Holdship, J, Viti, S, Haworth, TJ et al. (1 more author) (2021) Chemulator: Fast, accurate thermochemistry for dynamical models through emulation. Astronomy and Astrophysics, 653. A76. ISSN 0365-0138
Abstract
Context. Chemical modelling serves two purposes in dynamical models: accounting for the effect of microphysics on the dynamics and providing observable signatures. Ideally, the former must be done as part of the hydrodynamic simulation but this comes with a prohibitive computational cost that leads to many simplifications being used in practice.
Aims. We aim to produce a statistical emulator that replicates a full chemical model capable of solving the temperature and abundances of a gas through time. This emulator should suffer only a minor loss of accuracy when compared to a full chemical solver and would have a fraction of the computational cost allowing it to be included in a dynamical model.
Methods. The gas-grain chemical code UCLCHEM was updated to include heating and cooling processes, and a large dataset of model outputs from possible starting conditions was produced. A neural network was then trained to map directly from inputs to outputs.
Results. Chemulator replicates the outputs of UCLCHEM with an overall mean squared error (MSE) of 1.7 × 10−4 for a single time step of 1000 yr, and it is shown to be stable over 1000 iterations with an MSE of 3 × 10−3 on the log-scaled temperature after one timzze step and 6 × 10−3 after 1000 time steps. Chemulator was found to be approximately 50 000 times faster than the time-dependent model it emulates but can introduce a significant error to some models.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | astrochemistry; methods: numerical; methods: statistical; hydrodynamics |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Physics and Astronomy (Leeds) > Astrophysics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 28 Jun 2022 13:56 |
Last Modified: | 28 Jun 2022 13:56 |
Status: | Published |
Publisher: | EDP Sciences |
Identification Number: | 10.1051/0004-6361/202140357 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:188151 |