Implementation of rare isotopologues into machine learning of the chemical inventory of the solar-type protostellar source IRAS 16293-2422
Name
d3dd00020f.pdf
Description
Published version
Size
1.25 MB
Format
Adobe PDF
Checksum (MD5)
f32be07b5d54fdb613c5ce3bd469e75e
Author(s)
Fried, Zachary TP
Lee, Kin Long Kelvin
Byrne, Alex N
McGuire, Brett A
Date Issued
2023
Journal
Digital Discovery
Publisher
Royal Society of Chemistry
Citation
Digital Discovery, 2023,2, 952-966
Version
Final published version
Abstract
Machine learning techniques have been previously used to model and predict column densities in the TMC-1 dark molecular cloud. In interstellar sources further along the path of star formation, such as those where a protostar itself has been formed, the chemistry is known to be drastically different from that of largely quiescent dark clouds. To that end, we have tested the ability of various machine learning models to fit the column densities of the molecules detected in source B of the Class 0 protostellar system IRAS 16293-2422. By including a simple encoding of isotopic composition in our molecular feature vectors, we also examine for the first time how well these models can replicate the isotopic ratios. Finally, we report the predicted column densities of the chemically relevant molecules that may be excellent targets for radioastronomical detection in IRAS 16293-2422B.
MIT Department
Massachusetts Institute of Technology. Department of Chemistry
Terms of Use
Creative Commons Attribution-Noncommercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1039/d3dd00020f
