Bayesian Inference of the Rates of Surface Reactions in Icy Mantles
Bayesian Inference of the Rates of Surface Reactions in Icy Mantles
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DOI：
10.3847/1538-4357/aae1fa
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发表时间：
2018-09
期刊：
影响因子：
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通讯作者：
J. Holdship;N. Jeffrey;A. Makrymallis;S. Viti;J. Yates
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文献类型：
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作者：
J. Holdship;N. Jeffrey;A. Makrymallis;S. Viti;J. Yates
气粒化学模型中的颗粒表面化学及其处理是一个具有很大不确定性的领域。虽然实验室实验正在取得进展，但关于谷物表面化学仍有许多未知之处。此外，实验产生的结果和参数往往不容易转化为天体化学建模中最常用的速率方程方法。统计方法有可能减少谷物表面化学网络的不确定性。本文建立了分子云中颗粒表面化学的一个简单模型，并通过马尔可夫链蒙特卡罗抽样对反应速率进行了贝叶斯推断。利用分子云中主要化学物种的固体丰度观测数据，以一种适用于速率方程模型的形式，计算了产生CO、CO2、CH3OH和H2O的七个反应的后验分布。这是开发一种方法的关键的第一步，该方法通过观测天体物理环境中的化学丰度来推断反应速度。
Grain surface chemistry and its treatment in gas-grain chemical models is an area of large uncertainty. While laboratory experiments are making progress, there is still much that is unknown about grain surface chemistry. Further, the results and parameters produced by experiments are often not easily translated to the rate equation approach most commonly used in astrochemical modeling. It is possible that statistical methods can reduce the uncertainty in grain surface chemical networks. In this work, a simple model of grain surface chemistry in a molecular cloud is developed and a Bayesian inference of the reactions rates is performed through Markov Chain Monte Carlo sampling. Using observational data of the solid state abundances of major chemical species in molecular clouds, the posterior distributions for the rates of seven reactions producing CO, CO2, CH3OH, and H2O are calculated in a form that is suitable for rate equation models. This represents a vital first step in the development of a method to infer reaction rates from observations of chemical abundances in astrophysical environments.