# Incorporating Astrochemistry into Molecular Line Modelling via Emulation

## TL;DR

This paper presents a neural network-based emulator for astrochemical models to improve molecular line modeling in the interstellar medium, enabling faster and more realistic radiative transfer analyses.

## Contribution

A novel neural network emulator for the UCLCHEM astrochemical model that integrates into radiative transfer frameworks for efficient molecular line analysis.

## Key findings

- Emulator accurately replicates UCLCHEM outputs.
- Enables faster parameter estimation from molecular observations.
- Improves chemical realism in radiative transfer modeling.

## Abstract

In studies of the interstellar medium in galaxies, radiative transfer models of molecular emission are useful for relating molecular line observations back to the physical conditions of the gas they trace. However, doing this requires solving a highly degenerate inverse problem. In order to alleviate these degeneracies, the abundances derived from astrochemical models can be converted into column densities and fed into radiative transfer models. This enforces that the molecular gas composition used by the radiative transfer models be chemically realistic. However, because of the complexity and long running time of astrochemical models, it can be difficult to incorporate chemical models into the radiative transfer framework. In this paper, we introduce a statistical emulator of the UCLCHEM astrochemical model, built using neural networks. We then illustrate, through examples of parameter estimations, how such an emulator can be applied on real and synthetic observations.