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

用于同时估计 b 射流能量和分辨率的深度神经网络。

• 发表时间: 2020
• 来源期刊: Comput Softw Big Sci
• DOI: 10.1007/s41781-020-00041-z
• PMID: 33196702
• 文章类型: Journal Article
• 作者列表: 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 CE,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 JL,Andrea J,Bloch 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S,

关键词: CMS; Deep learning; Higgs boson; Jet energy; Jet resolution; b jets

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 ¯ .

摘要

我们描述了一种方法，该方法可以获得在CERNLHC上以s=13TeV的能量在质子-质子碰撞中产生的b夸克产生的射流能量的点和色散估计值。该算法是在大量模拟b射流样本上进行训练的，并在CMS探测器2017年记录的数据上进行了验证，该数据对应于41fb-1的综合光度。基于深度前馈神经网络的多元回归算法利用射流成分和形状信息，以及与射流相关的重建次顶点的属性。该算法的结果用于提高分析的灵敏度，利用b射流在最终状态，如观察希格斯玻色子对bb的衰变。