2024/11/01 by S. Ragoni, Ragoni, Simone, Janet Elizabeth Seger +3 · 1 citation
Physics and Astronomy · #Dark Matter and Cosmic Phenomena #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Particle Detector Development and Performance #Particle physics theoretical and experimental studies
paper · pdf · doi:10.48550/arxiv.2411.00903
openalex publication_date 2024/11/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present an application of unsupervised learning for zero-bias detection of rare particle decays and exotic hadrons in low-background environments such as those characteristic of diffractive events and ultraperipheral pp, p--A, or A--A collisions at the CERN Large Hadron Collider (LHC), or in e--A collisions at the ePIC experiment at the future Electron-Ion Collider (EIC). Using a toy dataset simulating the decays of known resonances, including \ensuremath\mathrm J/ψ\xspace and \ensuremathψ'\xspace, as well as more exotic candidates, we implement an autoencoder neural network to identify anomalies in the decay kinematics. The autoencoder, trained solely on typical events, is designed to reconstruct normal decays with low error while flagging anomalous decays based on the reconstruction error. We demonstrate that the autoencoder successfully separates typical decays from rare exotic events, with peaks in the invariant mass distribution corresponding to the injected rare signals. Our method shows promise in detecting rare, unpredicted processes in large-scale collider data, offering an effective approach for discovering new physics beyond the Standard Model.