Zero-bias new particle searches using autoencoders in UPCs and diffractive events

TL;DR

This paper introduces an unsupervised autoencoder-based method to detect rare particle decays and exotic hadrons in collider data, enhancing the discovery potential for new physics phenomena.

Contribution

It applies autoencoders to identify anomalies in particle decay data, demonstrating effectiveness in separating typical and rare events in high-energy physics experiments.

Findings

Autoencoder successfully detects anomalies in simulated decay data.

Peaks in invariant mass distributions correspond to injected rare signals.

Method shows potential for discovering new physics beyond the Standard Model.

Abstract

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 $\ensuremath{{\mathrm J}/\psi}\xspace$ and {\ensuremath{\psi'}\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.