Machine Learning of the Chemical Inventory and Rare Isotopologues of the Solar-Type Protostellar Source IRAS 16293-2422 B

Aug 4, 2026

Upload by Rowan

Machine learning models are applied to predict and fit molecular column densities in source B of the Class 0 protostellar binary IRAS 16293-2422. Previous work on the TMC-1 dark molecular cloud is extended to the chemically distinct environment further along the star-formation pathway. By encoding isotopic composition into molecular feature vectors, the models are evaluated for reproducing isotopic ratios. Trained models generate high-abundance molecule candidates suitable for laboratory spectroscopy and radioastronomical detection.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages1

Reading Time3 min

AccessFree to download

Modeling molecular column densities

Isotopic feature encoding for rare isotopologues

Predicted targets for spectroscopy and radioastronomical detection

FAQs

What does the talk focus on regarding IRAS 16293-2422 B?

It focuses on using multiple machine learning models to fit the column densities of molecules detected in source B of IRAS 16293-2422.

How are isotopic ratios incorporated into the machine learning models?

Isotopic composition is encoded into the molecular feature vectors, enabling assessment of how well the models reproduce isotopic ratios.

What outputs are produced after training the models?

The trained models provide a list of predicted high-abundance molecules that can serve as targets for laboratory spectroscopy and subsequent radioastronomical detection.

Aug 4, 2026

Upload by Rowan