Accelerating Atomic Fine Structure Determination with Graph Reinforcement Learning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911163762081792 |
|---|---|
| author | Ding, M. Darvariu, V. -A. Ryabtsev, A. N. Hawes, N. Pickering, J. C. |
| author_facet | Ding, M. Darvariu, V. -A. Ryabtsev, A. N. Hawes, N. Pickering, J. C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16184 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating Atomic Fine Structure Determination with Graph Reinforcement Learning Ding, M. Darvariu, V. -A. Ryabtsev, A. N. Hawes, N. Pickering, J. C. Atomic Physics Artificial Intelligence Machine Learning 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. |
| title | Accelerating Atomic Fine Structure Determination with Graph Reinforcement Learning |
| topic | Atomic Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.16184 |