arXiv subjects

Seokju Chung

Publications and source records attributed to Seokju Chung.

5 recordsLinked to original sources

arXiv2026

Readout electronics for SUBMET

A dedicated data acquisition (DAQ) system has been developed for the SUB-Millicharge ExperimenT (SUBMET) at the Japan Proton Accelerator Research Complex (J-PARC), a search for particles carrying a fractional electric charge $Q = εe$ with $ε$ below $\mathcal{O}(10^{-3})$, hereafter referred to as millicharged particles (mCPs). Because such particles are expected to produce at most a few scintillation photons, the system is optimized for single-photoelectron detection from the photomultiplier tubes (PMTs), combining high-speed waveform digitization with precise timing. To capture eight consecutive proton bunches of the 30 GeV J-PARC beam within a single trigger, the eight channels of the Domino Ring Sampler 4 (DRS4) chip are cascaded in groups of four to form two readout inputs, each sampling 4096 points continuously at 820.5 MHz over an effective time window of 5 us. After calibration, timing differences between channels are within 1 ns on the same DRS4 chip, 2 ns on the same board, and 8 ns across different boards, well within the 30 ns coincidence window of the experiment. The front-end electronics achieve an RMS noise below 0.4 mV. The baseline is deliberately offset upward such that the negative-going pulses span a larger fraction of the digitizer range, improving voltage resolution and dynamic range. A trigger control board aggregates data from multiple readout boards and sustains the data-transfer rate required for beam operation. The measured performance confirms that the DAQ system meets the timing, noise, and throughput requirements of the experiment.

hep-ex↗

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↗

arXiv2026

Real-time Anomaly Detection for Liquid Argon Time Projection Chambers

We present a real-time anomaly detection framework for liquid argon time projection chambers (LArTPCs), targeting applications in particle physics experiments such as the Short Baseline Near Detector or the future Deep Underground Neutrino Experiment. These experiments employ detectors that generate and stream high-resolution but sparse images of neutrino and other particle interactions. Our approach utilizes anomaly detection with autoencoders, compressed through knowledge distillation, to enable the detection of anomalous signals in the data through efficient inference on resource-constrained hardware. The framework is targeted for deployment on computing platforms equipped with field-programmable gate arrays, GPUs, or CPUs, allowing low-latency selection of relevant activity directly from the raw detector data stream. We demonstrate that our approach is suitable for the detection and localization of anomalously "high-multiplicity" activity, and outline promising applications for LArTPC online data filtering and triggering.

hep-ex↗

arXiv2025

Afterpulse prediction for SUBMET experiment

The SUB-Millicharge ExperimenT (SUBMET) investigates an unexplored parameter space of millicharged particles with mass $m_χ< $ 1.6 GeV/c$^2$ and charge $Q_χ< 10^{-3}e$. The detector consists of an Eljen-200 plastic scintillator coupled to a Hamamatsu Photonics R7725 photomultiplier tube (PMT). PMT afterpulses, delayed pulses produced after an energetic pulse, have been observed in the SUBMET readout system, especially following primary pulses with a large area. We present a prediction method for afterpulse rates based on measurable parameters, which reproduces the observed rate with approximately 20\% precision. This approach enables a better understanding of afterpulse contributions and, consequently, improves the reliability of background predictions.

physics.ins-det↗

arXiv2025

Design and Mechanical Integration of Scintillation Modules for SUB-Millicharge ExperimenT (SUBMET)

We present a detailed description of the detector design for the SUB-Millicharge ExperimenT (SUBMET), developed to search for millicharged particles. The experiment probes a largely unexplored region of the charge-mass parameter space, focusing on particles with mass $m_χ< 1.6~\textrm{GeV}/c^2$ and electric charge $Q < 10^{-3}e$. The detector has been optimized to achieve high sensitivity to interactions of such particles while maintaining effective discrimination against background events. We provide a comprehensive overview of the key detector components, including scintillation modules, photomultiplier tubes, and the mechanical support structure.

physics.ins-det↗