Journal
JOURNAL OF INSTRUMENTATION
Volume 15, Issue 6, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1748-0221/15/06/P06005
Keywords
Large detector-systems performance; Pattern recognition, cluster finding, calibration and fitting methods
Categories
Funding
- BMBWF (Austria)
- FWF (Austria)
- FNRS (Belgium)
- FWO (Belgium)
- CNPq (Brazil)
- CAPES (Brazil)
- FAPERJ (Brazil)
- FAPERGS (Brazil)
- FAPESP (Brazil)
- MES (Bulgaria)
- CERN
- CAS (China)
- MoST (China)
- NSFC (China)
- COLCIENCIAS (Colombia)
- MSES (Croatia)
- CSF (Croatia)
- RPF (Cyprus)
- SENESCYT (Ecuador)
- MoER (Estonia)
- ERC IUT (Estonia)
- PUT (Estonia)
- ERDF (Estonia)
- Academy of Finland (Finland)
- MEC (Finland)
- HIP (Finland)
- CEA (France)
- CNRS/IN2P3 (France)
- BMBF (Germany)
- DFG (Germany)
- HGF (Germany)
- GSRT (Greece)
- NKFIA (Hungary)
- DAE (India)
- DST (India)
- IPM (Iran)
- SFI (Ireland)
- INFN (Italy)
- MSIP (Republic of Korea)
- NRF (Republic of Korea)
- MES (Latvia)
- LAS (Lithuania)
- MOE (Malaysia)
- UM (Malaysia)
- BUAP (Mexico)
- CINVESTAV (Mexico)
- CONACYT (Mexico)
- LNS (Mexico)
- SEP (Mexico)
- UASLP-FAI (Mexico)
- MOS (Montenegro)
- MBIE (New Zealand)
- PAEC (Pakistan)
- MSHE (Poland)
- NSC (Poland)
- FCT (Portugal)
- JINR (Dubna)
- MON (Russia)
- RosAtom (Russia)
- RAS (Russia)
- RFBR (Russia)
- NRC KI (Russia)
- MESTD (Serbia)
- SEIDI (Spain)
- CPAN (Spain)
- PCTI (Spain)
- FEDER (Spain)
- MOSTR (Sri Lanka)
- Swiss Funding Agencies (Switzerland)
- MST (Taipei)
- ThEPCenter (Thailand)
- IPST (Thailand)
- STAR (Thailand)
- NSTDA (Thailand)
- TUBITAK (Turkey)
- TAEK (Turkey)
- NASU (Ukraine)
- STFC (United Kingdom)
- DOE (U.S.A.)
- NSF (U.S.A.)
- Marie-Curie program
- European Research Council (European Union) [675440, 752730, 765710]
- Horizon 2020 (European Union) [675440, 752730, 765710]
- Leventis Foundation
- A.P. Sloan Foundation
- Alexander von Humboldt Foundation
- Belgian Federal Science Policy Office
- Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium)
- Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)
- F.R.S.-FNRS (Belgium) under the Excellence of ScienceEOS-be.h project [30820817]
- FWO (Belgium) under the Excellence of ScienceEOS-be.h project [30820817]
- Beijing Municipal Science & Technology Commission [Z191100007219010]
- Ministry of Education, Youth and Sports (MEYS) of the Czech Republic
- Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy [EXC 2121, 390833306]
- Lendulet (Momentum) Program of the Hungarian Academy of Sciences (Hungary)
- Janos Bolyai Research Scholarship of the HungarianAcademy of Sciences (Hungary)
- NewNational Excellence Program UNKP (Hungary)
- NKFIA (Hungary) [123842, 123959, 124845, 124850, 125105, 128713, 128786, 129058]
- Council of Science and Industrial Research, India
- HOMING PLUS program of the Foundation for Polish Science
- European Union, Regional Development Fund
- Mobility Plus program of the Ministry of Science and Higher Education
- National Science Center (Poland) [Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406]
- National Priorities Research Program by Qatar National Research Fund
- Ministry of Science and Education (Russia) [14.W03.31.0026]
- Tomsk Polytechnic University Competitiveness Enhancement Program (Russia)
- Nauka Project (Russia) [FSWW-2020-0008]
- Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu [MDM-20150509]
- Programa Severo Ochoa del Principado de Asturias
- Thalis program - EU-ESF
- Aristeia program - EU-ESF
- Greek NSRF
- Rachadapisek Sompot Fund
- Chulalongkorn University (Thailand)
- Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand)
- Kavli Foundation
- Nvidia Corporation
- SuperMicro Corporation
- Welch Foundation [C-1845]
- Weston Havens Foundation (U.S.A.)
- Science and Technology Facilities Council [ST/K003542/1, ST/L005603/1, ST/K001639/1, ST/N001273/1, ST/M004775/1] Funding Source: researchfish
- STFC [ST/S00078X/1, ST/N001273/1, ST/K003542/1, ST/M004775/1, ST/L005603/1, ST/S000739/1, ST/K001639/1] Funding Source: UKRI
Ask authors/readers for more resources
Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at root S = 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1). Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available