Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC
- NázevTitle
- Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHCPerformance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC
- Druh výsledkuResult type
- Článek v časopiseJournal article
- AutořiAuthors
- M. Aaboud, G. Aad, B. Abbott, O. Abdinov, B. Ali, K. Augsten, D. Caforio, P. Gallus, M. Havránek, Z. Hubáček, M. Myška, R. Novotný, S. Pospíšil, T. Slavíček, K. Smolek, M. Solar, A. Sopczak, M. Suk, V. Vacek, P. Vokáč, V. Vrba
- Klíčová slovaKeywords
- top quark, W boson, Standard Model
- ExperimentCollaboration
- ATLAS
- DOIDOI
- 10.1140/epjc/s10052-019-6847-8
- Časopis / citaceJournal / citation
- The European Physical Journal C 79(5), 375 (2019) · ISSN 1434-6044
- RokYear
- 2019
- JazykLanguage
- eng
- ZáznamyRecords
- ProjektProject
- Získávání nových poznatků o mikrosvětě v infrastruktuře CERNAcquiring new pieces of knowledge about micro-world in CERN research infrastructure; Centrum pokročilých aplikovaných přírodních vědCenter for advanced applied sciences
- CitovánoCited by
- 220 (INSPIRE-HEP)
- Plný text (open access)Full text (open access)
- https://link.springer.com/content/pdf/10.1140/epjc/s10052-019-6847-8.pdf
- Citace ke staženíDownload citation
- TXT · BibTeX
AbstraktAbstract
The performance of identification algorithms (taggers) for hadronically decaying top quarks and W bosons in pp collisions at = 13TeV recorded by the ATLAS experiment at the Large Hadron Collider is presented. A set of techniques based on jet shape observables are studied to determine a set of optimal cut-based taggers for use in physics analyses. The studies are extended to assess the utility of combinations of substructure observables as a multivariate tagger using boosted decision trees or deep neural networks in comparison with taggers based on two-variable combinations. In addition, for highly boosted top-quark tagging, a deep neural network based on jet constituent inputs as well as a re-optimisation of the shower deconstruction technique is presented. The performance of these taggers is studied in data collected during 2015 and 2016 corresponding to 36.1fb-1 for the tt and +jet and 36.7-1 for the dijet event topologies.
The performance of identification algorithms (taggers) for hadronically decaying top quarks and W bosons in pp collisions at = 13TeV recorded by the ATLAS experiment at the Large Hadron Collider is presented. A set of techniques based on jet shape observables are studied to determine a set of optimal cut-based taggers for use in physics analyses. The studies are extended to assess the utility of combinations of substructure observables as a multivariate tagger using boosted decision trees or deep neural networks in comparison with taggers based on two-variable combinations. In addition, for highly boosted top-quark tagging, a deep neural network based on jet constituent inputs as well as a re-optimisation of the shower deconstruction technique is presented. The performance of these taggers is studied in data collected during 2015 and 2016 corresponding to 36.1fb-1 for the tt and +jet and 36.7-1 for the dijet event topologies.