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arXiv 2201.01312astro-ph.GA
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历史无关示踪剂：致密星际介质物理条件的遗忘分子探测器

History-independent tracers: Forgetful molecular probes of the physical conditions of the dense interstellar medium

Jonathan Holdship, Serena Viti

备注：
9 pages, 2 figures. Accepted for publication in A&A. Results table at https://uclchem.github.io/hits
发表信息：
A&A 658, A103 (2022)
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摘要：

分子谱线发射是探测天体物理条件的有力工具，但建模起来可能很复杂，而且对于希望限制给定参数的观测者来说，通常不清楚哪些跃迁是最好的目标。因此，我们产生了一个分子物种的气体历史可以忽略不计，消除了主要的建模复杂性。然后，我们确定这些物种中最好的观察时，试图约束各种物理参数。为了实现这一目标，我们使用了一个大的化学模型与不同的化学历史，以确定哪些物种的丰度在1百万年，是不敏感的初始条件。然后，我们使用辐射转移模型来产生这些分子的每个跃迁的强度。最后，我们计算的物理参数和所有的过渡和过渡比之间的互信息，以确定一个给定的参数的值，以排名他们的有用性。我们发现48种是不敏感的气体，其中23个碰撞数据的化学历史。我们产生的所有这些物种的转换和比率的排名列表，使用它们的相互信息与各种气体性质。我们表明，互信息是一个足够的措施，以及如何过渡可以约束一个物理参数，通过恢复已知的探针，并证明随机森林回归模型成为更准确的预测时，包括高分功能。因此，该列表可用于选择观测的目标跃迁，以便最大限度地了解这些物理参数。

英文摘要：

Molecular line emission is a powerful probe of the physical conditions of astrophysical objects but can be complex to model, and it is often unclear which transitions would be the best targets for observers who wish to constrain a given parameter. We therefore produce a list of molecular species for which the gas history can be ignored, removing a major modelling complexity. We then determine the best of these species to observe when attempting to constrain various physical parameters. To achieve this, we use a large set of chemical models with different chemical histories to determine which species have abundances at 1 MYr that are insensitive to the initial conditions. We then use radiative transfer modelling to produce the intensity of every transition of these molecules. We finally compute the mutual information between the physical parameters and all transitions and transition ratios in order to rank their usefulness in determining the value of a given parameter. We find 48 species that are insensitive to the chemical history of the gas, 23 of which have collisional data available. We produce a ranked list of all the transitions and ratios of these species using their mutual information with various gas properties. We show mutual information is an adequate measure of how well a transition can constrain a physical parameter by recovering known probes and demonstrating that random forest regression models become more accurate predictors when high-scoring features are included. Therefore, this list can be used to select target transitions for observations in order to maximize knowledge about those physical parameters.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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