arXiv subjects

Junghwan Goh

Publications and source records attributed to Junghwan Goh.

5 recordsLinked to original sources

arXiv2026

Characterization of the 20-inch Photomultiplier Tubes for RENE Detector

To address the Reactor Antineutrino Anomaly (RAA) observed in neutrino experiments, the Reactor Experiment for Neutrino and Exotics (RENE) has been initiated using a liquid scintillation detector. In this study, we investigate the characteristics of two 20-inch Hamamatsu R12860 photomultiplier tubes (PMTs) intended for installation in the RENE detector. The charge and timing responses of the PMTs were evaluated at both the nominal and target gains expected during actual operation. In particular, gain non-uniformity arising from the large-diameter photocathode with a box-and-line type dynode structure was examined, and the maximum gain variation was measured. The occurrence rate, timing, and charge distributions of late pulses and afterpulses were also investigated to characterize the specific response features of the R12860 PMT. The results reported in this study will aid in the interpretation of signals from the RENE detector and serve as a reference for estimating potential systematic uncertainties in RENE data. Furthermore, these findings are expected to provide valuable information for other experiments employing the same type of PMTs.

physics.ins-det↗

arXiv2025

RENE experiment for the sterile neutrino search using reactor neutrinos

This paper summarizes the details of the Reactor Experiment for Neutrinos and Exotics (RENE) experiment. It covers the detector construction, Monte Carlo (MC) simulation study, and physics expectations. The primary goal of the RENE project is to investigate the sterile neutrino oscillation at $\Delta{m}^{2}_{41}\sim 2\,{\rm{eV}^{2}}$. which overlap with the allowed region predicted by the Reactor Antineutrino Anomaly (RAA). On the other hand, the STEREO and PROSPECT experiments have excluded certain regions of the parameter space with 95 \% confidence level (C.L.), while the joint study conducted by RENO and NEOS suggests possible indications of sterile neutrinos at $\Delta{m}^{2}_{41}\sim2.4\,{\rm{eV}^{2}}$ and $\sim{1.7}{\,\rm{eV}^{2}}$ with sin$^{2}\theta_{41} < 0.01$. Accordingly, a more meticulous investigation of these remaining regions continues to be a scientifically valuable endeavor. This paper reports the technical details of the detector and physics objectives.

hep-ex↗

arXiv2023

Large-Scale Deep Learning for Multi-Jet Event Classification

We report the largest scale deep learning with High Performance Computing (HPC) to physics analysis with the CMS simulation data in proton-proton collisions at 13 TeV. We build a Convolutional Neural Network (CNN) model that takes low-level information as images considering the geometry of the CMS detector and use this model to discriminate \textit{R}-parity violating super symmetry (RPV SUSY) events from the background events with inelastic quantum process from the Standard Model (QCD multi-jet). We compare the classification performance of the CNN method with that of the widely used cut-based method. The signal efficiency (and expected significance) of the CNN method is 1.85 (1.2) times higher than that of the cut-based method. To speed-up the training, the model training is conducted using the Nurion HPC system at the Korea Institute of Science and Technology Information, which is equipped with thousands of parallel \texttt{Xeon Phi} CPUs. Notably, our CNN model shows scalability up to 1024 nodes.

hep-ex↗

arXiv2019

Anti-electron Neutrino Event Selection from Backgrounds Based on Machine Learning

For reactor neutrino experiments including the next--generation experiments will be adopting the liquid scintillator technique, criteria and time to select neutrino--induced inverse beta decay events from the background events need to be established. For higher performance efficiency, we investigated the results of applying a machine learning technique embedded in a standard ROOT package to select IBD signals. To obtain a higher statistics, the signals and background events in a gadolinium-loaded liquid scintillation detector were reproduced by Monte Carlo simulation. We report the efficiencies of neutrino--induced $n-H$ and $n-Gd$ events selection using the machine learning technique.

hep-ex↗

arXiv2012

CMS Resistive Plate Chamber overview, from the present system to the upgrade phase I

University of Sofia, Faculty of Physics, Atomic Physics Department, 5, James Bourchier Boulevard, BG-1164 Sofia, Bulgaria Ghent University, Department of Physics and Astronomy, Proeftuinstraat 86, BE-9000 Ghent, Belgium Bulgarian Academy of Sciences, Inst. for Nucl. Res. and Nucl. Energy, Tzarigradsko shaussee Boulevard 72, BG-1784 Sofia, Bulgaria Peking University, Department of Technical Physics, CN-100 871 Beijing, China Universidad de Los Andes, Apartado Aéreo 4976, Carrera 1E, no. 18A 10, CO-Bogotá, Colombia Academy of Scientific Research and Technology of the Arab Republic of Egypt, 101 Sharia Kasr El-Ain, Cairo, Egypt Panjab University, Department of Physics, Chandigarh Mandir 160 014, India Universita e INFN, Sezione di Bari, Via Orabona 4, IT-70126 Bari, Italy INFN, Laboratori Nazionali di Frascati, PO Box 13, Via Enrico Fermi 40, IT-00044 Frascati, Italy Universita e INFN, Sezione di Napoli, Complesso Univ. Monte S. Angelo, Via Cintia, IT-80126 Napoli, Italy Universita e INFN, Sezione di Pavia, Via Bassi 6, IT-Pavia, Italy Department of Physics and Korea Detector Laboratory, Korea University, Aman-dong 5-ga, Sungbuk-gu, Seou,l Republic of Korea Sungkyunkwan University, Department of Physics 2066, Seobu-ro, Jangan-gu, Suwon, Gyeonggi-Do, Republic of Korea

physics.ins-det↗