Signal Separation from Thermal Neutrons in Electron-Neutron Detectors Using Convolutional Neural Nets in the ENDA Experiment

被引：0

作者：

Kurinov, K. O. ^{[1
]}

Kuleshov, D. A. ^{[1
]}

Lagutkina, A. A. ^{[2
]}

Stenkin, Yu. V. ^{[1
,2
]}

Shchegolev, O. B. ^{[1
,2
]}

机构：

[1] Russian Acad Sci, Inst Nucl Res, Moscow 117312, Russia

[2] Moscow Inst Phys & Technol, Dolgoprudnyi 141701, Moscow Oblast, Russia

来源：

JOURNAL OF EXPERIMENTAL AND THEORETICAL PHYSICS | 2023年 / 136卷 / 04期

关键词：

PULSE-SHAPE DISCRIMINATION;

D O I：

10.1134/S1063776123040039

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

The electron-neutron detector array (ENDA) is being created in China within the large high-altitude air shower observatory (LHAASO) project. The concept of the array is to simultaneously record the electromagnetic and hadronic components of extensive air showers (EAS) with EN detectors. To estimate the number of hadrons in an EAS, the array detectors record secondary thermal neutrons delayed relative to the shower front. Some of the delayed pulses are created by the simultaneous passage of several charged particles through the scintillator (the signal from one particle lies below the detection threshold) and by the photomultiplier noise. We propose a neutron pulse separation method for EN detectors using convolutional neural networks and make a comparison with the baseline method being currently applied at the installation.

引用

页码：465 / 471

页数：7

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