Issue |
A&A
Volume 666, October 2022
|
|
---|---|---|
Article Number | L8 | |
Number of page(s) | 10 | |
Section | Letters to the Editor | |
DOI | https://doi.org/10.1051/0004-6361/202244691 | |
Published online | 11 October 2022 |
Letter to the Editor
Machine learning-accelerated chemistry modeling of protoplanetary disks
1
Max Planck Institute for Astronomy, Königstuhl 17, 69117 Heidelberg, Germany
e-mail: smirnov@mpia.de
2
Institute of Astronomy, Russian Academy of Sciences, Pyatnitskaya str. 48, Moscow 119017, Russia
3
Department of Chemistry, Ludwig Maximilian University, Butenandtstr. 5-13, 81377 Munich, Germany
Received:
5
August
2022
Accepted:
22
September
2022
Aims. With the large amount of molecular emission data from (sub)millimeter observatories and incoming James Webb Space Telescope infrared spectroscopy, access to fast forward models of the chemical composition of protoplanetary disks is of paramount importance.
Methods. We used a thermo-chemical modeling code to generate a diverse population of protoplanetary disk models. We trained a K-nearest neighbors (KNN) regressor to instantly predict the chemistry of other disk models.
Results. We show that it is possible to accurately reproduce chemistry using just a small subset of physical conditions, thanks to correlations between the local physical conditions in adopted protoplanetary disk models. We discuss the uncertainties and limitations of this method.
Conclusions. The proposed method can be used for Bayesian fitting of the line emission data to retrieve disk properties from observations. We present a pipeline for reproducing the same approach on other disk chemical model sets.
Key words: astrochemistry / methods: numerical / protoplanetary disks / stars: pre-main sequence / ISM: molecules / submillimeter: planetary systems
© G. V. Smirnov-Pinchukov et al. 2022
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This article is published in open access under the Subscribe-to-Open model.
Open Access funding provided by Max Planck Society.
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