Searching for anomalous quartic gauge couplings at muon colliders using principal component analysis

Abstract

Searching for new physics (NP) is one of the areas of high-energy physics that requires the most processing of large amounts of data. At the same time, quantum computing has huge potential advantages when dealing with large amounts of data. The principal component analysis (PCA) algorithm may be one of the bridges connecting these two aspects. On the one hand, it can be used for anomaly detection, and on the other hand, there are corresponding quantum algorithms for PCA. In this paper, we investigate how to use PCA to search for NP. Taking the example of anomalous quartic gauge couplings in the tri-photon process at muon colliders, we find that PCA can be used to search for NP. Compared with the traditional event selection strategy, the expected constraints on the operator coefficients obtained by PCA based event selection strategy are even better.

Measuring the anomalous quartic gauge couplings in the W+W− → W+W− process at muon collider using artificial neural networks

“…This paper employs the same energies and luminosities as those in Refs [56,75], but with a different event selection strategy. To estimate the effectiveness of our approach, we compare the expected coefficient constraints in this paper with those in Refs.…”

“…To estimate the effectiveness of our approach, we compare the expected coefficient constraints in this paper with those in Refs. [56,75], the…”

“…In recent years, machine learning algorithms (ML) have experienced rapid development in the field of HEP [40][41][42][43][44][45][46][47][48][49][50][51]. From a ML perspective, searching for NP signals can be viewed as anomaly detection [50][51][52][53][54][55][56][57][58][59]. In addition to this, discriminating between NP signal events and background events can be viewed as a classification problem, and there exists a large number of classification algorithms available for use in ML.…”