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Mass composition anisotropy with the Telescope Array Surface Detector data 37

Mass composition anisotropy with the Telescope Array Surface...

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37th International Cosmic Ray conference, ICRC 2021

作者： Zhezher, Yana Abbasi, R.U. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Globus, N. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuzmin, V. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nishio, E. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. Institute for Cosmic Ray Research University of Tokyo Kashiwanoha 5-1-5 Chiba Kashiwa Japan Institute for Nuclear Research Russian Academy of Sciences 60-letya Oktyabrya 7a Moscow Russia Department of Physics Loyola University Chicago ChicagoIL United States High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research of the Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawa-shi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Theorique Universite Libre de Bruxelles Brussels Bel

Mass composition anisotropy is predicted by a number of theories describing sources of ultrahigh-energy cosmic rays. Event-by-event determination of a type of a primary cosmic-ray particle is impossible due to large shower-to-shower fluctuations, and the mass composition usually is obtained by averaging over some composition-sensitive observable determined independently for each extensive air shower (EAS) over a large number of events. In the present study we propose to employ the observable ξ used in the TA mass composition analysis for the mass composition anisotropy analysis. The ξ variable is determined with the use of Boosted Decision Trees (BDT) technique trained with the Monte-Carlo sets, and the ξ value is assigned for each event, where ξ = 1 corresponds to an event initiated by the primary iron nuclei and ξ = −1 corresponds to a proton event. Use of ξ distributions obtained for the Monte-Carlo sets allows us to separate proton and iron candidate events from a data set with some given accuracy and study its distributions over the observed part of the sky. Results for the TA SD 11-year data set mass composition anisotropy will be presented. © Copyright owned by the author(s).

关键词： Anisotropy

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Deep Neural Network Method of Recognizing the Critical Situations for Transport Systems by Video Images 10

Deep Neural Network Method of Recognizing the Critical Situa...

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10th International conference on Ambient Systems, Networks and Technologies (ANT) / 2nd International conference on Emerging Data and Industry 4.0 (EDI40)

作者： Pashchenko, F. F. Amosov, O. S. Amosova, S. G. Ivanov, Y. S. Zhiganov, S., V Russian Acad Sci VA Trapeznikov Inst Control Sci Profsoyuznaya St 65 Moscow 117997 Russia Moscow Inst Phys & Technol Kerchenskaya St 1A Moscow 117303 Russia Komsomolsk Na Amure State Univ Lenin Ave 27 Komsomolsk On Amur 681013 Russia

The deep neural network method of recognizing critical situations for transport systems according to video frames from the intelligent vehicles cameras is offered, that is effective in terms of accuracy and high-speed performance. Unlike the known solutions for the objects and normal or critical situations detection and recognition, it uses the classification with the subsequent reinforcement on the basis of several video stream frames and with the automatic annotation algorithm. The adapted architectures of neural networks are offered: the dual network to identify drivers and passengers according to the face image, the network with independent recurrent layers to classify situations according to the video fragment. The scheme of the intellectual distributed city system of transport safety using the cameras and on-board computers united in a single network is offered. Software modules in Python are developed and natural experiments are made. The possibility of the offered algorithms and programs in UGV or in the driver assistant systems implementation is shown with the illustrating examples in real-time. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND (http://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the conference program Chairs.

关键词： critical situation transport system localization recognition deep neural network computer vision system

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Update on the large-scale cosmic-ray anisotropy search at the highest energies by the Telescope Array Experiment 37

Update on the large-scale cosmic-ray anisotropy search at th...

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37th International Cosmic Ray conference, ICRC 2021

作者： Fujii, Toshihiro Abbasi, R.U. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuzmin, V. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nishio, E. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. Hakubi Center for Advanced Research Kyoto University Sakyo-ku Kyoto Japan Graduate School of Science Kyoto University Sakyo-ku Kyoto Japan Department of Physics Loyola University Chicago ChicagoIL United States High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research of the Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawa-shi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Théorique Université Libre de Bruxelles Brussels Belgium Department of Physics Yonsei University Se

The study of large-scale anisotropy at the highest energies is essential for understanding the transition from cosmic rays of galactic origin to those of extra-galactic origin, along with the magnetic fields in the galaxy and those beyond. Motivated by a significant detection of the large-scale anisotropy above 8 EeV by the Pierre Auger Observatory (Auger), we had previously reported, using 11 years of Telescope Array (TA) surface array data, a result compatible both with that of Auger, and with an isotropic source distribution [1]. In this contribution, we will show the preliminary updated results using 12 years TA SD data to search for the large-scale anisotropy at the highest energies. © Copyright owned by the author(s).

关键词： Cosmic rays

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Hotspot Update, and a new Excess of Events on the Sky Seen by the Telescope Array Experiment 37

Hotspot Update, and a new Excess of Events on the Sky Seen b...

引用

37th International Cosmic Ray conference, ICRC 2021

作者： Kim, Jihyun Ivanov, Dmitri Kawata, Kazumasa Sagawa, Hiroyuki Thomson, Gordon Abbasi, R.U. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuzmin, V. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nishio, E. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Institute for Cosmic Ray Research University of Tokyo Kashiwa Chiba Japan Department of Physics Loyola University Chicago ChicagoIL United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan Astrophysical Big Bang Laboratory RIKEN Wako Saitama Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawashi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Théorique Université Libre de Bruxelles Brussels Belgium Department of Physics Yonsei University Seodaemun-gu Seoul Korea Republic of Center for Astrophysics and Cosmology University of Nova Gorica Nova Gorica Slovenia Faculty of Science Koc

The Telescope Array (TA) experiment, the largest observatory studying ultrahigh energy cosmic rays in the northern hemisphere, has reported an excess in the arrival direction distribution for events with energies above 5.7 × 1019 eV, called the hotspot. We report here the latest results of the TA hotspot using the most recent data measured by the TA surface detector array, which is more than doubled exposure since the first publication;the hotspot still exists with 3 sigma post-trial significance. By using an oversampling search with a 20◦-circle, similar to the study of the hotspot, we find an additional excess of events at slightly lower energies. The Perseus-Pisces supercluster lies at the location of the new excess. Assuming this structure is responsible for the excess, we conducted a statistical analysis to verify the correlation between observed events and the members of the Perseus-Pisces supercluster. © Copyright owned by the author(s).

关键词： Cosmology

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Study on the cosmic ray intensity variation using scintillation counters for air shower observation 37

Study on the cosmic ray intensity variation using scintillat...

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37th International Cosmic Ray conference, ICRC 2021

作者： Nonaka, Toshiyuki Oshima, Akitoshi Yamazaki, Katsuya Abbasi, R.U. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nishio, E. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwanoha Kashiwa ChiBa277-8582 Japan College of Engineering ChuBu University Aichi Kasugai487-8501 Japan Department of Physics Loyola University Chicago ChicagoIL United States High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawa-shi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Theorique University Libre de Bruxelles Brussels Belgium Department of P

We report a study on the conversion of a giant cosmic ray observatory for air shower observation to observe cosmic ray intensity variations caused by solar activity, anisotropy associated with interplanetary disturbances, and detection of sudden cosmic ray events on the earth’s surface. In this report, we use data from the surface detectors operated by the Telescope Array experiment located at 39°N, 112°W (total detector area: 2250 m2). In order to evaluate the cosmic ray intensity variations, we will compare the data with some of the corrections considered and with available world wide database such as Nagoya Muon detector and other observatories that have been in stable operation at different geographic longitudes. Finally, we will report on the intensity variations due to weather and solar activity recorded during the observation period. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Cosmic rays

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Mass composition of Telescope Array’s surface detectors events using deep learning 37

Mass composition of Telescope Array’s surface detectors eve...

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37th International Cosmic Ray conference, ICRC 2021

作者： Kharuk, I. Kalashev, O. Abbasi, R.U. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nishio, E. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. Institute for Nuclear Research The Russian Academy of Sciences 60th October Anniversary Prospect 7a Moscow117312 Russia Moscow Institute of Physics and Technology Institutsky lane 9 Moscow region Dolgoprudny141700 Russia Department of Physics Loyola University Chicago ChicagoIL United States High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawa-shi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Théorique Université

We report on an improvement of deep learning techniques used for identifying primary particles of atmospheric air showers. The progress was achieved by using two neural networks. The first works as a classifier for individual events, while the second predicts fractions of elements in an ensemble of events based on the inference of the first network. For a fixed hadronic model, this approach yields an accuracy of 90% in identifying fractions of elements in an ensemble of events. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Cosmology

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performance Evaluation of Some Machine Learning Algorithms in NASA Defect Prediction Data Sets

Performance Evaluation of Some Machine Learning Algorithms i...

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International conference on computer Science and Engineering (UBMK)

作者： Zeynep Behrin Guven Aydin Ruya Samli SoftwareEngineering Maltepe University Istanbul Turkey Computer Engineering Istanbul University-Cerrahpasa Istanbul Turkey

ISBN: (数字)9781728175652

ISBN: (纸本)9781728175669

The main purpose of machine learning is to model the systems making predictions by using some mathematical and operational features on the data with computers [1]. Today, there are many studies on machine learning in all areas of the software world. Software Defect Prediction is a sub-branch that progresses rapidly in machine learning. In this study, five of machine learning classification algorithms were conducted on with PYTHON programming language on defect prediction data sets which are JM1, KC1, CM1, PC1 in the PROMISE repository. These data sets are created within the scope of the publicly available NASA institution's Metric Data program. The accuracy, recall, precision and F-measure and support values of the algorithms on the data are compared. When the results are examined in terms of the accuracy of machine learning algorithms, the accuracy rates of the algorithms are quite high in all 4 data sets. The highest success rates were obtained from the classification algorithms applied in 4 data sets in CMl and PCI data sets. In 4 data sets, the highest success rates were seen with Random Forest algorithm.

关键词： Software Random forests Decision trees

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Research on performance of Terrain Matching Algorithm for Underwater Autonomous Vehicle Based on Particle Filter 5

Research on Performance of Terrain Matching Algorithm for Un...

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5th International conference on Mechanical Engineering and Automation Science (ICMEAS)

作者： Jian, Shen Lu, Xiong Ning, Ba Naval Univ Engn Postdoctoral Program Control Sci & Engn Wuhan 430033 Peoples R China OrdnanceNCO Acad Army Engn Univ Dept Radar Syst Wuhan 430075 Peoples R China

AUV is a powerful tool for exploration and development of marine resources. Underwater terrain matching navigation is an emerging passive autonomous navigation method, which improves the long-term and high-precision navigation capability of AUV. The underwater terrain matching algorithm based on particle filter (PF) use a large number of particle sampling to approximate the system state, which can avoid the Taylor expansion truncation error caused by the terrain linearization method, and has good matching precision and terrain adaptability. However, due to the strong nonlinearity and the uneven distribution of the underwater terrain features, it is difficult to achieve the full-range terrain matching optimize with a single configuration parameter of PF underwater terrain matching algorithm. For this reason, this paper has comprehensively studied the PF terrain matching algorithm (referred to as PF matching algorithm). The principle, accuracy and implementation steps of PF algorithm are analyzed. With the UKF algorithm as a reference, the advantages and disadvantages of PF-based algorithm are studied through computer simulation of terrain matching navigation. The matching precision and terrain adaptability of PF-based algorithm has been tested. From the simulation results, it is concluded that the particle diversity decline is one of the main factors affecting the matching performance, which provides a basis for algorithm performance improvement and engineering adaptability improvement.

关键词： Monte Carlo methods

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Boinc-based branch-and-bound 4th

Boinc-based branch-and-bound

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4th Russian Supercomputing Days conference, RuSCDays 2018

作者： Ignatov, Andrei Posypkin, Mikhail Moscow State University Moscow Russia Federal Research Center Computer Science and Control of Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783030058067

The paper proposes an implementation of the Branch-and-Bound method for an enterprise grid based on the BOINC infrastructure. The load distribution strategy and the overall structure of the developed system are described with special attention payed to some specific issues such as incumbent updating and load distribution. The implemented system was experimentally tested on a moderate size enterprise grid. The achieved results demonstrate an adequate efficiency of the proposed approach. © 2019, Springer Nature Switzerland AG.

关键词： Branch and bound method

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Countering Variations and Thermal Effects for Accurate Optical Neural Networks

Countering Variations and Thermal Effects for Accurate Optic...

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IEEE International conference on computer-Aided Design

作者： Ying Zhu Grace Li Zhang Bing Li Xunzhao Yin Cheng Zhuo Huaxi Gu Tsung-Yi Ho Ulf Schlichtmann Chair of Electronic Design Automation Technical University of Munich (TUM) Germany College of Information Science and Electronic Engineering Zhejiang University China School of Telecommunication Engineering Xidian University China National Tsing Hua University Taiwan

ISBN: (数字)9781665423243

Optical neural networks (ONNs) have emerged as a promising high-performance computing platform to accelerate deep neural networks. In ONNs, phases of light are modulated through Mach-Zehnder Interferometers (MZIs), and MZIs are connected in a gridlike layout to implement multiply-accumulate operations. However, ONNs are very sensitive to process variations and thermal effects. This sensitivity leads to a significant degradation of inference accuracy of ONNs and thus renders them unusable in practice. In this paper, we propose a framework to calibrate process variations and counter thermal effects by power compensation. Experimental results demonstrate that the proposed framework can recover the inference accuracy under variations and thermal effects, e.g., from as low as 11.05% back to 74.11% for LeNet-5 on Cifar10, so that ONNs can achieve an inference accuracy similar to the accuracy after software training while providing their high bandwidth in neuromorphic computing.

关键词： Training Neural networks computer architecture Temperature measurement Software Optical sensors Optical device fabrication

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