| 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) ; Pierini, Maurizio (CERN) ; Wozniak, Kinga Anna (CERN) ; Aarrestad, Thea Klaeboe (Zurich, ETH) ; Ngadiuba, Jennifer (Fermilab) ; Zoi, Irene (Fermilab) ; Bright-Thonney, Samuel Kai (Cornell U.) ; David Shih ; Bal, Aritra (KIT, Karlsruhe, EKP) |
| 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. |
Journalen skapades 2023-11-20, och modifierades senast 2023-11-20
