Implementation of Rare Isotopologues into Machine Learning of the Chemical Inventory of the Solar-Type Protostellar Source IRAS 16293-2422 - 26 May 2023 abstract

Aug 4, 2026

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Machine learning models are tested for modeling molecular column densities in IRAS 16293-2422B, a Class 0 protostellar source whose chemistry differs strongly from quiescent dark clouds. By adding a simple encoding of isotopic composition to molecular feature vectors, the study evaluates whether predicted values can reproduce observed isotopic ratios. Predicted column densities for chemically relevant molecules are then reported as promising targets for radioastronomical detection, aiming to support line assignments and constrain astrochemical modeling for low-mass protostars.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

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Introduction

Motivation for machine learning in astrochemical modeling

IRAS 16293-2422B as a target source

Extending the method to isotopically substituted species

arXiv :2305 . 11193v2 [ astro-ph .GA] 26 May 2023 Implementation of Rare Isotopologues into Machine Learning of the Chemical Inventory of the Solar-Type Protostellar Source IRAS 16293-2422 Zachary T. P. Fried, a Kin Long Kelvin Lee, b Alex N. Byrne, c and Brett A. McGuired ,e 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. 1 Introduction The observation of interstellar molecules is a central component of astrochemical studies. Molecular species have shaped our understanding of star 1 and planet formation 2 , can trace stellar outflows 3 , interstellar shocks 4 , and protoplanetary disks 5 , and can serve as probes of the physical conditions of interstellar sources such as the temperature 6. However, until recently, in order to model interstellar abundances and predict new molecules for detection, observations have relied on complex chemical models based on a vast network of interconnected reactions (e.g. Ruaudet al. 7 , Wakelam et al. 8 ) . While these astrochemical models can be excellent tools to explore specific chemical processes that occur in space, their predictive ability can also be quite limited for several reasons (e.g. McGuire et al. 9 ) . Firstly, these models are by definition incomplete representations of the true chemical complexity of the interstellar medium because network expansions rely on human input. Additionally, the networks are oftentimes dependent on uncertain extrapolated rate constants 10. In an attempt to predict molecular abundances without the a Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139; [E-mail: zfried@mit.edu](E-mail: zfried@mit.edu) b Accelerated Computing Systems and Graphics Group, Intel Corporation, 2111 NE 25thAve., Hillsboro, OR 97124 c Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139 d Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139; E-mail: [brettmc@mit.edu](brettmc@mit.edu) e National Radio Astronomy Observatory, Charlottesville, VA 22903 † Electronic Supplementary Information (ESI) available: [details of any supplementary information available should be included here] . See DOI: 00.0000/00000000 . need for complete networks, Lee et al. 11 introduced a novel methodology involving machine learning. A major benefit of their approach contrasts traditional astrochemical modeling, as it requires no prior knowledge of the conditions of an interstellar source or any reaction pathways involving the previously detected molecules. Instead, abundances are expressed purely in terms of a chemical vector space. Simple regression algorithms were shown to significantly outperform traditional astrochemical models in reproducing the abundances of molecules already observed, and provided a straightforward way to extrapolate to yet undetected molecules. An interstellar source for which this machine learning technique could be effectively applied is the Class 0 protostar IRAS 16293-2422B (hereafter referred to as IRAS 16293B) . IRAS 16293B is one component of the protostellar system IRAS 16293, which is located in the L1689 region of the ρ Ophi

FAQs

What is the main objective of this study for IRAS 16293-2422B?

To test machine learning models that fit molecular column densities in IRAS 16293-2422B and evaluate whether they can replicate isotopic ratios when isotopic information is included.

How does the method extend earlier machine learning approaches in astrochemistry?

It incorporates an encoding of isotopic composition into molecular feature vectors, enabling assessment of isotopic ratio reproduction beyond fitting overall abundances.

Why is IRAS 16293-2422B considered advantageous for this work?

Its submillimeter spectrum is extremely rich and has very narrow line widths, reducing line confusion and providing many detected molecular features suitable for model fitting and target selection.

Aug 4, 2026

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