Exploring Effects of Modified Machine Learning Pipelines of Astrochemical Inventories
Name
Toru_Shay_2025_ApJ_985_123.pdf
Description
Published version
Size
1.09 MB
Format
Adobe PDF
Checksum (MD5)
ed240641320a2bda6ab2caa75d5688e8
Author(s)
Toru Shay, Hannah
Scolati, Haley N
Wenzel, Gabi
Lee, Kin Long Kelvin
Marimuthu, Aravindh N
McGuire, Brett A
Date Issued
May 19, 2025
Journal
The Astrophysical Journal
Publisher
American Astronomical Society
Citation
Hannah Toru Shay et al 2025 ApJ 985 123
Version
Final published version
Abstract
Machine learning pipelines for astrochemical inventories have been introduced as a useful addition to the astrochemist toolbox, having first been used to model and predict column densities in the Taurus Molecular Cloud (TMC-1). Rapid changes in the field of machine learning have provided new tools in optimizing this pipeline, including improved vector representations. Furthermore, the addition of new detections since the original model allows for a retrospective analysis of model performance, in addition to new data for the model. This study revisits TMC-1, investigating both effects of an increased detection inventory on the model and changes to various portions of the pipeline, yielding a significant improvement in column density predictions. Through these comparisons, we attempt to derive insight into the ultimate effectiveness of these models, as well as their current limitations and words of caution in their use and interpretation. Finally, we provide suggestions for future machine learning of interstellar sources.
MIT Department
Massachusetts Institute of Technology. Department of Chemistry
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3847/1538-4357/adc80b
