Accelerating Atomic Fine Structure Determination with Graph Reinforcement Learning

Agentic AI

Published: arXiv: 2509.16184v1

Authors

M. Ding V. -A. Darvariu A. N. Ryabtsev N. Hawes J. C. Pickering

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 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.

Paper Summary

Problem

Atomic data, such as the energies of energy levels in atoms, is crucial for understanding and predicting the behavior of matter in various fields like astronomy, fusion science, and lighting industries. However, determining these energies is a complex and time-consuming task that requires extensive human labor and expertise in atomic spectroscopy. The current process, known as term analysis, involves analyzing observed atomic spectra to extract energy level energies and transition wavenumbers.

Key Innovation

Researchers have proposed a new artificial intelligence (AI) approach to automate term analysis using graph reinforcement learning. This approach, called Term Analysis with Graph Deep Q-Network (TAG-DQN), involves casting the analysis procedure as a Markov decision process and solving it using a variant of the Deep Q-network algorithm. The AI agent learns to choose valid actions that maximize a reward function trained on human preferences from past analyses.

Practical Impact

The new AI approach has the potential to accelerate the determination of atomic fine structure, which is essential for various applications. By automating the term analysis process, researchers can rapidly develop fundamental atomic data that would otherwise take decades to obtain. This can lead to breakthroughs in atomic physics, astronomy, and fusion technology, and can help address the growing demands for atomic data from these fields.

Analogy / Intuitive Explanation

Imagine trying to solve a complex puzzle with many interconnected pieces. Each piece represents a spectral line, and the puzzle itself represents the energy level energies and transition wavenumbers that need to be determined. The AI agent in TAG-DQN is like a super-smart puzzle solver that uses machine learning to figure out the correct connections between the pieces, allowing it to quickly and accurately determine the energy level energies and transition wavenumbers.

Paper Information

Categories:

physics.atom-ph cs.AI cs.LG

Published Date:

arXiv ID:

2509.16184v1

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