Accelerating Atomic Fine Structure Determination with Graph Reinforcement Learning
M. Ding, V.-A. Darvariu, A. N. Ryabtsev, N. Hawes, J. C. Pickering
通过分析观察到的原子光谱来确定的原子数据对于等离子体诊断至关重要。 对于每个低电离开放的d-和f-亚壳原子物种,大约10^3的精细结构水平能量可以通过对10^4可观测光谱线的多年分析来确定。 我们提出这项任务的自动化,将分析过程作为马尔可夫决策过程,并使用在人类历史决策上学到的奖励函数进行图强化学习来解决它。 在对现有光谱线列表的评估以及Co II和Nd II-III的理论计算中,在数小时内计算了数百种水平能量,与95中的已发表值一致。
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 ou...