Accelerating atomic fine structure determination with graph reinforcement learning

Abstract 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 energy levels can be determined through years of analysis of 10 4 observable spectral lines. We propose a partial automation of this task by casting the analysis procedure as a Markov decision process and solving it by graph reinforcement learning using reward functions partly learned on historical h