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Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector

Authors :
Chang-Wei Loh
Zhi-Qiang Qian
Rui Zhang
You-Hang Liu
De-Wen Cao
Wei Wang
Hai-Bo Yang
Ming Qi
Source :
Advances in High Energy Physics, Vol 2018 (2018)
Publication Year :
2018
Publisher :
Hindawi Limited, 2018.

Abstract

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.

Subjects

Subjects :
Physics
QC1-999

Details

Language :
English
ISSN :
16877357 and 16877365
Volume :
2018
Database :
Directory of Open Access Journals
Journal :
Advances in High Energy Physics
Publication Type :
Academic Journal
Accession number :
edsdoj.3e806d037cf4917bb9f126f7f3e67b3
Document Type :
article
Full Text :
https://doi.org/10.1155/2018/7024309