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Stronger symbolic summary statistics for the LHC
Nathalie Soybelman · Anja Butter · Tilman Plehn · Johann Brehmer

Analyzing the high-dimensional data collected at the Large Hadron Collider experiments often requires a balance between maximizing sensitivity and maintaining interpretability by domain experts. We propose a new algorithm to construct powerful summary statistics for LHC processes in the form of simple symbolic expressions. First, we extract latent information from a chain of simulators; through symbolic regression on this data we then learn approximately sufficient statistics. Observables constructed in this way can be used as plug-in replacements for established summary statistics, potentially improving the precision of scientific results without adding any overhead. In Higgs production in weak boson fusion, our algorithm rediscovers well-known heuristics and proposes new, moderately complex formulas that rival the new physics reach of neural networks.

Author Information

Nathalie Soybelman (Universität Heidelberg)
Anja Butter (Universität Heidelberg)
Tilman Plehn (Heidelberg)
Johann Brehmer (New York University)

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