MACHINE LEARNING OF THE CHEMICAL INVENTORY AND RARE ISOTOPOLOGUES OF THE SOLAR-TYPE PROTOSTELLAR SOURCE IRAS 16293-2422 B
Fried, Zachary Taylor Philip
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6861.pdf
TC03_1.pdf
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https://hdl.handle.net/2142/122326
Description
Title
MACHINE LEARNING OF THE CHEMICAL INVENTORY AND RARE ISOTOPOLOGUES OF THE SOLAR-TYPE PROTOSTELLAR SOURCE IRAS 16293-2422 B
Author(s)
Fried, Zachary Taylor Philip
Contributor(s)
McGuire, Brett A.
Byrne, Alex
Lee, Kelvin
Issue Date
2023-06-20
Keyword(s)
Astronomy
Abstract
Machine learning techniques have been previously used to model and predict column densities in the TMC-1 dark molecular cloud. However, 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. In this talk, I will describe the ability of various machine learning models to fit the column densities of the molecules detected in source B of the Class 0 protostellar binary IRAS 16293-2422. By including a simple encoding of isotopic composition in the molecular feature vectors, I also examine for the first time how well these models can replicate the isotopic ratios. Finally, these trained models provide a list of predicted high-abundance molecules that may be excellent targets for laboratory spectroscopy and subsequent radioastronomical detection in IRAS 16293-2422 B.
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6861.pdf
TC03_1.pdf