Search for New Phenomena in Two-Body Invariant Mass Distributions Using Unsupervised Machine Learning for Anomaly Detection at √s=13 TeV with the ATLAS Detector
ContributorsATLAS Collaboration
CollaboratorsAlgren, Malte; Alves Cardoso, Mario; Antel, Claire; Axiotis, Konstantinos; Cepaitis, Vilius; Clark, Allan Geoffrey; Della Volpe, Domenico; Drozdova, Mariia; Ehrke, Lukas; Ferrere, Didier; Franchellucci, Stefano
; Golling, Tobias; Gonzalez Sevilla, Sergio; Guth, Manuel; Harada, Daigo; Iacobucci, Giuseppe; Lezki, Merve; Klein, Samuel
; Leigh, Matthew; Moreno Martinez, Carlos; Nindhito, Herjuno Rah; Paolozzi, Lorenzo; Pirttikoski, Antti Pietari
; Quetant, Guillaume
; Raine, Johnny; Sabater Iglesias, Jorge; Schramm, Steven; Schroeer, Tomke; Sengupta, Debajyoti
; Sfyrla, Anna; Shirabe, Shohei; Theiner, Ondrej; Wu, Xin; Zambito, Stefano; Zoch, Knut
Published inPhysical review letters, vol. 132, no. 8
Publication date2024
First online date2024-02-20
Abstract
Keywords
- P p: scattering
- P p: colliding beams
- Resonance: production
- New physics: search for
- Mass spectrum
- ATLAS
- Muon
- Photon
- Electron
- Machine learning
- Neural network
- Final state: ((n)jet lepton)
- Jet: bottom
- CERN LHC Coll
- Background
- Anomaly
- Channel cross section: upper limit
- Data analysis method
- Experimental results
- 13000 GeV-cms
- S015CS
Affiliation entities
Citation (ISO format)
ATLAS Collaboration. Search for New Phenomena in Two-Body Invariant Mass Distributions Using Unsupervised Machine Learning for Anomaly Detection at √s=13 TeV with the ATLAS Detector. In: Physical review letters, 2024, vol. 132, n° 8. doi: 10.1103/PhysRevLett.132.081801
Main files (1)
Article (Published version)
Identifiers
- PID : unige:193345
- DOI : 10.1103/PhysRevLett.132.081801
- PMID : 38457710
- arXiv : 2307.01612
Additional URL for this publicationhttps://link.aps.org/doi/10.1103/PhysRevLett.132.081801
Journal ISSN0031-9007
