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arXiv · 2611.08656

Higher Order Component Attribution via Learned Surrogates in High Energy Physics

Lauri Laatu·Aritra Bal·Benedikt Maier·Markus Klute·Michael Spannowsky
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Abstract

Experimental High Energy Physics (HEP) has adopted the transformer architecture for identification of jets, particularly the state-of-the-art Particle Transformer (ParT) which injects explicit information about particle correlations into the attention mechanism. While able to achieve state-of-the-art results in accuracy, interpretability to its effectiveness has been limited. In this paper we propose component attribution methods using Kolmogorov-Arnold Networks (KANs) that directly tie back to the physical descriptors of jets, to probe whether mechanisms such as Multi-Head Attention (MHA), or the ParT architecture, are able to learn these as discriminating features.

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Lauri Laatu, Aritra Bal, Benedikt Maier, Markus Klute, Michael Spannowsky. 2026-11-05. Higher Order Component Attribution via Learned Surrogates in High Energy Physics. https://arxiv.org/abs/2611.08656

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