Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector
We provide a fast approach incorporating the usage of deep learning for studying the effects of the number of photon sensors in an antineutrino detector on the event reconstruction performance therein. This work is a first attempt to harness the power of deep learning for detector designing and upgr...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Advances in High Energy Physics |
| Online Access: | http://dx.doi.org/10.1155/2018/7024309 |
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| _version_ | 1849434720138428416 |
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| author | Chang-Wei Loh Zhi-Qiang Qian Rui Zhang You-Hang Liu De-Wen Cao Wei Wang Hai-Bo Yang Ming Qi |
| author_facet | Chang-Wei Loh Zhi-Qiang Qian Rui Zhang You-Hang Liu De-Wen Cao Wei Wang Hai-Bo Yang Ming Qi |
| author_sort | Chang-Wei Loh |
| collection | DOAJ |
| description | We provide a fast approach incorporating the usage of deep learning for studying the effects of the number of photon sensors in an antineutrino detector on the event reconstruction performance therein. This work is a first attempt to harness the power of deep learning for detector designing and upgrade planning. Using the Daya Bay detector as a case study and the vertex reconstruction performance as the objective for the deep neural network, we find that the photomultiplier tubes (PMTs) at Daya Bay have different relative importance to the vertex reconstruction. More importantly, the vertex position resolutions for the Daya Bay detector follow approximately a multiexponential relationship with respect to the number of PMTs and, hence, the coverage. This could also assist in deciding on the merits of installing additional PMTs for future detector plans. The approach could easily be used with other objectives in place of vertex reconstruction. |
| format | Article |
| id | doaj-art-e3e806d037cf4917bb9f126f7f3e67b3 |
| institution | Kabale University |
| issn | 1687-7357 1687-7365 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in High Energy Physics |
| spelling | doaj-art-e3e806d037cf4917bb9f126f7f3e67b32025-08-20T03:26:33ZengWileyAdvances in High Energy Physics1687-73571687-73652018-01-01201810.1155/2018/70243097024309Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino DetectorChang-Wei Loh0Zhi-Qiang Qian1Rui Zhang2You-Hang Liu3De-Wen Cao4Wei Wang5Hai-Bo Yang6Ming Qi7Physics Department, Nanjing University, 22 Hankou Road, Nanjing, Jiangsu, ChinaPhysics Department, Nanjing University, 22 Hankou Road, Nanjing, Jiangsu, ChinaPhysics Department, Nanjing University, 22 Hankou Road, Nanjing, Jiangsu, ChinaPhysics Department, Nanjing University, 22 Hankou Road, Nanjing, Jiangsu, ChinaPhysics Department, Nanjing University, 22 Hankou Road, Nanjing, Jiangsu, ChinaPhysics Department, Nanjing University, 22 Hankou Road, Nanjing, Jiangsu, ChinaPhysics Department, Nanjing University, 22 Hankou Road, Nanjing, Jiangsu, ChinaPhysics Department, Nanjing University, 22 Hankou Road, Nanjing, Jiangsu, ChinaWe provide a fast approach incorporating the usage of deep learning for studying the effects of the number of photon sensors in an antineutrino detector on the event reconstruction performance therein. This work is a first attempt to harness the power of deep learning for detector designing and upgrade planning. Using the Daya Bay detector as a case study and the vertex reconstruction performance as the objective for the deep neural network, we find that the photomultiplier tubes (PMTs) at Daya Bay have different relative importance to the vertex reconstruction. More importantly, the vertex position resolutions for the Daya Bay detector follow approximately a multiexponential relationship with respect to the number of PMTs and, hence, the coverage. This could also assist in deciding on the merits of installing additional PMTs for future detector plans. The approach could easily be used with other objectives in place of vertex reconstruction.http://dx.doi.org/10.1155/2018/7024309 |
| spellingShingle | Chang-Wei Loh Zhi-Qiang Qian Rui Zhang You-Hang Liu De-Wen Cao Wei Wang Hai-Bo Yang Ming Qi Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector Advances in High Energy Physics |
| title | Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector |
| title_full | Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector |
| title_fullStr | Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector |
| title_full_unstemmed | Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector |
| title_short | Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector |
| title_sort | deep learning the effects of photon sensors on the event reconstruction performance in an antineutrino detector |
| url | http://dx.doi.org/10.1155/2018/7024309 |
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