GAN-AE : An anomaly detection algorithm for New Physics search in LHC data

Abstract

In recent years, interest has grown in alternative strategies for the search for New Physics beyond the Standard Model. One envisaged solution lies in the development of anomaly detection algorithms based on unsupervised machine learning techniques. In this paper, we propose a new Generative Adversarial Network-based auto-encoder model that allows both anomaly detection and model-independent background modeling. This algorithm can be integrated with other model-independent tools in a complete heavy resonance search strategy. The proposed strategy has been tested on the LHC Olympics 2020 dataset with promising results.

11 pages, 9 figures

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Cited by in corpus (5)

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• Graph theory inspired anomaly detection at the LHC
• Learnable cut flow for high energy physics