Chemulator - Fast, accurate thermochemistry for dynamical models

Authors: Holdship, Jonathan; Viti, Serena; Haworth, Thomas; Ilee, John
Arxiv: https://arxiv.org/abs/2106.14789
DOI: https://doi.org/10.1051/0004-6361/202140357
GitHub: https://github.com/uclchem/Chemulator

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. To counter this, we aimed 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.

To achieve this, 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. 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 0.003 on the log-scaled temperature after one time step and 0.006 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.