Bridging Machine Learning and Spectroscopy: A New Analysis for Astrochemical Target Selection
Name
Toru_Shay_2025_ApJ_993_75.pdf
Description
Published version
Size
3.57 MB
Format
Adobe PDF
Checksum (MD5)
327c91dd50bd70426f18183240cfdc13
Author(s)
Toru Shay, Hannah
Wenzel, Gabi
Xue, Ci
McGuire, Brett A
Date Issued
October 24, 2025
Journal
The Astrophysical Journal
Publisher
American Astronomical Society
Citation
Hannah Toru Shay et al 2025 ApJ 993 75
Version
Final published version
Abstract
In the last few years, several approaches have used machine learning to reproduce the inventory and column densities of interstellar molecular sources. These models can also provide predictions of other molecules that might make good targets for discovery in a source, along with a predicted column density. A molecular target and column density alone are often not enough to dictate a good target; spectroscopic considerations (e.g., permanent electric dipole moments, line intensities, and line frequencies) need to be accounted for as well. Here we describe a new approach to take these considerations into account, in an attempt to identify the hidden likely candidates among thousands of molecules and associated column densities recommended by machine learning. We leverage machine learning results alongside quantum chemical and astronomical considerations to define a “detectability” metric and then apply this analysis to machine learning–recommended molecules in TMC–1. We find that this approach significantly narrows the field of candidate species but also highlights some of the ongoing issues with results from machine learning models trained on the very small (for machine learning) data sets produced by observations. We discuss these in the context of the need for human intervention, finding that our approach reduces but certainly does not eliminate this requirement.
MIT Department
Massachusetts Institute of Technology. Department of Chemistry
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3847/1538-4357/ae03ae
