A 3D track finder for the Belle II CDC L1 trigger

Skambraks, S.; Bähr, Steffen; Becker, Jürgen; Kiesling, C.; Mccarney, S.; Meggendorfer, F.; Tonder, R. Van; Unger, K.

doi:10.1088/1742-6596/1525/1/012102

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…This will be done by a 3D Hough transformation for further efficiency increases. It has already been shown in simulation that a more accurate resolution and larger solid angle coverage can be achieved (Skambraks et al, 2020 ).…”

Section: Exemplars Of Domain Applicationsmentioning

confidence: 95%

Applications and Techniques for Fast Machine Learning in Science

Deiana¹,

Tran²,

Agar³

et al. 2022

Front. Big Data

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In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

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Section: Exemplars Of Domain Applicationsmentioning

confidence: 95%

Applications and Techniques for Fast Machine Learning in Science

Deiana¹,

Tran²,

Agar³

et al. 2022

Front. Big Data

Self Cite

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“…Currently, the z-Vertex Track Trigger requires a found 2-dimensional track from the 2D Finder. It has been shown that a 3D Hough [2] preprocessing gives better results than the 2D finder, especially in the regions of shallow polar scattering angles and low transverse momentua. Prototype designs already exist which are also implemented in FPGA.…”

Section: Future Updates 3d Houghmentioning

confidence: 99%

Operation of the Neural z-Vertex Track Trigger for Belle II in 2021 - a Hardware Perspective

Unger

Bähr²,

Becker³

et al. 2023

J. Phys.: Conf. Ser.

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To reduce the background the z-Vertex Track Trigger estimates the collision origin in the Belle II experiment using neural networks. The main part is a pre-trained multilayer perceptron. The task of this perceptron is to estimate the z-vertex of the collision to suppress background from outside the interaction point. For this, a low latency real-time FPGA implementation is needed. We present an overview of the architecture and the FPGA implementation of the neuronal network and the preprocessing. We also show the handling of missing input data through preprocessing with specially trained neuronal networks implemented in hardware. For this, we will show the results of the z-vertex estimation and the latency for the implementation in the Belle II trigger system. Major update for the preprocessing stage utilizing a 3D Hough transformation processing step is ongoing.

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“…The resolution of the constructed transverse momentum and azimuthal angle was tested with 150 thousand single-electron events with 𝑝 𝑡 ranging from 160 MeV/c to 1500 MeV/c. According to The CDC detector at Belle II experiment [18] has a similar detector architecture to the MDC detector at STCF. Therefore, the results of 2D track reconstruction for the CDC L1 trigger can be used as a reference.…”

Section: Performance Of Parameter Estimationmentioning

confidence: 99%

“…Even though the FPGA logic uses quadratic interpolation and fixed-point numbers, the precision of azimuthal angle reconstruction is almost the same as the software when 𝑝 𝑡 is above 180 MeV. When 𝑝 𝑡 is close to 160 MeV, the interpolated value error becomes large due to the large derivative of the function in equation 3.4.The CDC detector at Belle II experiment[18] has a similar detector architecture to the MDC detector at STCF. Therefore, the results of 2D track reconstruction for the CDC L1 trigger can be used as a reference.…”

mentioning

confidence: 99%

FPGA-based fast track reconstruction for the conceptual design of STCF MDC trigger

et al. 2022

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The Super Tau-Charm Facility (STCF) is a future high luminosity electron-positron collider proposed in China, with a peak luminosity greater than 0.5× 1035 cm2s-1 at center-of-mass energy of 4.0 GeV. The main drift chamber (MDC) is designed as the central tracker, which provides the charged particle information for the level-1 trigger. This paper presents an FPGA-based fast trigger algorithm with the function of finding the charged particle tracks and reconstructing their transverse momentum and azimuthal angle. It adopts a two-stage lookup-table-based pattern matching architecture. The prototype trigger logic was implemented in a Xilinx XCKU060 FPGA. Test results with simulated data suggest that the trigger efficiency of J/Ψ events is better than 96%, and the resolution in the reconstructed track's azimuthal angle at the interaction point is better than 12 mrad. The overall dead time is 24 ns and the total latency is 339 ns. All these performances meet the requirements of the MDC trigger in the current conceptual design stage of STCF project.

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A 3D track finder for the Belle II CDC L1 trigger

Cited by 3 publications

References 7 publications

Applications and Techniques for Fast Machine Learning in Science

Applications and Techniques for Fast Machine Learning in Science

Operation of the Neural z-Vertex Track Trigger for Belle II in 2021 - a Hardware Perspective

FPGA-based fast track reconstruction for the conceptual design of STCF MDC trigger

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