Disentangling CO Chemistry in a Protoplanetary Disk Using Explanatory Machine Learning Techniques

• URL: http://arxiv.org/abs/2312.05254v1
• Date: Fri, 8 Dec 2023 18:59:22 GMT
• Title: Disentangling CO Chemistry in a Protoplanetary Disk Using Explanatory Machine Learning Techniques
• Authors: Amina Diop (1), Ilse Cleeves (1), Dana Anderson (2), Jamila Pegues (3), Adele Plunkett (4) ((1) University of Virginia, (2) Earth and Planets Laboratory, Carnegie Institution for Science, (3) Space Telescope Science Institute, (4) National Radio Astronomy Observatory)
• Abstract summary: Molecular abundances in protoplanetary disks are highly sensitive to the local physical conditions. We present a new approach to understanding these chemical and physical interdependencies using machine learning.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Molecular abundances in protoplanetary disks are highly sensitive to the local physical conditions, including gas temperature, gas density, radiation field, and dust properties. Often multiple factors are intertwined, impacting the abundances of both simple and complex species. We present a new approach to understanding these chemical and physical interdependencies using machine learning. Specifically we explore the case of CO modeled under the conditions of a generic disk and build an explanatory regression model to study the dependence of CO spatial density on the gas density, gas temperature, cosmic ray ionization rate, X-ray ionization rate, and UV flux. Our findings indicate that combinations of parameters play a surprisingly powerful role in regulating CO compared to any singular physical parameter. Moreover, in general, we find the conditions in the disk are destructive toward CO. CO depletion is further enhanced in an increased cosmic ray environment and in disks with higher initial C/O ratios. These dependencies uncovered by our new approach are consistent with previous studies, which are more modeling intensive and computationally expensive. Our work thus shows that machine learning can be a powerful tool not only for creating efficient predictive models, but also for enabling a deeper understanding of complex chemical processes.

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