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<p>Transformer networks excel in scientific applications. We explore two scenarios in ultra-high-energy cosmic ray simulations to examine what these network architectures learn. First, we investigate the trained positional encodings in air showers which are azimuthally symmetric. Second, we visualize the attention values assigned to cosmic particles originating from a galaxy catalog. In both cases, the Transformers learn plausible, physically meaningful features.</p>

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What Exactly Did the Transformer Learn from Our Physics Data?

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月15日

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  • Martin Erdmann,
  • Niklas Langner,
  • Josina Schulte,
  • Dominik Wirtz

摘要

Transformer networks excel in scientific applications. We explore two scenarios in ultra-high-energy cosmic ray simulations to examine what these network architectures learn. First, we investigate the trained positional encodings in air showers which are azimuthally symmetric. Second, we visualize the attention values assigned to cosmic particles originating from a galaxy catalog. In both cases, the Transformers learn plausible, physically meaningful features.