Identifying the most constraining ice observations to infer molecular binding energies

TL;DR

This paper uses a data compression algorithm to identify key ice species for future observations, aiming to improve the estimation of molecular binding energies critical for grain-surface chemistry models.

Contribution

It introduces a method to prioritize ice species for observations, enhancing the accuracy of binding energy estimates through constrained data collection.

Findings

Identifies specific ice species that most constrain binding energy estimates.

Provides recommendations for future observational focus to improve chemical models.

Demonstrates the effectiveness of MOPED in astrophysical data prioritization.

Abstract

In order to understand grain-surface chemistry, one must have a good understanding of the reaction rate parameters. For diffusion-based reactions, these parameters are binding energies of the reacting species. However, attempts to estimate these values from grain-surface abundances using Bayesian inference are inhibited by a lack of enough sufficiently constraining data. In this work, we use the Massive Optimised Parameter Estimation and Data (MOPED) compression algorithm to determine which species should be prioritised for future ice observations to better constrain molecular binding energies. Using the results from this algorithm, we make recommendations for which species future observations should focus on.