Heyl, Johannes;
Sellentin, Elena;
Holdship, Jonathan;
Viti, Serena;
(2022)
Identifying the most constraining ice observations to infer molecular binding energies.
Monthly Notices of the Royal Astronomical Society
, 517
(1)
pp. 38-46.
10.1093/mnras/stac2652.
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Abstract
In order to understand grain-surface chemistry, one must have a good understanding of the reaction rate parameters. For diffusion-based reactions, these parameters are binding energies of the reacting species. However, attempts to estimate these values from grain-surface abundances using Bayesian inference are inhibited by a lack of enough sufficiently constraining data. In this work, we use the Massive Optimised Parameter Estimation and Data compression algorithm to determine which species should be prioritized for future ice observations to better constrain molecular binding energies. Using the results from this algorithm, we make recommendations for which species future observations should focus on.
| Type: | Article |
|---|---|
| Title: | Identifying the most constraining ice observations to infer molecular binding energies |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/mnras/stac2652 |
| Publisher version: | https://doi.org/10.1093/mnras/stac2652 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | astrochemistry, methods: data analysis, methods: statistical, ISM: abundances |
| UCL classification: | 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 UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10157709 |
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