A fast neural emulator for interstellar chemistry

TL;DR

This paper introduces a neural network emulator for astrochemical models that drastically reduces computation time while maintaining high accuracy, enabling more complex and thorough exploration of interstellar chemistry.

Contribution

We developed a neural emulator for the Nautilus astrochemical code using conditional neural fields, significantly speeding up calculations with minimal uncertainty.

Findings

Emulator produces abundances for 192 species across 1-10^7 years.

Uncertainty below 0.2 dex for all species.

Computational speed is about 10^4 times faster than Nautilus.

Abstract

Astrochemical models are important tools to interpret observations of molecular and atomic species in different environments. However, these models are time-consuming, precluding a thorough exploration of the parameter space, leading to uncertainties and biased results. Using neural networks to simulate the behavior of astrochemical models is a way to circumvent this problem, providing fast calculations that are based on real astrochemical models. In this paper, we present a fast neural emulator of the astrochemical code Nautilus based on conditional neural fields. The resulting model produces the abundance of 192 species for arbitrary times between 1 and 10$^7$ years. Uncertainties well below 0.2 dex are found for all species, while the computing time is of the order of 10$^4$ smaller than Nautilus. This will open up the possibility of performing much more complex forward models to better understand the physical properties of the interstellar medium. As an example of the power of these models, we ran a feature importance analysis on the electron abundance predicted by Nautilus. We found that the electron density is coupled to the initial sulphur abundance in a low density gas. Increasing the initial sulphur abundance from a depleted scenario to the cosmic abundance leads to an enhancement of an order of magnitude of the electron density. This enhancement can potentially influence the dynamics of the gas in star formation sites.