Implementation of rare isotopologues into machine learning of the chemical inventory of the solar-type protostellar source IRAS 16293-2422

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.