Abstract
Astrochemical modelling of the interstellar medium typically makes use of complex computational codes with parameters whose values can be varied. It is not always clear what the exact nature of the relationship is between these input parameters and the output molecular abundances. In this work, a feature importance analysis is conducted using SHapley Additive exPlanations (SHAP), an interpretable machine learning technique, to identify the most important physical parameters as well as their relationship with each output. The outputs are the abundances of species and ratios of abundances. In order to reduce the time taken for this process, a neural network emulator is trained to model each species’ output abundance and this emulator is used to perform the interpretable machine learning. SHAP is then used to further explore the relationship between the physical features and the abundances for the various species and ratios we considered. H2O and CO’s gas phase abundances are found to strongly depend on the metallicity. NH3 has a strong temperature dependence, with there being two temperature regimes (<100 K and >100 K). By analysing the chemical network, we relate this to the chemical reactions in our network and find the increased temperature results in increased efficiency of destruction pathways. We investigate the HCN/HNC ratio and show that it can be used as a cosmic thermometer, agreeing with the literature. This ratio is also found to be correlated with the metallicity. The HCN/CS ratio serves as a density tracer, but also has three separate temperature-dependence regimes, which are linked to the chemistry of the two molecules.
| Type: | Article |
|---|---|
| Title: | Understanding molecular abundances in star-forming regions using interpretable machine learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/mnras/stad2814 |
| Publisher version: | https://doi.org/10.1093/mnras/stad2814 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Astrochemistry, methods: statistical, stars: abundances |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10179806 |
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