用图强化学习自动解析原子光谱，提速百倍且精度高
Accelerating Atomic Fine Structure Determination with Graph Reinforcement Learning
- 将光谱分析转为马尔可夫决策过程，用图神经网络+强化学习自动推断能级
- 数小时内完成数百个能级计算，钴离子匹配度达95%，钕离子达54%-87%
- 适合等离子体诊断、天体物理和核聚变领域急需海量原子数据的科研人员
通过分析原子光谱确定的原子数据对等离子体诊断至关重要。对于每个低电离度d-和f亚壳层原子物种，需经数年分析约10^4条可观测谱线，才能确定约10^3个精细结构能级。本文提出将该分析过程建模为马尔可夫决策过程，利用基于历史人工决策学习的奖励函数，通过图强化学习实现自动化。在Co II和Nd II-III现有谱线列表与理论计算上的评估中，数小时内完成数百个能级计算，结果与已发表值相比，Co II匹配率达95%，Nd II-III匹配率为54%-87%。当前原子精细结构确定效率难以满足天文学与聚变科学日益增长的数据需求，本研究的人工智能方法为填补这一差距奠定基础。
原文摘要 · Abstract (English)
Atomic data determined by analysis of observed atomic spectra are essential for plasma diagnostics. For each low-ionisation open d- and f-subshell atomic species, around $10^3$ fine structure level energies can be determined through years of analysis of $10^4$ observable spectral lines. We propose the automation of this task by casting the analysis procedure as a Markov decision process and solving it by graph reinforcement learning using reward functions learned on historical human decisions. In our evaluations on existing spectral line lists and theoretical calculations for Co II and Nd II-III, hundreds of level energies were computed within hours, agreeing with published values in 95% of cases for Co II and 54-87% for Nd II-III. As the current efficiency in atomic fine structure determination struggles to meet growing atomic data demands from astronomy and fusion science, our new artificial intelligence approach sets the stage for closing this gap.
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