18th International Workshop on Boosted Object Phenomenology, Reconstruction, Measurements, and Searches at Colliders

Going HyPER (and beyond): geometric machine learning methods for collider data analysis

16 Jul 2026, 11:50

20m

Talk Novel methods from information theory and ML

Speaker

Ethan Lewis Simpson (The University of Manchester (GB))

Description

Modern analysis of collider data relies heavily on machine learning. This talk will discuss geometric machine learning methods, focusing on the HyPER tool: applying hypergraph representation learning to reconstruct short-lived particles in collider data. The method is extended through generative ML techniques to include production processes featuring neutrinos, making the HyPER tool a comprehensive event reconstruction method. Studies on the impact of event reconstruction in signal-background classification will also be presented, as well as showing contrasting ML approaches to building event observables. The methodologies presented will be showcased in several different collider processes, and their application to real data discussed.

Presentation materials

BOOST_VyPER_els285_16Jul26f.pdf