Standard

Telescope Array Surface Detector Energy and Arrival Direction Estimation Using Deep Learning. / The Telescope Array Collaboration; Калашев, Олег Евгеньевич.

2022. Paper presented at 37th International Cosmic Ray Conference, Берлин, Berlin, Germany.

Research output: Contribution to conference › Paper › peer-review

Harvard

The Telescope Array Collaboration & Калашев, ОЕ 2022, 'Telescope Array Surface Detector Energy and Arrival Direction Estimation Using Deep Learning', Paper presented at 37th International Cosmic Ray Conference, Берлин, Germany, 12.07.2021 - 23.07.2021. https://doi.org/10.22323/1.395.0252

APA

The Telescope Array Collaboration, & Калашев, О. Е. (2022). Telescope Array Surface Detector Energy and Arrival Direction Estimation Using Deep Learning. Paper presented at 37th International Cosmic Ray Conference, Берлин, Berlin, Germany. https://doi.org/10.22323/1.395.0252

Vancouver

The Telescope Array Collaboration, Калашев ОЕ. Telescope Array Surface Detector Energy and Arrival Direction Estimation Using Deep Learning. 2022. Paper presented at 37th International Cosmic Ray Conference, Берлин, Berlin, Germany. doi: 10.22323/1.395.0252

Author

The Telescope Array Collaboration ; Калашев, Олег Евгеньевич. / Telescope Array Surface Detector Energy and Arrival Direction Estimation Using Deep Learning. Paper presented at 37th International Cosmic Ray Conference, Берлин, Berlin, Germany.10 p.

BibTeX

@conference{2f90ce51873c47a1b252dce95e05972b,
title = "Telescope Array Surface Detector Energy and Arrival Direction Estimation Using Deep Learning",
abstract = "A novel ultra-high-energy cosmic rays energy and arrival direction reconstruction method for Telescope Array surface detector is presented. The analysis is based on a deep convolutional neural network using detector signal time series as the input and the network is trained on a large Monte-Carlo dataset. This method is compared in terms of statistical and systematic energy and arrival direction determination errors with the standard Telescope Array surface detector event reconstruction procedure.",
author = "{The Telescope Array Collaboration} and D. Ivanov and Kuznetsov, {M. Yu} and Rubtsov, {G. I.} and T. Sako and Y. Tsunesada and Zhezher, {Y. V.} and Abbasi, {R. U.} and M. Abe and T. Abu-Zayyad and M. Allen and Y. Arai and E. Barcikowski and Belz, {J. W.} and Bergman, {D. R.} and Blake, {S. A.} and I. Buckland and R. Cady and Cheon, {B. G.} and J. Chiba and M. Chikawa and T. Fujii and K. Fujisue and K. Fujita and R. Fujiwara and M. Fukushima and R. Fukushima and G. Furlich and R. Gonzalez and W. Hanlon and M. Hayashi and N. Hayashida and K. Hibino and R. Higuchi and K. Honda and D. Ikeda and T. Inadomi and N. Inoue and T. Ishii and H. Ito and D. Ivanov and H. Iwakura and Jeong, {H. M.} and S. Jeong and Jui, {C. C.H.} and K. Kadota and F. Kakimoto and K. Kasahara and S. Kasami and H. Kawai and S. Kawakami and Калашев, {Олег Евгеньевич}",
note = "The cluster of the Theoretical Division of INR RAS was used for the numerical part of the work. The development and application of the machine learning analysis method is supported by the Russian Science Foundation grant No. 17-72-20291 (INR).; 37th International Cosmic Ray Conference : The astroparticle physics conference, ICRC2021 ; Conference date: 12-07-2021 Through 23-07-2021",
year = "2022",
month = mar,
day = "18",
doi = "10.22323/1.395.0252",
language = "English",
url = "https://icrc2021.desy.de/",

}

RIS

TY - CONF

T1 - Telescope Array Surface Detector Energy and Arrival Direction Estimation Using Deep Learning

AU - The Telescope Array Collaboration

AU - Ivanov, D.

AU - Kuznetsov, M. Yu

AU - Rubtsov, G. I.

AU - Sako, T.

AU - Tsunesada, Y.

AU - Zhezher, Y. V.

AU - Abbasi, R. U.

AU - Abe, M.

AU - Abu-Zayyad, T.

AU - Allen, M.

AU - Arai, Y.

AU - Barcikowski, E.

AU - Belz, J. W.

AU - Bergman, D. R.

AU - Blake, S. A.

AU - Buckland, I.

AU - Cady, R.

AU - Cheon, B. G.

AU - Chiba, J.

AU - Chikawa, M.

AU - Fujii, T.

AU - Fujisue, K.

AU - Fujita, K.

AU - Fujiwara, R.

AU - Fukushima, M.

AU - Fukushima, R.

AU - Furlich, G.

AU - Gonzalez, R.

AU - Hanlon, W.

AU - Hayashi, M.

AU - Hayashida, N.

AU - Hibino, K.

AU - Higuchi, R.

AU - Honda, K.

AU - Ikeda, D.

AU - Inadomi, T.

AU - Inoue, N.

AU - Ishii, T.

AU - Ito, H.

AU - Ivanov, D.

AU - Iwakura, H.

AU - Jeong, H. M.

AU - Jeong, S.

AU - Jui, C. C.H.

AU - Kadota, K.

AU - Kakimoto, F.

AU - Kasahara, K.

AU - Kasami, S.

AU - Kawai, H.

AU - Kawakami, S.

AU - Калашев, Олег Евгеньевич

N1 - Conference code: 37

PY - 2022/3/18

Y1 - 2022/3/18

N2 - A novel ultra-high-energy cosmic rays energy and arrival direction reconstruction method for Telescope Array surface detector is presented. The analysis is based on a deep convolutional neural network using detector signal time series as the input and the network is trained on a large Monte-Carlo dataset. This method is compared in terms of statistical and systematic energy and arrival direction determination errors with the standard Telescope Array surface detector event reconstruction procedure.

AB - A novel ultra-high-energy cosmic rays energy and arrival direction reconstruction method for Telescope Array surface detector is presented. The analysis is based on a deep convolutional neural network using detector signal time series as the input and the network is trained on a large Monte-Carlo dataset. This method is compared in terms of statistical and systematic energy and arrival direction determination errors with the standard Telescope Array surface detector event reconstruction procedure.

UR - https://www.scopus.com/inward/record.url?eid=2-s2.0-85144403106&partnerID=40&md5=e9e2462c78541edd1533fda7371a17f4

UR - https://www.mendeley.com/catalogue/7e76eb9d-b554-3d53-af80-6d02b8eb7304/

U2 - 10.22323/1.395.0252

DO - 10.22323/1.395.0252

M3 - Paper

T2 - 37th International Cosmic Ray Conference

Y2 - 12 July 2021 through 23 July 2021

ER -

ID: 46054694