Automatic anomaly detection in high energy collider data

TL;DR

This paper introduces a genetic programming-based method for automatic anomaly detection in high energy collider data, capable of identifying new physics signatures and validating simulations without relying on specific models.

Contribution

It presents a novel approach that generates and evolves analytic expressions for kinematical variables to improve anomaly detection in collider experiments.

Findings

Effective in detecting anomalies in simulated collider data

Useful for validating Monte-Carlo simulations

Potential to discover new physics signatures

Abstract

We address the problem of automatic anomaly detection in high energy collider data. Our approach is based on the random generation of analytic expressions for kinematical variables, which can then be evolved following a genetic programming procedure to enhance their discriminating power. We apply this approach to three concrete scenarios to demonstrate its possible usefulness, both as a detailed check of reference Monte-Carlo simulations and as a model independent tool for the detection of New Physics signatures.