Please use this identifier to cite or link to this item:
https://hdl.handle.net/11147/10480Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Karapınar, Güler | - |
| dc.contributor.author | CMS Collaboration | - |
| dc.date.accessioned | 2021-01-24T18:44:53Z | - |
| dc.date.available | 2021-01-24T18:44:53Z | - |
| dc.date.issued | 2020 | - |
| dc.identifier.issn | 1748-0221 | - |
| dc.identifier.uri | https://doi.org/10.1088/1748-0221/15/06/P06005 | - |
| dc.identifier.uri | https://hdl.handle.net/10480 | - |
| dc.description.abstract | 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. | en_US |
| dc.description.sponsorship | We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies: BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus); SENESCYT (Ecuador); MoER, ERC IUT, PUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); NKFIA (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS, RFBR, and NRC KI (Russia); MESTD (Serbia); SEIDI, CPAN, PCTI, and FEDER (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR, and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (U.S.A.).; Individuals have received support from the Marie-Curie program and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 752730, and 765710 (European Union); the Leventis Foundation; the A.P. Sloan Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Office; the Fonds pour la Formation a la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium); the F.R.S.-FNRS andFWO (Belgium) under the "Excellence of ScienceEOS"-be.h project n. 30820817; the Beijing Municipal Science & Technology Commission, No. Z191100007219010; the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy -EXC 2121 "Quantum Universe" -390833306; the Lendulet ("Momentum") Program and the Janos Bolyai Research Scholarship of the HungarianAcademy of Sciences, theNewNational Excellence Program UNKP, the NKFIA research grants 123842, 123959, 124845, 124850, 125105, 128713, 128786, and 129058 (Hungary); the Council of Science and Industrial Research, India; the HOMING PLUS program of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus program of the Ministry of Science and Higher Education, theNational Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2014/13/B/ST2/02543, 2014/15/B/ST2/03998, and 2015/19/B/ST2/02861, Sonata-bis 2012/07/E/ST2/01406; the National Priorities Research Program by Qatar National Research Fund; the Ministry of Science and Education, grant no. 14.W03.31.0026 (Russia); the Tomsk Polytechnic University Competitiveness Enhancement Program and "Nauka" Project FSWW-2020-0008 (Russia); the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia Maria de Maeztu, grant MDM-20150509 and the Programa Severo Ochoa del Principado de Asturias; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); the Kavli Foundation; the Nvidia Corporation; the SuperMicro Corporation; theWelch Foundation, contract C-1845; and theWeston Havens Foundation (U.S.A.). | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IOP Publishing Ltd. | en_US |
| dc.relation.ispartof | Journal of Instrumentation | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Large detector-systems performance | en_US |
| dc.subject | Pattern recognition, cluster finding, calibration and fitting methods | en_US |
| dc.title | Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques | en_US |
| dc.type | Article | en_US |
| dc.institutionauthor | Karapınar, Güler | - |
| dc.department | İzmir Institute of Technology. Mathematics | en_US |
| dc.identifier.volume | 15 | en_US |
| dc.identifier.issue | 6 | en_US |
| dc.identifier.wos | WOS:000545350900005 | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.identifier.doi | 10.1088/1748-0221/15/06/P06005 | - |
| dc.relation.doi | 10.1088/1748-0221/15/06/P06005 | en_US |
| dc.coverage.doi | 10.1088/1748-0221/15/06/P06005 | en_US |
| dc.identifier.wosquality | Q4 | - |
| dc.identifier.scopusquality | Q1 | - |
| dc.identifier.wosqualityttp | Top10% | en_US |
| item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
| item.cerifentitytype | Publications | - |
| item.fulltext | No Fulltext | - |
| item.languageiso639-1 | en | - |
| item.grantfulltext | none | - |
| item.openairetype | Article | - |
| crisitem.author.dept | 01. Izmir Institute of Technology | - |
| Appears in Collections: | Rectorate / Rektörlük WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection | |
CORE Recommender
SCOPUSTM
Citations
89
checked on Apr 5, 2024
WEB OF SCIENCETM
Citations
74
checked on Mar 30, 2024
Page view(s)
92
checked on Apr 15, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.