The neural network first-level hardware track trigger of the Belle II experiment

We describe the principles and performance of the first-level (“L1”) hardware track trigger of Belle II , which uses the information of Belle II ’s Central Drift Chamber (“CDC”) and provides three-dimensional track candidates based on neural networks. The inputs to the networks are “2D” track candidates in the plane transverse to the electron–positron beams, obtained via Hough transforms, and selected information from the stereo layers of the CDC. The networks then provide estimates for the origin of the track candidates in direction of the colliding beams (“ z -vertex”), as well as their polar emission angles θ . Using a suitable cut d on the z -vertices of the “neural” tracks allows us to identify events coming from the collision region ( z ≈ 0 ), and to suppress the overwhelming background from outside. Requiring | z | < d for at least one neural track in an event with two or more 2D candidates will set an L1 track trigger. The networks also enable a minimum bias trigger, requiring a single 2D track candidate validated by a neural track with a momentum larger than 0.7 GeV in addition to the | z | condition. We also sketch our concepts for upgrading the neural trigger in view of rising instantaneous luminosities, accompanied by increasing backgrounds.

Authors

Institutions

Publication Details

Journal
Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment
Published
2025-02-06
DOI
https://doi.org/10.1016/j.nima.2025.170279
Citations
3
Primary Topic
Particle physics theoretical and experimental studies
Type
article
Field-Weighted Citation Impact
5.61
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The neural network first-level hardware track trigger of the Belle II experiment

Alexander Lenz, M. Neu, H. Bae, Jürgen Becker et al.
3 citations
Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment
Particle physics theoretical and experimental studies
5.61
article

The neural network first-level hardware track trigger of the Belle II experiment

Alexander Lenz, M. Neu, H. Bae, Jürgen Becker, G. Inguglia, M. Bertemes, S. Skambraks, J.-G. Shiu, M. Campajola, Alois Knoll, M. Iwasaki, C. Kiesling, K. Unger, F. Meggendorfer, Ying D. Liu, Y.-T. Lai, J. H. Yin, J. Schieck, T. Koga, Tobias Jülg, H. Nakazawa, Elia Schmidt, T. Ferber, Steffen Bähr, T. Forsthofer, S. Hiesl
article en
3 citations

Abstract

We describe the principles and performance of the first-level (“L1”) hardware track trigger of Belle II , which uses the information of Belle II ’s Central Drift Chamber (“CDC”) and provides three-dimensional track candidates based on neural networks. The inputs to the networks are “2D” track candidates in the plane transverse to the electron–positron beams, obtained via Hough transforms, and selected information from the stereo layers of the CDC. The networks then provide estimates for the origin of the track candidates in direction of the colliding beams (“ z -vertex”), as well as their polar emission angles θ . Using a suitable cut d on the z -vertices of the “neural” tracks allows us to identify events coming from the collision region ( z ≈ 0 ), and to suppress the overwhelming background from outside. Requiring | z | < d for at least one neural track in an event with two or more 2D candidates will set an L1 track trigger. The networks also enable a minimum bias trigger, requiring a single 2D track candidate validated by a neural track with a momentum larger than 0.7 GeV in addition to the | z | condition. We also sketch our concepts for upgrading the neural trigger in view of rising instantaneous luminosities, accompanied by increasing backgrounds.

Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated EquipmentVol. 1073
Karlsruhe Institute of Technology (DE), Austrian Academy of Sciences (AT), High Energy Accelerator Research Organization (JP), Max Planck Institute for Physics (DE), Istituto Nazionale di Fisica Nucleare, Sezione di Napoli (IT), Institute of High Energy Physics (AT), Embedded Systems (United States) (US), Technical University of Munich (DE)
Openalex Percentile: Top 6%
Particle physics theoretical and experimental studies
5.61
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.