Constraining chemical networks inAstrochemistry

TL;DR

This paper reviews the challenge of accurately constraining chemical networks in Astrochemistry, emphasizing the potential of statistical and machine learning methods to reduce uncertainties in reaction databases.

Contribution

It explores the application of statistical and machine learning techniques to improve the accuracy and completeness of chemical networks in Astrochemistry.

Findings

Identification of key uncertainties in chemical reaction databases.

Proposal of ML methods to systematically constrain chemical networks.

Potential for improved modeling of interstellar medium chemistry.

Abstract

Databases of gas and surface chemical reactions are a key tool for scientists working in a wide range of physical sciences. In Astrochemistry, databases of chemical reactions are used as inputs to chemical models to determine the abundances of the interstellar medium. Gas chemistry and, in particular, grain surface chemistry and its treatment in gas-grain chemical models are however areas of large uncertainty. Many reactions - especially on the dust grains - have not been systematically experimentally studied. Moreover, experimental measurements are often not easily translated to the rate equation approach most commonly used in astrochemical modelling. Reducing the degree of uncertainty intrinsic in these databases is therefore a prime problem, but has so far been approached mainly by ad hoc procedures of essentially trial and error. In this chapter we review the problem of the determination of accurate and complete chemical networks in the wider context of Astrochemistry and explore the possibility of using statistical methods and machine learning (ML) techniques to reduce the uncertainty in chemical networks.