@article{10902/32178,
year = {2023},
month = {9},
url = {https://hdl.handle.net/10902/32178},
abstract = {A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentzboosted 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 A into two photons, A → γγ, is chosen as a benchmark decay. Lorentz boosts
γL ¼ 60–600 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 π0 → γγ decays in LHC collision data.},
organization = {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 à 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 Técnica 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).},
publisher = {American Physical Society},
publisher = {Physical Review D, 2023, 108(5), 052002},
title = {Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector},
author = {Tumasyan, A. and Brochero Cifuentes, Javier Andrés and Cabrillo Bartolomé, José Iban and Calderón Tazón, Alicia and Duarte Campderros, Jorge and Fernández García, Marcos and Fernández Madrazo, Celia and Fernández Manteca, Pedro José and García Alonso, Andrea and Gómez Gramuglio, Gervasio and Martínez Rivero, Celso and Martínez Ruiz del Árbol, Pablo and Matorras Weinig, Francisco and Matorras Cuevas, Pablo and Piedra Gómez, Jonatan and Prieëls, Cedric and Ruiz Jimeno, Alberto and Scodellaro, Luca and Vila Álvarez, Iván  and Vizán García, Jesús Manuel},
}