Using Deep Learning to Enhance Event Geometry Reconstruction for the Telescope Array Surface Detector
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| Yayımlandı: | arXiv.org (Sep 13, 2020), p. n/a |
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| Yazar: | |
| Diğer Yazarlar: | , , , , , |
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Cornell University Library, arXiv.org
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| Online Erişim: | Citation/Abstract Full text outside of ProQuest |
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MARC
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|---|---|---|---|
| 001 | 2403203412 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 024 | 7 | |a 10.1088/2632-2153/abae74 |2 doi | |
| 035 | |a 2403203412 | ||
| 045 | 0 | |b d20200913 | |
| 100 | 1 | |a Ivanov, D | |
| 245 | 1 | |a Using Deep Learning to Enhance Event Geometry Reconstruction for the Telescope Array Surface Detector | |
| 260 | |b Cornell University Library, arXiv.org |c Sep 13, 2020 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a The extremely low flux of ultra-high energy cosmic rays (UHECR) makes their direct observation by orbital experiments practically impossible. For this reason all current and planned UHECR experiments detect cosmic rays indirectly by observing the extensive air showers (EAS) initiated by cosmic ray particles in the atmosphere. The world largest statistics of the ultra-high energy EAS events is recorded by the networks of surface stations. In this paper we consider a novel approach for reconstruction of the arrival direction of the primary particle based on the deep convolutional neural network. The latter is using raw time-resolved signals of the set of the adjacent trigger stations as an input. The Telescope Array (TA) Surface Detector (SD) is an array of 507 stations, each containing two layers plastic scintillator with an area of \(3\) m\(^2\). The training of the model is performed with the Monte-Carlo dataset. It is shown that within the Monte-Carlo simulations, the new approach yields better resolution than the traditional reconstruction method based on the fitting of the EAS front. The details of the network architecture and its optimization for this particular task are discussed. | |
| 653 | |a Scintillation counters | ||
| 653 | |a Arrays | ||
| 653 | |a Surface detectors | ||
| 653 | |a Stations | ||
| 653 | |a Computer architecture | ||
| 653 | |a Reconstruction | ||
| 653 | |a Cosmic ray showers | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Cosmic rays | ||
| 653 | |a Computer simulation | ||
| 653 | |a Optimization | ||
| 653 | |a Deep learning | ||
| 653 | |a Monte Carlo simulation | ||
| 700 | 1 | |a Kalashev, O E | |
| 700 | 1 | |a M Yu Kuznetsov | |
| 700 | 1 | |a Rubtsov, G I | |
| 700 | 1 | |a Sako, T | |
| 700 | 1 | |a Tsunesada, Y | |
| 700 | 1 | |a Zhezher, Y V | |
| 773 | 0 | |t arXiv.org |g (Sep 13, 2020), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2403203412/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2005.07117 |