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.

分子丰度、原行星盘、机器学习、CO 流失、化学过程

利用解释型机器学习技术解开原行星盘中的 CO 化学

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

The inverse imaging task in radio interferometry is a key limiting factor to
retrieving Bayesian uncertainties in radio astronomy in a computationally
effective manner. We use a score-based prior derived from optical images of
galaxies to recover images of protoplanetary disks from the DSHARP survey. We
demonstrate that our method produces plausible posterior samples despite the
misspecified galaxy prior. We show that our approach produces results which are
competitive with existing radio interferometry imaging algorithms.

利用光学图像的基于得分的先验，我们以高效的计算方式从 DSHARP 调查中恢复原行星盘的图像样本，尽管银河系先验估计不准确，我们的方法仍然能够得到可信的后验样本，并且能够与现有射电干涉成像算法有竞争力的结果。