Dijet resonance search with weak supervision using √s=13 TeV pp collisions in the ATLAS detector

0534147 - FZÚ 2021 RIV US eng J - Journal Article
Aad, G. - Abbott, B. - Abbott, D.C. - Chudoba, Jiří - Hejbal, Jiří - Hladík, Ondřej - Jačka, Petr - Jakoubek, Tomáš - Kepka, Oldřich - Kroll, Jiří - Kupčo, Alexander - Lokajíček, Miloš - Lysák, Roman - Marčišovský, Michal - Mikeštíková, Marcela - Němeček, Stanislav - Penc, Ondřej - Šícho, Petr - Staroba, Pavel - Svatoš, Michal - Taševský, Marek … Total 2941 authors
Dijet resonance search with weak supervision using √s=13 TeV pp collisions in the ATLAS detector.
Physical Review Letters. Roč. 125, č. 13 (2020), s. 1-23, č. článku 131801. ISSN 0031-9007. E-ISSN 1079-7114
R&D Projects: GA MŠMT(CZ) LTT17018
Research Infrastructure: CERN-CZ II - 90104
Institutional support: RVO:68378271
Keywords : ATLAS * CERN * LHC * jet * production
OECD category: Particles and field physics
Impact factor: 9.161, year: 2020
Method of publishing: Open access

This Letter describes a search for narrowly resonant new physics using a machine-learning anomaly detection procedure that does not rely on signal simulations for developing the analysis selection. Weakly supervised learning is used to train classifiers directly on data to enhance potential signals. The targeted topology is dijet events and the features used for machine learning are the masses of the two jets. The resulting analysis is essentially a three-dimensional search A→BC, for mA∼O(TeV), mB,mC∼O(100  GeV) and B, C are reconstructed as large-radius jets, without paying a penalty associated with a large trials factor in the scan of the masses of the two jets. The full run 2 √s=13  TeV pp collision dataset of 139  fb-1 recorded by the ATLAS detector at the Large Hadron Collider is used for the search.
Permanent Link: http://hdl.handle.net/11104/0312376