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A major task in particle physics is the measurement of rare signal processes.These measurements are highly dependent on the classification accuracy of theseevents in relation to the huge background of other Standard Model processes.Reducing the background by a few tens of percent with the same signalefficiency can already increase the sensitivity considerably. This workdemonstrates the importance of incorporating physical information into deeplearning-based event selection. The paper includes this information intodifferent methods for classifying events, in particular Boosted Decision Trees,Transformer Architectures (Particle Transformer) and Graph Neural Networks(Particle Net). In addition to the physical information previously proposed forjet tagging, we add particle measures for energy-dependent particle-particleinteraction strengths as predicted by the leading order interactions of theStandard Model (SM). We find that the integration of physical information intothe attention matrix (transformers) or edges (graphs) notably improvesbackground rejection by $10\%$ to $40\%$ over baseline models (a graphnetwork), with about $10\%$ of this improvement directly attributable to whatwe call the SM interaction matrix. In a simplified statistical analysis, wefind that such architectures can improve the significance of signals by asignificant factor compared to a graph network (our base model).

Attention to the strengths of physical interactions: Transformer and graph-based event classification for particle physics experiments