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Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

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Sirunyan_2020_J._Inst._15_P06005.pdfPublished version5.48 MBAdobe PDFView/Open
Title: Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
Authors: Sirunyan, AM
Tumasyan, A
Adam, W
Ambrogi, F
Bergauer, T
Brandstetter, J
Dragicevic, M
Eroe, J
Del Valle, AE
Flechl, M
Fruehwirth, R
Jeitler, M
Krammer, N
Kraetschmer, I
Liko, D
Madlener, T
Mikulec, I
Rad, N
Schieck, J
Schoefbeck, R
Spanring, M
Spitzbart, D
Waltenberger, W
Wulz, C-E
Zarucki, M
Drugakov, V
Mossolov, V
Gonzalez, JS
Darwish, MR
De Wolf, EA
Di Croce, D
Janssen, X
Lelek, A
Pieters, M
Sfar, HR
Van Haevermaet, H
Van Mechelen, P
Van Putte, S
Van Remortel, N
Blekman, F
Bols, ES
Chhibra, SS
D'Hondt, J
De Clercq, J
Lontkovskyi, D
Lowette, S
Marchesini, I
Moortgat, S
Python, Q
Skovpen, K
Tavernier, S
Van Doninck, W
Van Mulders, P
Burns, D
Beghin, D
Bilin, B
Brun, H
Clerbaux, B
De Lentdecker, G
Delannoy, H
Dorney, B
Favart, L
Grebenyuk, A
Kalsi, AK
Popov, A
Postiau, N
Starling, E
Thomas, L
Vander Velde, C
Vanlaer, P
Vannerom, D
Fang, W
Gao, X
Cornelis, T
Dobur, D
Khvastunov, I
Niedziela, M
Roskas, C
Trocino, D
Tytgat, M
Verbeke, W
Vermassen, B
Vit, M
Bondu, O
Bruno, G
Caputo, C
David, P
Delaere, C
Delcourt, M
Giammanco, A
Lemaitre, V
Prisciandaro, J
Saggio, A
Marono, MV
Vischia, P
Zobec, J
Alves, FL
Alves, GA
Correia Silva, G
Hensel, C
Moraes, A
Rebello Teles, P
Belchior Batista Das Chagas, E
Carvalho, W
Chinellato, J
Coelho, E
Da Costa, EM
Da Silveira, GG
De Jesus Damiao, D
De Oliveira Martins, C
Fonseca De Souza, S
Huertas Guativa, LM
Malbouisson, H
Martins, J
Matos Figueiredo, D
Medina Jaime, M
Melo De Almeida, M
Mora Herrera, C
Mundim, L
Nogima, H
Prado Da Silva, WL
Sanchez Rosas, LJ
Santoro, A
Sznajder, A
Thiel, M
Tonelli Manganote, EJ
Torres Da Silva De Araujo, F
Vilela Pereira, A
Bernardes, CA
Calligaris, L
Fernandez Perez Tomei, TR
Novaes, SF
Padula, S
Gregores, EM
Lemos, DS
Mercadante, PG
Aleksandrov, A
Antchev, G
Hadjiiska, R
Iaydjiev, P
Misheva, M
Rodozov, M
Shopova, M
Sultanov, G
Bonchev, M
Dimitrov, A
Ivanov, T
Litov, L
Pavlov, B
Petkov, P
Yuan, L
Ahmad, M
Hu, Z
Wang, Y
Dozen, C
Chen, GM
Chen, HS
Chen, M
Jiang, CH
Leggat, D
Liao, H
Liu, Z
Spiezia, A
Tao, J
Yazgan, E
Zhang, H
Zhang, S
Zhao, J
Agapitos, A
Ban, Y
Chen, G
Levin, A
Li, J
Li, L
Li, Q
Lu, M
Qian, SJ
Wang, D
Wang, Q
Xiao, M
Avila, C
Cabrera, A
Florez, C
Gonzalez Hernandez, CF
Segura Delgado, MA
Mejia Guisao, J
Ruiz Alvarez, JD
Salazar Gonzalez, CA
Vanegas Arbelaez, N
Giljanovic, D
Godinovic, N
Lelas, D
Puljak, I
Sculac, T
Antunovic, Z
Kovac, M
Brigljevic, V
Ferencek, D
Kadija, K
Mesic, B
Roguljic, M
Starodumov, A
Susa, T
Ather, MW
Attikis, A
Erodotou, E
Ioannou, A
Kolosova, M
Konstantinou, S
Mavromanolakis, G
Mousa, J
Nicolaou, C
Ptochos, F
Razis, PA
Rykaczewski, H
Tsiakkouri, D
Finger, M
Fingera, M
Kveton, A
Tomsa, J
Ayala, E
Carrera Jarrin, E
Assran, Y
Elgammal, S
Bhowmik, S
De Oliveira, ACA
Dewanjee, RK
Ehataht, K
Kadastik, M
Raidal, M
Veelken, C
Eerola, P
Forthomme, L
Kirschenmann, H
Osterberg, K
Voutilainen, M
Garcia, F
Havukainen, J
Heikkila, JK
Karimaki, V
Kim, MS
Kinnunen, R
Lampen, T
Lassila-Perini, K
Laurila, S
Lehti, S
Linden, T
Luukka, P
Maenpaa, T
Siikonen, H
Tuominen, E
Tuominiemi, J
Tuuva, T
Besancon, M
Couderc, F
Dejardin, M
Denegri, D
Fabbro, B
Faure, JL
Ferri, F
Ganjour, S
Givernaud, A
Gras, P
De Monchenault, GH
Jarry, P
Leloup, C
Locci, E
Malcles, J
Rander, J
Rosowsky, A
Savoy-Navarro, A
Titov, M
Ahuja, S
Amendola, C
Beaudette, F
Busson, P
Charlot, C
Diab, B
Falmagne, G
De Cassagnac, RG
Kucher, I
Lobanov, A
Perez, CM
Nguyen, M
Ochando, C
Paganini, P
Rembser, J
Salerno, R
Sauvan, JB
Sirois, Y
Zabi, A
Zghiche, A
Agram, J-L
Andrea, J
Bloch, D
Bourgatte, G
Brom, J-M
Chabert, EC
Collard, C
Conte, E
Fontaine, J-C
Gele, D
Goerlach, U
Jansova, M
Le Bihan, A-C
Tonon, N
Van Hove, P
Gadrat, S
Beauceron, S
Bernet, C
Boudoul, G
Camen, C
Carle, A
Chanon, N
Chierici, R
Contardo, D
Depasse, P
El Mamouni, H
Fay, J
Gascon, S
Gouzevitch, M
Ille, B
Jain, S
Lagarde, F
Laktineh, IB
Lattaud, H
Lesauvage, A
Lethuillier, M
Mirabito, L
Perries, S
Sordini, V
Torterotot, L
Touquet, G
Vander Donckt, M
Viret, S
Toriashvili, T
Mestvirishvili, A
Bagaturia, I
Autermann, C
Feld, L
Kiesel, MK
Klein, K
Lipinski, M
Meuser, D
Pauls, A
Preuten, M
Rauch, MP
Schulz, J
Teroerde, M
Wittmer, B
Erdmann, M
Fischer, B
Ghosh, S
Hebbeker, T
Hoepfner, K
Keller, H
Mastrolorenzo, L
Merschmeyer, M
Meyer, A
Millet, P
Mocellin, G
Mondal, S
Mukherjee, S
Noll, D
Novak, A
Pook, T
Pozdnyakov, A
Quast, T
Radziej, M
Rath, Y
Reithler, H
Roemer, J
Schmidt, A
Schuler, SC
Sharma, A
Wiedenbeck, S
Zaleski, S
Borras, K
Kasem, A
Coubez, X
Flgge, G
Ahmad, WH
Hlushchenko, O
Kress, T
Mller, T
Nowack, A
Pistone, C
Pooth, O
Roy, D
Sert, H
Stahl, A
Martin, MA
Asmuss, P
Babounikau, I
Bakhshiansohi, H
Beernaert, K
Behnke, O
Martinez, AB
Bertsche, D
Bin Anuar, AA
Botta, V
Campbell, A
Cardini, A
Connor, P
Rodriguez, SC
Contreras-Campana, C
Danilov, V
De Wit, A
Defranchis, MM
Pardos, CD
Damiani, DD
Eckerlin, G
Eckstein, D
Eichhorn, T
Elwood, A
Eren, E
Gallo, E
Geiser, A
Grohsjean, A
Guthoff, M
Haranko, M
Harb, A
Jafari, A
Jomhari, NZ
Jung, H
Kasemann, M
Kaveh, H
Keaveney, J
Kleinwort, C
Knolle, J
Kruecker, D
Lange, W
Lenz, T
Lidrych, J
Lipka, K
Lohmann, W
Mankel, R
Melzer-Pellmann, I-A
Meyer, AB
Meyer, M
Missiroli, M
Mittag, G
Mnich, J
Mussgiller, A
Myronenko, V
Adan, DP
Pflitsch, SK
Pitzl, D
Raspereza, A
Saibel, A
Savitskyi, M
Scheurer, V
Schtze, P
Schwanenberger, C
Shevchenko, R
Singh, A
Tholen, H
Turkot, O
Vagnerini, A
Van De Klundert, M
Walsh, R
Wen, Y
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Wissing, C
Zenaiev, O
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Ebrahimi, A
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Gonzalez, D
Gunnellini, P
Haller, J
Hinzmann, A
Karavdina, A
Kasieczka, G
Klanner, R
Kogler, R
Kovalchuk, N
Kurz, S
Kutzner, V
Lange, J
Lange, T
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Niemeyer, CEN
Perieanu, A
Reimers, A
Rieger, O
Scharf, C
Schleper, P
Schumann, S
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Stadie, H
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Zhokin, A
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Item Type: Journal Article
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.
Issue Date: 1-Jun-2020
Date of Acceptance: 25-Apr-2020
URI: http://hdl.handle.net/10044/1/80869
DOI: 10.1088/1748-0221/15/06/P06005
ISSN: 1748-0221
Publisher: IOP Publishing
Start Page: 1
End Page: 87
Journal / Book Title: Journal of Instrumentation
Volume: 15
Issue: 6
Copyright Statement: © 2020 CERN. Published by IOP Publishing Ltd on behalf of Sissa Medialab. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (http://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Keywords: Science & Technology
Technology
Instruments & Instrumentation
Large detector-systems performance
Pattern recognition, cluster finding, calibration and fitting methods
ANNIHILATION
ALGORITHMS
Science & Technology
Technology
Instruments & Instrumentation
Large detector-systems performance
Pattern recognition, cluster finding, calibration and fitting methods
ANNIHILATION
ALGORITHMS
hep-ex
hep-ex
physics.ins-det
Nuclear & Particles Physics
02 Physical Sciences
09 Engineering
Publication Status: Published
Open Access location: https://iopscience.iop.org/article/10.1088/1748-0221/15/06/P06005
Article Number: ARTN P06005
Online Publication Date: 2020-06-03
Appears in Collections:Physics
High Energy Physics