Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/21256
Title: Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
Authors: Sirunyan, AM
Tumasyan, A
Adam, W
Ambrogi, F
Bergauer, T
Dragicevic, M
Erö, J
Valle, AED
Flechl, M
Frühwirth, R
Jeitler, M
Wilson, G
Montagna, P
Ratti, SP
Re, V
Saggio, A
Ressegotti, M
Riccardi, C
Salvini, P
Vai, I
Vitulo, P
Oh, M
Duric, S
Dremin, I
Biasini, M
Bilei, GM
Ciangottini, D
Fanò, L
Lariccia, P
Marono, MV
Leonardi, R
Manoni, E
Mantovani, G
Ivanov, A
Mariani, V
Kirakosyan, M
Menichelli, M
Rossi, A
Santocchia, A
Spiga, D
Androsov, K
Azzurri, P
Vischia, P
Bagliesi, G
Kaadze, K
Bertacchi, V
Bianchini, L
Terkulov, A
Boccali, T
Castaldi, R
Ciocci, MA
Dell'Orso, R
Donato, S
Fedi, G
Giannini, L
Kim, D
Zobec, J
Giassi, A
Grippo, MT
Belyaev, A
Ligabue, F
Manca, E
Mandorli, G
Messineo, A
Palla, F
Rizzi, A
Maravin, Y
Rolandi, G
Chowdhury, SR
Alves, FL
Scribano, A
Boos, E
Spagnolo, P
Tenchini, R
Tonelli, G
Turini, N
Venturi, A
Mendis, DR
Verdini, PG
Cavallari, F
Cipriani, M
Re, DD
Alves, GA
Dubinin, M
Marco, ED
Diemoz, M
Longo, E
Meridiani, P
Starodumov, A
Organtini, G
Pandolfi, F
Paramatti, R
Quaranta, C
Rahatlou, S
Rovelli, C
Dudko, L
Silva, GC
Santanastasio, F
Soffi, L
Mitchell, T
Amapane, N
Arcidiacono, R
Argiro, S
Arneodo, M
Bartosik, N
Bellan, R
Bellora, A
Ershov, A
Biino, C
Hensel, C
Modak, A
Cappati, A
Cartiglia, N
Cometti, S
Costa, M
Covarelli, R
Demaria, N
Kiani, B
Legger, F
Gribushin, A
Mariotti, C
Krolikowski, J
Gallinaro, M
Maselli, S
Moraes, A
Migliore, E
Monaco, V
Monteil, E
Monteno, M
Obertino, MM
Ortona, G
Pacher, L
Sznajder, A
Mohammadi, A
Pastrone, N
Pelliccioni, M
Angioni, GLP
Teles, PR
Romero, A
Ruspa, M
Salvatico, R
Sola, V
Solano, A
Soldi, D
Rebassoo, F
Oh, SB
Klyukhin, V
Staiano, A
Trocino, D
Belforte, S
Candelise, V
Chagas, EBBD
Casarsa, M
Cossutti, F
Rold, AD
Wright, D
Ricca, GD
Vazzoler, F
Kodolova, O
Zanetti, A
Kim, B
Kim, DH
Kim, GN
Lee, J
Carvalho, W
Lee, SW
Baden, A
Moon, CS
Oh, YD
Pak, SI
Lokhtin, I
Sekmen, S
Son, DC
Yang, YC
Kim, H
Moon, DH
Oh, G
Baron, O
Chinellato, J
Francois, B
Kim, TJ
Park, J
Obraztsov, S
Cho, S
Choi, S
Go, Y
Ha, S
Hong, B
Belloni, A
Lee, K
Lee, KS
Coelho, E
Lim, J
Park, J
Petrushanko, S
Park, SK
Roh, Y
Yoo, J
Goh, J
Eno, SC
Kim, HS
Almond, J
Bhyun, JH
Choi, J
Savrin, V
Snigirev, A
Barnyakov, A
Blinov, V
Dimova, T
Radburn-Smith, BC
Feng, Y
Thiel, M
Kardapoltsev, L
Skovpen, Y
Azhgirey, I
Bayshev, I
Bitioukov, S
Kachanov, V
Konstantinov, D
Mandrik, P
Petrov, V
Susa, T
Silveira, GGD
Ryutin, R
Manganote, EJT
Slabospitskii, S
Sobol, A
Troshin, S
Tyurin, N
Uzunian, A
Volkov, A
Babaev, A
Saunders, M
Hadley, NJ
Iuzhakov, A
Yang, UK
Okhotnikov, V
Borchsh, V
Krätschmer, I
Araujo, FTDSD
Ivanchenko, V
Tcherniaev, E
Adzic, P
Cirkovic, P
Costa, EMD
Dordevic, M
Milenovic, P
Yoo, HD
Milosevic, J
Stojanovic, M
Aguilar-Benitez, M
Maestre, JA
Pereira, AV
Fernández, AÁ
Bachiller, I
Jaime, MM
Luna, MB
Bedoya, C
Cifuentes, JAB
Yoon, I
Montoya, CAC
Cepeda, M
Cerrada, M
Colino, N
Cruz, BDL
Bernardes, CA
Jabeen, S
Peris, AD
Ramos, JPF
Flix, J
Fouz, MC
Jeon, D
Lopez, OG
Lopez, SG
Hernandez, JM
Josa, MI
Moran, D
Jeng, GY
Tobar, ÁN
Calligaris, L
Yzquierdo, AP-C
Pelayo, JP
Redondo, I
Kim, JH
Romero, L
Navas, SS
Soares, MS
Triossi, A
Kellogg, RG
Willmott, C
Albajar, C
de Trocóniz, JF
Tomei, TRFP
Reyes-Almanza, R
Gonzalez, BA
Lee, JSH
Cuevas, J
Erice, C
Menendez, JF
Mignerey, AC
Folgueras, S
Caballero, IG
Fernández, JRG
Cortezon, EP
Bouza, VR
Gregores, EM
Cruz, SS
Park, IC
Cabrillo, IJ
Calderon, A
Nabili, S
Quero, BC
Campderros, JD
Fernandez, M
Manteca, PJF
Alonso, AG
Gomez, G
Rivero, CM
Lemos, DS
Choi, Y
Arbol, PMRD
Ricci-Tam, F
Matorras, F
Gomez, JP
Prieels, C
Rodrigo, T
Ruiz-Jimeno, A
Russo, L
Scodellaro, L
Vila, I
Garcia, JMV
Hwang, C
Seidel, M
Mercadante, PG
Sonnadara, DUJ
Dharmaratna, WGD
Wickramage, N
Abbaneo, D
Akgun, B
Auffray, E
Auzinger, G
Baechler, J
Baillon, P
Yumiceva, F
Shin, YH
Jeong, Y
Ball, AH
Novaes, SF
Barney, D
Bendavid, J
Bianco, M
Bocci, A
Bortignon, P
Bossini, E
Brondolin, E
Skuja, A
Camporesi, T
Damiao, DDJ
Caratelli, A
Cerminara, G
Padula, S
Chapon, E
Cucciati, G
d'Enterria, D
Dabrowski, A
Daci, N
Ather, MW
Daponte, V
David, A
Lee, J
Davignon, O
Roeck, AD
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Liko, D
Aleksandrov, A
Dobson, M
Dünser, M
Hollar, J
Dupont, N
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Lee, Y
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Fiorendi, S
Franzoni, G
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Malgeri, L
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Menasce, D
Monti, F
Moroni, L
Schmitz, E
Paganoni, M
Pedrini, D
Delaere, C
Parygin, P
Ragazzi, S
de Fatis, TT
Valsecchi, D
Zuolo, D
Buontempo, S
Cavallo, N
Roguljic, M
Iorio, AD
Crescenzo, AD
Fabozzi, F
Fienga, F
Popova, E
Delcourt, M
Galati, G
Iorio, AOM
Lista, L
Meola, S
Takaki, JDT
Paolucci, P
Rossi, B
Sciacca, C
Voevodina, E
Azzi, P
Rusinov, V
Bacchetta, N
Giammanco, A
Bisello, D
Boletti, A
Wang, Q
Bragagnolo, A
Carlin, R
Checchia, P
Manzano, PDC
Dorigo, T
Dosselli, U
Andreev, V
Gasparini, F
Gasparini, U
Lemaitre, V
Williams, J
Gozzelino, A
Hoh, SY
Lujan, P
Margoni, M
Meneguzzo, AT
Pazzini, J
Presilla, M
Santoro, A
Ronchese, P
Rossin, R
Galinhas, B
Simonetto, F
Prisciandaro, J
Tiko, A
Tosi, M
Zanetti, M
Zotto, P
Zumerle, G
Braghieri, A
Azarkin, M
Fiorina, D
Keywords: Large detector-systems performance;Pattern recognition;cluster finding;calibration and fitting methods
Issue Date: 3-Jun-2020
Publisher: IOP Publishing
Citation: Journal of Instrumentation, 15 (06), pp. P06005 - P06005
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 √s = 13TeV, 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.
URI: http://bura.brunel.ac.uk/handle/2438/21256
DOI: http://dx.doi.org/10.1088/1748-0221/15/06/p06005
ISSN: 1748-0221
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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