Higher Order Component Attribution via Learned Surrogates in High Energy Physics

Experimental High Energy Physics (HEP) has adopted the transformer architecture for identification of jets, particularly the 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, or the Particle Transformer architecture, are able to learn these as discriminating features.