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CMS Note
Report number CMS-NOTE-2023-013 ; CERN-CMS-NOTE-2023-013
Title Machine learning techniques for model-independent searches in dijet final states
Author(s) Harris, Philip (MIT) ; Mccormack, William Patrick (MIT) ; Park, Sang Eon (MIT) ; Quadfasel, Tobias (Hamburg U.) ; Sommerhalder, Manuel (Hamburg U.) ; Moureaux, Louis Jean (Hamburg U.) ; Kasieczka, Gregor (Hamburg U.) ; Amram, Oz (Fermilab) ; Maksimovic, Petar (Johns Hopkins U.) ; Maier, Benedikt (KIT, Karlsruhe, EKP) Εμφάνιση και των 18 συγγραφέων
Publication 2023
Imprint 10 Nov 2023
Number of pages 11
Subject category Detectors and Experimental Techniques
Accelerator/Facility, Experiment CERN LHC ; CMS
Keywords Physics
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 anomalous 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.

 


 Δημιουργία εγγραφής 2023-11-20, τελευταία τροποποίηση 2023-11-20


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