What Machine Learning Can Do for Focusing Aerogel Detectors

被引：1

作者：

Shipilov, F. ^{[1
]}

Barnyakov, A. ^{[2
,3
]}

Bobrovnikov, V. ^{[2
]}

Kononov, S. ^{[2
,4
]}

Ratnikov, F. ^{[1
]}

机构：

[1] NRU Higher Sch Econ, Moscow, Russia

[2] Russian Acad Sci, Siberian Branch, Budker Inst Nucl Phys, Novosibirsk, Russia

[3] Novosibirsk State Tech Univ, Novosibirsk, Russia

[4] Novosibirsk State Univ, Novosibirsk, Russia

来源：

PHYSICS OF ATOMIC NUCLEI | 2023年 / 86卷 / 05期

关键词：

RECONSTRUCTION;

D O I：

10.1134/S106377882305037X

中图分类号：

O57 [原子核物理学、高能物理学];

学科分类号：

070202 ;

摘要：

Particle identification at the Super Charm-Tau factory experiment will be provided by a Focusing Aerogel Ring Imaging CHerenkov detector (FARICH). Silicon photomultipliers used for the Cherenkov light detection generate a lot of noise hits that must be mitigated to reduce both the data flow and negative effects on particle velocity resolution. In this work we present our approach to filtering signal hits, inspired by object detection techniques for computer vision. Several ML-based approaches to the FARICH reconstruction problem in different settings are also discussed.

引用

页码：864 / 868

页数：5

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