arXiv subjects

Tommy Lam

Publications and source records attributed to Tommy Lam.

5 recordsLinked to original sources

arXiv2026

Migration of Belle II TOP Feature Extraction from the Zynq Processing System to PCIe40 Readout PCs

The Belle II Time-of-Propagation (TOP) detector is a key system for charged-particle identification (PID). Although TOP operated successfully during initial Belle II data taking, increasing luminosity and beam-induced background led to more frequent single-event upsets (SEUs) in the radiation-exposed Zynq systems-on-chip (SoCs). Resulting lockups of the embedded processing systems (PSs) interrupted data acquisition. To mitigate this limitation, waveform feature extraction, a critical task of the on-detector Zynq PS, was migrated to the off-detector PCIe40 readout PCs (ROPCs). The new architecture bypasses the PS in the event data path and performs feature extraction outside the detector radiation environment. This change removed SEU-induced PS lockups from the event data path and enabled stable operation of the TOP front-end electronics and the Belle II data-acquisition system under increased luminosity and background conditions. The typical TOP deadtime decreased from about 1% to a level consistent with zero after the final firmware patch, and stable operation was demonstrated at a Level-1 (L1) trigger rate of 30 kHz with a microchannel-plate photomultiplier-tube (MCP-PMT) hit rate of approximately 5 MHz per PMT. This paper describes the migrated architecture, its deployment and validation, and its operational and PID performance.

physics.ins-det↗

arXiv2026

Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML), silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science.

physics.ins-det↗

arXiv2025

Kink Finder at Belle II

We present a track-finding algorithm for the Belle II experiment that specifically targets so-called kinks: signatures of charged particles decaying or scattering in-flight in the detector material, resulting in a sudden and significant change of the particle's flight direction. Our benchmark studies of this Kink Finder show that the reconstruction efficiency for such signatures is about 40%, compared to a value of around 11% for the standard Belle II track-finding algorithm. Our studies also show that the Kink Finder significantly improves the resolution of the secondary track parameters, suppresses the number of cloned tracks, and reduces the PID misidentification rates for kaon and pions.

hep-ex↗

arXiv2025

Muon identification with Deep Neural Network in the Belle II K-Long and Muon detector

Muon identification is crucial for elementary particle physics experiments. At the Belle II experiment, muons and pions with momenta greater than 0.7 GeV/c are distinguished by their penetration ability through the $K_L$ and Muon (KLM) sub-detector, which is the outermost sub-detector of Belle II. In this paper, we first discuss the possible room for $\mu/\pi$ identification performance improvement and then present a new method based on Deep Neural Network (DNN). This DNN model utilizes the KLM hit pattern variables as the input and thus can digest the penetration information better than the current algorithm. We test the new method in simulation and find that the pion fake rate (specificity) is reduced from 4.1% to 1.6% at a muon efficiency (recall) of 90%.

hep-ex↗

arXiv2024

Development and commissioning of a new readout system for the gas flow of the Belle II $K_L^0$ and muon detector

We have designed and commissioned a new readout board to detect photosensor signals from gas-bubbler panels to continuously monitor the gas flow through the resistive plate chambers of the $K_L^0$ and muon detector of Belle II. The gas flow measurements have been integrated into Belle II's alarm system. The bubbler-monitoring system was first employed during the February 2024 to July 2024 Belle II data-taking period.

hep-ex↗