Author
Tumasyan, A., Işıldak, Bora
Publication Date
2023-09-05
Publication Place
-
American Physical Society
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
2470-0010
Record ID
6db92a6e-451d-453a-b0ae-405b6a9002f8
Library Location
Natural and Mathematical Sciences
Date
2023-09-05
Notes
BMBWF
and FWF (Austria); FNRS and FWO (Belgium); CNPq,
CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES
and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC
(China); MINCIENCIAS (Colombia); MSES and CSF
(Croatia); RIF (Cyprus); SENESCYT (Ecuador); MoER,
ERC PUT and ERDF (Estonia); Academy of Finland,
MEC, and HIP (Finland); CEA and CNRS/IN2P3
(France); BMBF, DFG, and HGF (Germany); GSRI
(Greece); NKFIH (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);
MES and NSC (Poland); FCT (Portugal); MESTD
(Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri
Lanka); Swiss Funding Agencies (Switzerland); MST
(Taipei); MHESI and NSTDA (Thailand); TUBITAK
and TENMAK (Turkey); NASU (Ukraine); STFC
(United Kingdom); DOE and NSF (USA). Individuals
have received support from the Marie-Curie program
and the European Research Council and Horizon 2020
Grant, Contracts No. 675440, No. 724704, No. 752730,
No. 758316, No. 765710, No. 824093, No. 884104, and
COST Action CA16108 (European Union); the Leventis
Foundation; the Alfred 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 and
FWO (Belgium) under the “Excellence of Science–
EOS”—be.h Project No. 30820817; the Beijing
Municipal Science & Technology Commission,
No. Z191100007219010; the Ministry of Education,
Youth and Sports (MEYS) of the Czech Republic; the
Hellenic Foundation for Research and Innovation
(HFRI), Project No. 2288 (Greece); the Deutsche
Forschungsgemeinschaft (DFG), under Germany’s
Excellence Strategy—EXC 2121 “Quantum Universe”—
390833306, and under Project No. 400140256—
GRK2497; the Hungarian Academy of Sciences, the
New National Excellence Program—ÚNKP, the NKFIH
Research Grants No. K 124845, No. K 124850, No. K
128713, No. K 128786, No. K 129058, No. K 131991,
No. K 133046, No. K 138136, No. K 143460, No. K
143477, 2020-2.2.1-ED-2021-00181, and TKP2021-
NKTA-64 (Hungary); the Council of Science and
Industrial Research, India; the Latvian Council of
Science; the Ministry of Education and Science, Project
No. 2022/WK/14, and the National Science Center,
Contracts No. Opus 2021/41/B/ST2/01369 and
No. 2021/43/B/ST2/01552 (Poland); the Fundação para a
Ciência e a Tecnologia, Grant No. CEECIND/01334/2018
(Portugal); the National Priorities Research Program by
Qatar National Research Fund; MCIN/AEI/10.13039/
501100011033, ERDF “a way of making Europe,” and
the Programa Estatal de Fomento de la Investigación
Científica y T´ecnica de Excelencia María de Maeztu,
Grant No. MDM-2017-0765 and Programa Severo
Ochoa del Principado de Asturias (Spain); the
Chulalongkorn Academic into Its 2nd Century Project
Advancement Project, and the National Science,
Research and Innovation Fund via the Program
Management Unit for Human Resources & Institutional
Development, Research and Innovation, Grant
No. B05F650021 (Thailand); the Kavli Foundation;
the Nvidia Corporation; the SuperMicro Corporation; the
Welch Foundation, Contract No. C-1845; and the Weston
Havens Foundation (USA).
Sample Text
A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz-boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods typically used in high energy physics analyses. It uses minimally processed detector data as input and directly outputs particle properties of interest. The new technique is demonstrated for the reconstruction of the invariant mass of particles decaying in the CMS detector. The decay of a hypothetical scalar particle Formula Presented into two photons, Formula Presented, is chosen as a benchmark decay. Lorentz boosts Formula Presented are considered, ranging from regimes where both photons are resolved to those where the photons are closely merged as one object. A training method using domain continuation is introduced, enabling the invariant mass reconstruction of unresolved photon pairs in a novel way. The new technique is validated using Formula Presented decays in LHC collision data.
DOI
10.1103/PhysRevD.108.052002
Cilt
108
