A neural network for beam background decomposition in Belle II at SuperKEKB

被引：0

作者：

Schwenker, B. ^{[1
]}

Herzberg, L. ^{[1
]}

Buch, Y. ^{[1
]}

Frey, A. ^{[1
]}

Natochii, A. ^{[2
]}

Vahsen, S. ^{[2
]}

Nakayama, H. ^{[3
,4
]}

机构：

[1] Georg August Univ Gottingen, Phys Inst 2, D-37073 Gottingen, Germany

[2] Univ Hawaii, Honolulu, HI 96822 USA

[3] High Energy Accelerator Res Org KEK, Tsukuba 3050801, Japan

[4] Grad Univ Adv Studies, SOKENDAI, Hayama 2400193, Japan

来源：

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT | 2023年 / 1049卷

关键词：

Belle II; SuperKEKB; Beam background; Neural networks; Nonlinear regression; Machine learning for accelerators;

D O I：

10.1016/j.nima.2023.168112

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

We describe a neural network for predicting the background hit rate in the Belle II detector produced by the SuperKEKB electron-positron collider. The neural network, BGNet, learns to predict the individual contributions of different physical background sources, such as beam-gas scattering or continuous top-up injections into the collider, to Belle II sub-detector rates. The samples for learning are archived 1 Hz time series of diagnostic variables from the SuperKEKB collider subsystems and measured hit rates of Belle II used as regression targets. We test the learned model by predicting detector hit rates on archived data from different run periods not used during training. We show that a feature attribution method can help interpret the source of changes in the background level over time.

引用

页数：10

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