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A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution.

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Sirunyan2020_Article_ADeepNeuralNetworkForSimultane.pdfPublished version1.46 MBAdobe PDFView/Open
Title: A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution.
Authors: Sirunyan, AM
Tumasyan, A
Adam, W
Ambrogi, F
Bergauer, T
Dragicevic, M
Erö, J
Valle, AED
Flechl, M
Frühwirth, R
Jeitler, M
Krammer, N
Krätschmer, I
Liko, D
Madlener, T
Mikulec, I
Rad, N
Schieck, J
Schöfbeck, R
Spanring, M
Spitzbart, D
Waltenberger, W
Wulz, C-E
Zarucki, M
Drugakov, V
Mossolov, V
Gonzalez, JS
Darwish, MR
De Wolf, EA
Croce, DD
Janssen, X
Lelek, A
Pieters, M
Sfar, HR
Haevermaet, HV
Mechelen, PV
Putte, SV
Remortel, NV
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
Doninck, WV
Mulders, PV
Beghin, D
Bilin, B
Clerbaux, B
De Lentdecker, G
Delannoy, H
Dorney, B
Favart, L
Grebenyuk, A
Kalsi, AK
Popov, A
Postiau, N
Starling, E
Thomas, L
Velde, CV
Vanlaer, P
Vannerom, D
Cornelis, T
Dobur, D
Khvastunov, I
Niedziela, M
Roskas, C
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
Silva, GC
Hensel, C
Moraes, A
Teles, PR
Chagas, EBBD
Carvalho, W
Chinellato, J
Coelho, E
Da Costa, EM
Da Silveira, GG
De Jesus Damiao, D
De Oliveira Martins, C
De Souza, SF
Guativa, LMH
Malbouisson, H
Martins, J
Figueiredo, DM
Jaime, MM
De Almeida, MM
Herrera, CM
Mundim, L
Nogima, H
Da Silva, WLP
Rosas, LJS
Santoro, A
Sznajder, A
Thiel, M
Manganote, EJT
Da Silva De Araujo, FT
Pereira, AV
Bernardes, CA
Calligaris, L
Tomei, TRFP
Gregores, EM
Lemos, DS
Mercadante, PG
Novaes, SF
Padula, S
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
Fang, W
Gao, X
Yuan, L
Ahmad, M
Hu, Z
Wang, Y
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
Mao, Y
Qian, SJ
Wang, D
Wang, Q
Xiao, M
Avila, C
Cabrera, A
Florez, C
Hernández, CFG
Delgado, MAS
Guisao, JM
Alvarez, JDR
González, CAS
Arbelaez, NV
Giljanović, 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
Finger, M
Kveton, A
Tomsa, J
Ayala, E
Jarrin, EC
Abdalla, H
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
Heikkilä, JK
Karimäki, V
Kim, MS
Kinnunen, R
Lampén, T
Lassila-Perini, K
Laurila, S
Lehti, S
Lindén, T
Luukka, P
Mäenpää, 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
Lenzi, B
Locci, E
Malcles, J
Rander, J
Rosowsky, A
Sahin, MÖ
Savoy-Navarro, A
Titov, M
Yu, GB
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
Gelé, D
Goerlach, U
Jansová, M
Bihan, A-CL
Tonon, N
Hove, PV
Gadrat, S
Beauceron, S
Bernet, C
Boudoul, G
Camen, C
Carle, A
Chanon, N
Chierici, R
Contardo, D
Depasse, P
Mamouni, HE
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
Donckt, MV
Viret, S
Khvedelidze, A
Tsamalaidze, Z
Autermann, C
Feld, L
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
Flügge, G
Ahmad, WH
Hlushchenko, O
Kress, T
Müller, T
Nowack, A
Pistone, C
Pooth, O
Roy, D
Sert, H
Stahl, A
Martin, MA
Asmuss, P
Babounikau, I
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Beernaert, K
Behnke, O
Martínez, AB
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Anuar, AAB
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Rodríguez, SC
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Jomhari, NZ
Jung, H
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Kasemann, M
Kaveh, H
Keaveney, J
Kleinwort, C
Knolle, J
Krücker, D
Lange, W
Lenz, T
Lidrych, J
Lipka, K
Lohmann, W
Mankel, R
Melzer-Pellmann, I-A
Meyer, AB
Meyer, M
Missiroli, M
Mnich, J
Mussgiller, A
Myronenko, V
Adán, DP
Pflitsch, SK
Pitzl, D
Raspereza, A
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Savitskyi, M
Scheurer, V
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Shevchenko, R
Singh, A
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Turkot, O
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CMS Collaboration
Item Type: Journal Article
Abstract: We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of s = 13 TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb - 1 . A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to b b ¯ .
Issue Date: 2020
Date of Acceptance: 20-Jun-2020
URI: http://hdl.handle.net/10044/1/84282
DOI: 10.1007/s41781-020-00041-z
Start Page: 10
Journal / Book Title: Comput Softw Big Sci
Volume: 4
Issue: 1
Copyright Statement: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: CMS
Deep learning
Higgs boson
Jet energy
Jet resolution
b jets
CMS Collaboration
physics.data-an
physics.data-an
hep-ex
Publication Status: Published
Open Access location: https://link.springer.com/article/10.1007/s41781-020-00041-z
Appears in Collections:Physics
High Energy Physics



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