Articles

High-dimensional anomaly detection with radiative return in e+e− collisions

Gonski, Julia; Lai, Jerry; Nachman, Benjamin; Ochoa, Inês

Experiments at a future e+e− collider will be able to search for new particles with masses below the nominal centre-of-mass energy by analyzing collisions with initial-state radiation (radiative return). We show that machine learning methods that use imperfect or missing training labels can achieve sensitivity to generic new particle production in radiative return events. In addition to presenting an application of the classification without labels (CWoLa) search method in e+e− collisions, our study combines weak supervision with variable-dimensional information by deploying a deep sets neural network architecture. We have also investigated some of the experimental aspects of anomaly detection in radiative return events and discuss these in the context of future detector design.

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Also Published In

Title
Journal of High Energy Physics
DOI
https://doi.org/10.1007/JHEP04(2022)156

More About This Work

Published Here
July 22, 2024

Notes

Beyond Standard Model, e
+-e
− Experiments, Particle and Resonance Production