# AI-powered full-data set search for new physics in ultraperipheral and diffractive events

## TL;DR

This paper explores AI-driven anomaly detection methods to identify rare particle decays and exotic hadrons in low-background collider environments, demonstrating their potential for new physics searches at the LHC.

## Contribution

It introduces a novel AI-based approach trained on toy samples to detect rare processes like pentaquarks and exotic decays in collider data, enhancing search sensitivity.

## Key findings

- Models successfully flagged simulated rare processes as anomalous.
- Approach enables setting upper limits on exotic particle production.
- Demonstrates applicability for future collider data analysis.

## Abstract

We present possible strategies for anomaly detection of rare particle decays and exotic hadrons, such as pentaquarks, in low-background environments such as those characteristic of diffractive events and ultraperipheral \pp, \pA, or \AAcoll collisions at the CERN Large Hadron Collider (LHC). Our models are trained with toy samples representing the UPC processes measured until now by the ALICE Collaboration. When samples containing rare processes such as $\jpsi\rightarrow4\pi$ and pentaquark production, where the number of injected pentaquark events is estimated based on current experimentally available upper limits, and those for $\jpsi\rightarrow4\pi$ are estimated through the branching ratio of the decay channel, are analyzed, the rare processes are flagged as anomalous by the models. This approach demonstrates the applicability of such a technique for searches for new physics in the current and future data sets at collider experiments with high purity, while also allowing for the measurement of upper limits for the production of exotica.