A statistical and machine learning approach to the study of astrochemistry

Aug 5, 2026

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A statistical and machine learning framework targets key uncertainties in astrochemistry arising from disagreements in grain-surface binding energies that control chemical networks. Bayesian inference estimates binding energies, while highlighting the practical difficulty of inference when observational or training data are insufficient. The MOPED compression algorithm then identifies which species should be prioritized for future detections to better constrain binding energies. Finally, an interpretable machine-learning method explains nonlinear links between binding energies and resulting molecular abundances.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Grain-surface chemistry and binding-energy uncertainty

Bayesian inference for binding-energy estimation

Using MOPED to prioritize future detections

Interpretable ML for nonlinear abundance relationships

arXiv :2306 .05790v1 [ astro-ph .GA] 9 Jun 2023 A statistical and machine learning approach to the study of astrochemistry Johannes Heyl, a‡ Serena Viti, b,a‡ and Gijs Vermariënb In order to obtain a good understanding of astrochemistry, it is crucial to better understand the key parameters that govern grain-surface chemistry. For many chemical networks, these crucial parameters are the binding energies of the species. However, there exists much disagreement regarding these values in the literature. In this work, a Bayesian inference approach is taken to estimate these values. It is found that this is difficult to do in the absence of enough data. The Massive Optimised Parameter Estimation and Data (MOPED) compression algorithm is then used to help determine which species should be prioritised for future detections in order to better constrain the values of binding energies. Finally, an interpretable machine learning approach is taken in order to better understand the non-linear relationship between binding energies and the final abundances of specific species of interest. 1 Introduction Giant Molecular Clouds in our Milky Way as well as in other galaxies host gas which is almost entirely molecular, with densities above ∼ 100 cm−3 and temperature below ∼ 100 K. These denser, cooler regions contain a significant fraction of the nonstellar baryonic matter in a galaxy and they are usually much more massive than large tenuous ones. The importance of these regions lie in the fact that they are key for our understanding of how galaxies form and evolve because this denser, cooler gas is the reservoir of matter that forms stars and planets, as well as the gas that fuels the centres of galaxies. From an astrochemical point of view, due to their high densities and low temperatures, these regions are great laboratories to study the interactions of gas and dust, with species from the gas phase ‘freezing’ onto the dust grains present, and forming icy mantles rich in hydrogenated as well as complex organic molecules (COMs), due to the many fast surface reactions that take place. As stars form in these clouds (or if any other energetic process takes place) then the dust temperature may reach the mantle sublimation temperature (∼ 100K), and the molecules in the mantles are injected into the gas, where they react and form new, more complex, molecules. Associated with star formation, as well as with active galactic nucleus (AGN) activity, are highly supersonic collimated jets and molecular outflows. When the outflowing material encounters the quiescent gas of molec- a Department of Physics and Astronomy, University College London, Gower Street, WC1E 6BT, London, UK; E-mail: [johannes.heyl.19@ucl.ac.uk](johannes.heyl.19@ucl.ac.uk) b Leiden Observatory, Leiden University, PO Box 9513, 2300 RA Leiden, The Nether lands ‡These authors contributed equally. ular cloud, it creates shocks, where the grain mantles are (partially) sputtered and the refractory grains are shattered. Again, here, the interaction of gas and dust varies within very short timescales and the effects of chemistry and dynamics are interlocked in a complex non-linear fashion. In summary, the gas and dust surface compositions exhibit a complicated time dependent, non-linear chemistry that strongly depends on the physical environment. There are many open questions - still - about such interaction: what is the unprocessed ice composition? What are the efficiencies of the viable surface reactions? And how do the energetics of the ISM (cosmic rays, UV radiation, shocks) influence the processed ices? In order to determine accurate estimates of the abundances of molecular species as a function of all the parameters that influence their chemistry we need to be able to answer such questions. In other words, we need to understand the chemical pathways towards each molecule and its dependencies on the density, temperature and energetics of the gas and dust before molecul

FAQs

Why are binding energies central to astrochemical modeling?

For many chemical networks, binding energies govern grain-surface chemistry, which in turn controls how molecules form and evolve.

What challenge limits Bayesian estimation of binding energies?

Reliable inference becomes difficult without enough data, making the estimation underdetermined.

How does MOPED contribute to improving future constraints?

MOPED compression helps determine which species to prioritize in future detections so the available information more strongly constrains binding-energy values.

Aug 5, 2026

Upload by Finn