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Proceedings of The Ninth Annual Conference on Large Hadron Collider Physics — PoS(LHCP2021)
Although most of Beyond Standard Model (BSM) searches are targeting specific theory models, there has always been a keen interest in the development of model-independent methods amongst the High Energy Physics (HEP) community. Machine Learning (ML) based anomaly detection stands among the latest up-and-coming avenues for creating model-agnostic BSM searches. The focus of this research is the design of anomalous event taggers based on autoencoder models. Alongside the signal discriminationdoi:10.22323/1.397.0340 fatcat:2ka7kqgdmvhs7i2h34vpfp7htq