Disentangling CO Chemistry in a Protoplanetary Disk Using Explanatory
  Machine Learning Techniques
Amina Diop Ilse Cleeves Dana Anderson Jamila Pegues Adele Plunkett
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.