Anomaly Detection for Resonant New Physics with Machine Learning

TL;DR

This paper introduces a model-agnostic anomaly detection method using machine learning to enhance the search for unexpected new physics signals at the LHC, demonstrated by increasing significance in dijet resonance searches.

Contribution

It proposes a novel, model-agnostic anomaly detection technique leveraging machine learning, capable of identifying unexpected signals in high-energy physics data.

Findings

Enhanced detection significance from 2 sigma to 7 sigma for a BSM model.

Applicable to localized signals in phase space with uncorrelated directions.

Improves sensitivity to unanticipated new physics scenarios.

Abstract

Despite extensive theoretical motivation for physics beyond the Standard Model (BSM) of particle physics, searches at the Large Hadron Collider (LHC) have found no significant evidence for BSM physics. Therefore, it is essential to broaden the sensitivity of the search program to include unexpected scenarios. We present a new model-agnostic anomaly detection technique that naturally benefits from modern machine learning algorithms. The only requirement on the signal for this new procedure is that it is localized in at least one known direction in phase space. Any other directions of phase space that are uncorrelated with the localized one can be used to search for unexpected features. This new method is applied to the dijet resonance search to show that it can turn a modest 2 sigma excess into a 7 sigma excess for a model with an intermediate BSM particle that is not currently targeted by a dedicated search.