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arXiv 2209.09347astro-ph.GAastro-ph.IM
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识别最具约束力的冰观测以推断分子binding energies

Identifying the most constraining ice observations to infer molecular binding energies

Johannes Heyl, Elena Sellentin, Jonathan Holdship, Serena Viti

摘要:

为了理解晶粒表面化学,必须对反应速率参数有很好的理解。对于基于扩散的反应,这些参数是反应物质的结合能。然而,试图估计这些值从颗粒表面丰度使用贝叶斯推断是抑制了缺乏足够的充分约束的数据。在这项工作中,我们使用大规模优化参数估计和数据(MOPED)压缩算法来确定哪些物种应该优先用于未来的冰观测,以更好地约束分子结合能。使用该算法的结果,我们提出建议,哪些物种未来的观察应该集中在。

英文摘要:

In order to understand grain-surface chemistry, one must have a good understanding of the reaction rate parameters. For diffusion-based reactions, these parameters are binding energies of the reacting species. However, attempts to estimate these values from grain-surface abundances using Bayesian inference are inhibited by a lack of enough sufficiently constraining data. In this work, we use the Massive Optimised Parameter Estimation and Data (MOPED) compression algorithm to determine which species should be prioritised for future ice observations to better constrain molecular binding energies. Using the results from this algorithm, we make recommendations for which species future observations should focus on.

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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