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

• URL: http://arxiv.org/abs/2301.10787v2
• Date: Fri, 27 Jan 2023 13:43:43 GMT
• Title: Unravelling physics beyond the standard model with classical and quantum anomaly detection
• Authors: Julian Schuhmacher, Laura Boggia, Vasilis Belis, Ema Puljak, Michele Grossi, Maurizio Pierini, Sofia Vallecorsa, Francesco Tacchino, Panagiotis Barkoutsos, and Ivano Tavernelli
• Abstract summary: Current experiments do not indicate clear signs of new physics that could guide the development of additional Beyond Standard Model (BSM) theories. 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. Even more promising, we find employing an SVC trained to identify the artificial anomalies, it is possible to identify realistic BSM events with high accuracy.
• Score: 1.014313095022286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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