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Accelerating Atomic Fine Structure Determination with Graph Reinforcement Learning

arXiv 2509.16184 ↗First posted 19 Sep 2025physics.atom-ph cs.AI cs.LGcited by 0

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Abstract (arXiv)

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 103 fine structure level energies can be determined through years of analysis of 104 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 55 references that are in the map. First posted on arXiv 19 Sep 2025.

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References · 12

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
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  3. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
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  8. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 786
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  12. 2023Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discoverycited by 3

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