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Poster communications

Trigger Rate Anomaly Detection with Conditional Variational Autoencoders at the CMS Experiment

Adrian Alan Pol 1 Victor Berger 2 Gianluca Cerminara 1 Cécile Germain 2 Maurizio Pierini 1 
2 TAU - TAckling the Underspecified
LRI - Laboratoire de Recherche en Informatique, Inria Saclay - Ile de France
Abstract : Exploiting the rapid advances in probabilistic inference, in particular variational autoencoders (VAEs) for machine learning (ML) anomaly detection (AD) tasks, remains an open research question. In this work, we use the deep conditional varia-tional autoencoders (CVAE), and we define an original loss function together with a metric that targets AD for hierarchically structured data. Our target application is a real world problem: monitoring the trigger system which is a component of many particle physics experiments at the CERN Large Hadron Collider (LHC). Experiments show the superior performance of this method over vanilla VAEs.
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Submitted on : Saturday, January 4, 2020 - 6:10:40 PM
Last modification on : Saturday, June 25, 2022 - 10:43:36 PM
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  • HAL Id : hal-02428005, version 1


Adrian Alan Pol, Victor Berger, Gianluca Cerminara, Cécile Germain, Maurizio Pierini. Trigger Rate Anomaly Detection with Conditional Variational Autoencoders at the CMS Experiment. Machine Learning and the Physical Sciences Workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS), Dec 2019, Vancouver, Canada. ⟨hal-02428005⟩



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