Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC
AffiliationUniv Arizona, Dept Phys
MetadataShow full item record
CitationAaboud, M., Aad, G., Abbott, B., Abdinov, O., Abeloos, B., Abhayasinghe, D. K., ... & Abreu, H. (2019). Performance of top-quark and W-boson tagging with ATLAS in Run 2 of the LHC. The European Physical Journal C, 79(5), 375.
JournalEUROPEAN PHYSICAL JOURNAL C
Rights© CERN for the benefit of the ATLAS collaboration 2019
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractThe 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.
NoteOpen access journal
VersionFinal published version
SponsorsANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW, Austria; FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq, Brazil; FAPESP, Brazil; NSERC, Canada; NRC, Canada; CFI, Canada; CERN; CONICYT, Chile; CAS, China; MOST, China; NSFC, China; COLCIENCIAS, Colombia; MSMT CR, Czech Republic; MPO CR, Czech Republic; VSC CR, Czech Republic; DNRF, Denmark; DNSRC, Denmark; IN2P3-CNRS, France; CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, Germany; HGF, Germany; MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, Israel; Benoziyo Center, Israel; INFN, Italy; MEXT, Japan; JSPS, Japan; CNRST, Morocco; NWO, Netherlands; RCN, Norway; MNiSW, Poland; NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia; NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS, Slovenia; MIZS, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC, Sweden; Wallenberg Foundation, Sweden; SERI, Switzerland; SNSF, Switzerland; Canton of Bern, Switzerland; Canton of Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE, United States of America; NSF, United States of America; BCKDF, Canada; CANARIE, Canada; CRC, Canada; Compute Canada, Canada; COST, European Union; ERC, European Union; ERDF, European Union; Horizon 2020, European Union; Marie Skodowska-Curie Actions, European Union; Investissements d' Avenir Labex and Idex, France; ANR, France; DFG, Germany; AvH Foundation, Germany; Herakleitos program; Thales program; Aristeia program - EU-ESF; Greek NSRF, Greece; BSF-NSF, Israel; GIF, Israel; CERCA Programme Generalitat de Catalunya, Spain; Royal Society, United Kingdom; Leverhulme Trust, United Kingdom