Work

Latent Outlier Exposure in Real-Time Anomaly Detection at the Large Hadron Collider

Year: 2025

Type: article

Abstract: We propose a novel approach to real-time anomaly detection at the Large Hadron Collider, aimed at enhancing the discovery potential for new fundamental phenomena in particle physics. Our method leverages the Latent Outlier Exposure technique and is evaluated using three distinct anomaly detection models. Among these is a novel adaptation of the variational autoencoder’s reparameterisation trick, specifically optimised for anomaly detection. The models are validated on simulated datasets representing collider processes from the Standard Model and hypothetical Beyond the Standard Model scenarios. The results demonstrate significant advantages, particularly in addressing the formidable challenge of developing a signal-agnostic, hardware-level anomaly detection trigger for experiments at the Large Hadron Collider.

Source: Computers

DOI: 10.3390/computers14030079

Authors: Thomas Dartnall Stern, Amit Kumar Mishra, James Michael Keaveney

Institutions: University of Cape Town, University West

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

Funder: CERN