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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https://hdl.handle.net/2142/122326 Copy

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.

Publisher

International Symposium on Molecular Spectroscopy

Type of Resource

Text

Genre of Resource

Conference Paper / Presentation

Language

eng

Handle URL

https://hdl.handle.net/2142/122326

DOI

https://doi.org/10.15278/isms.2023.6861

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