Unravelling physics beyond the standard model with classical and quantum anomaly detection

TL;DR

This paper introduces a supervised anomaly detection method using classical and quantum support vector classifiers to identify potential new physics signals in high energy physics data, showing promising results for discovering beyond Standard Model phenomena.

Contribution

It proposes a novel supervised anomaly detection strategy with artificial anomalies and applies classical and quantum SVCs, demonstrating high accuracy in identifying BSM events.

Findings

Classical and quantum SVCs effectively detect artificial anomalies.

Quantum algorithms show potential for improved classification accuracy.

High accuracy in identifying realistic BSM events using trained SVCs.

Abstract

Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadron Collider (LHC). However, current experiments do not indicate clear signs of new physics that could guide the development of additional Beyond Standard Model (BSM) theories. Identifying signatures of new physics out of the enormous amount of data produced at the LHC falls into the class of anomaly detection and constitutes one of the greatest computational challenges. In this article, we propose a novel strategy to perform anomaly detection in a supervised learning setting, based on the artificial creation of anomalies through a random process. For the resulting supervised learning problem, we successfully apply classical and quantum Support Vector Classifiers (CSVC and QSVC respectively) to identify the artificial anomalies among the SM events. Even more promising, we find that employing an SVC trained to identify the artificial anomalies, it is possible to identify realistic BSM events with high accuracy. In parallel, we also explore the potential of quantum algorithms for improving the classification accuracy and provide plausible conditions for the best exploitation of this novel computational paradigm.