Searches for new physics in collision events using a statistical technique for anomaly detection

TL;DR

This paper introduces a statistical anomaly detection method for identifying potential new physics in LHC collision data by analyzing deviations from the Standard Model using Z-scores across multiple variables.

Contribution

It presents a novel, model-independent statistical technique employing Z-scores for anomaly detection in high-energy physics collision events.

Findings

Effective identification of anomalous events deviating from the Standard Model

Applicable to large datasets with multiple Lorenz-invariant variables

Enhances the search for new physics beyond current models

Abstract

This paper discusses a statistical anomaly-detection method for model-independent searches for new physics in collision events produced at the Large Hadron Collider (LHC). The method requires calculations of $Z$-scores for a large number of Lorenz-invariant variables to identify events that deviate from those expected for the Standard Model (SM).