Development of a vertex finding algorithm using Recurrent Neural Network

被引：3

作者：

Goto, Kiichi ^{[1
,4
,6
]}

Suehara, Taikan ^{[1
,2
,3
,4
,6
]}

Yoshioka, Tamaki ^{[1
,2
,3
,4
,6
]}

Kurata, Masakazu ^{[4
,6
]}

Nagahara, Hajime ^{[4
,5
,6
]}

Nakashima, Yuta ^{[4
,5
,6
]}

Takemura, Noriko ^{[4
,5
,6
]}

Iwasaki, Masako ^{[4
,5
,6
,7
,8
]}

机构：

[1] Kyushu Univ, Grad Sch Sci, Dept Phys, Fukuoka, Japan

[2] Kyushu Univ, Fac Sci, Dept Phys, Fukuoka, Japan

[3] Kyushu Univ, Res Ctr Adv Particle Phys RCAPP, Fukuoka, Japan

[4] Univ Tokyo, Grad Sch Sci, Dept Phys, Tokyo, Japan

[5] Osaka Univ, Inst Databil Sci IDS, Suita, Japan

[6] Osaka City Univ, Grad Sch Sci, Dept Math & Phys, Osaka, Japan

[7] Osaka City Univ, Nambu Yoichiro Inst Theoret & Expt Phys NITEP, Osaka, Japan

[8] Osaka Univ, Res Ctr Nucl Phys RCNP, Suita, Japan

来源：

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT | 2023年 / 1047卷

关键词：

International linear collider; Vertex finding; Recurrent Neural Network; Attention; LHC;

D O I：

10.1016/j.nima.2022.167836

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

Deep learning is a rapidly-evolving technology with the possibility to significantly improve the physics reach of collider experiments. In this study we developed a novel vertex finding algorithm for future lepton colliders such as the International Linear Collider. We deploy two networks: one consists of simple fully-connected layers to look for vertex seeds from track pairs, and the other is a customized Recurrent Neural Network with an attention mechanism and an encoder-decoder structure to associate tracks to the vertex seeds. The performance of the vertex finder is compared with the standard ILC vertex reconstruction algorithm.

引用

页数：9

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