Evaluating EAS Directions from TAIGA HiSCORE Data Using Fully Connected Neural Networks

被引:0
|
作者
A. P. Kryukov [1 ]
S. P. Polyakov [1 ]
Yu. Yu. Dubenskaya [1 ]
E. O. Gres [1 ]
E. B. Postnikov [2 ]
P. A. Volchugov [1 ]
D. P. Zhurov [1 ]
机构
[1] Skobeltsyn Institute of Nuclear Physics,
[2] Lomonosov Moscow State University,undefined
[3] Research Institute of Applied Physics,undefined
[4] Irkutsk State University,undefined
关键词
extensive area shower; EAS direction; Cherenkov detector; machine learning; artificial neural network; multilayer perceptron; skip connections;
D O I
10.3103/S0027134924702199
中图分类号
学科分类号
摘要
引用
收藏
页码:S724 / S730
相关论文
共 50 条
  • [1] Application of Convolutional Neural Networks for Data Analysis in TAIGA-HiSCORE Experiment
    A. P. Kryukov
    A. A. Vlaskina
    S. P. Polyakov
    E. O. Gres
    A. P. Demichev
    Yu. Yu. Dubenskaya
    D. P. Zhurov
    Moscow University Physics Bulletin, 2023, 78 : S32 - S36
  • [2] Application of Convolutional Neural Networks for Data Analysis in TAIGA-HiSCORE Experiment
    Kryukov, A. P.
    Vlaskina, A. A.
    Polyakov, S. P.
    Gres, E. O.
    Demichev, A. P.
    Dubenskaya, Yu. Yu.
    Zhurov, D. P.
    MOSCOW UNIVERSITY PHYSICS BULLETIN, 2023, 78 (SUPPL 1) : S32 - S36
  • [3] Data Symmetries and Learning in Fully Connected Neural Networks
    Anselmi, Fabio
    Manzoni, Luca
    D'onofrio, Alberto
    Rodriguez, Alex
    Caravagna, Giulio
    Bortolussi, Luca
    Cairoli, Francesca
    IEEE ACCESS, 2023, 11 : 47282 - 47290
  • [4] Energy Spectrum of Primary Cosmic Rays, According to TUNKA-133 and TAIGA-HiSCORE EAS Cherenkov Light Data
    Prosin V.V.
    Astapov I.I.
    Bezyazeekov P.A.
    Boreyko V.
    Borodin A.N.
    Brueckner M.
    Budnev N.M.
    Wischnewski R.
    Garmash A.Y.
    Gafarov A.R.
    Gorbunov N.V.
    Grebenyuk V.M.
    Gress O.A.
    Gress T.I.
    Grinyuk A.A.
    Grishin O.G.
    Dyachok A.N.
    Zhurov D.P.
    Zagorodnikov A.V.
    Zurbanov V.L.
    Ivanova A.L.
    Kazarina Y.A.
    Kalmykov N.N.
    Kindin V.V.
    Kirilenko P.S.
    Kiryuhin S.N.
    Kozhin V.A.
    Kokoulin R.P.
    Kompaniets K.G.
    Korosteleva E.E.
    Kravchenko E.A.
    Kuzmichev L.A.
    Kunnas M.
    Chiavassa A.
    Lagutin A.A.
    Lemeshev Y.
    Lenok V.V.
    Lubsandorzhiev B.K.
    Lubsandorzhiev N.B.
    Mirgazov R.R.
    Mirzoyan R.
    Monkhoev R.D.
    Osipova E.A.
    Panasyuk M.I.
    Pankov L.V.
    Pakhorukov A.L.
    Petrukhin A.A.
    Poleschuk V.A.
    Popescu M.
    Popova E.G.
    Bulletin of the Russian Academy of Sciences: Physics, 2019, 83 (08) : 1016 - 1019
  • [5] Spectrum Analysis for Fully Connected Neural Networks
    Jia, Bojun
    Zhang, Yanjun
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (12) : 10091 - 10104
  • [6] On the Learnability of Fully-connected Neural Networks
    Zhang, Yuchen
    Lee, Jason D.
    Wainwright, Martin J.
    Jordan, Michael I.
    ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 54, 2017, 54 : 83 - 91
  • [7] Distributed Learning of Fully Connected Neural Networks using Independent Subnet Training
    Yuan, Binhang
    Wolfe, Cameron R.
    Dun, Chen
    Tang, Yuxin
    Kyrillidis, Anastasios
    Jermaine, Chris
    PROCEEDINGS OF THE VLDB ENDOWMENT, 2022, 15 (08): : 1581 - 1590
  • [8] Compressing fully connected layers of deep neural networks using permuted features
    Nagaraju, Dara
    Chandrachoodan, Nitin
    IET COMPUTERS AND DIGITAL TECHNIQUES, 2023, 17 (3-4): : 149 - 161
  • [9] Automatic model selection for fully connected neural networks
    Laredo D.
    Ma S.F.
    Leylaz G.
    Schütze O.
    Sun J.-Q.
    International Journal of Dynamics and Control, 2020, 8 (04) : 1063 - 1079
  • [10] A homotopy training algorithm for fully connected neural networks
    Chen, Qipin
    Hao, Wenrui
    PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2019, 475 (2231):