Machine learning techniques for model-independent searches in dijet final states
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2023
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Report
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Abstract
We present the performance of Machine Learning–based anomaly detection techniques for extracting potential new physics phenomena in a model-agnostic way with the CMS Experiment at the Large Hadron Collider. We introduce five distinct outlier detection or density estimation techniques, namely CWoLa, Tag N’ Train, CATHODE, QUAK, and QR-VAE, tailored for the identification of anoma- lous jets originating from the decay of unknown heavy particles. We demonstrate the utility of these diverse approaches in enhancing the sensitivity to a wide variety of potential signals and assess their comparative performance in simulation.
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2023-013
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CERN
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03593 - Dissertori, Günther / Dissertori, Günther