Experimental Particle Physics at Kyung hee University

PI: Junghwan Goh (Space21-607, Kyung Hee University / jhgoh -at- khu.ac.kr)
- 2022-present: Associate professor / Department of Physics, Kyung Hee University
  - 2021-present: Science Information Convergence, Kyung Hee University
  - 2020-2024: Department of Big Data Analytics, Kyung Hee University
  - 2018-2022: Assistant professor / Department of Physics, Kyung Hee University
- 2016-2018: Research Professor at Hanyang University
  - 2017-present CMS RPC Detector Performance Group coordinator, CERN/LHC
  - 2016-2017 CMS RPC Upgrade coordinator (deputy), CERN/LHC
- 2014-2016: Postdoc at SungKyunKwan University / Korea CMS at CERN
- 2014: Ph.D Physics at SungKyunKwan University

Team Members (Space21-904, Kyung Hee University)
- Postdoc: S. Yang
- Ph.D: C. Yoo(2025.02), W. Hwang, J. Shin
- Master: J. Oh(2025.02)

Alumni
- Researcher: R. Rabadan(2021)
- Master: H. Hwang(2024.08), H. Park(2023.02), H. Jeong(2023.02), S. Lee(off), D. Nam(off)
- Undergrad: D. Bae(2020)

Required skills
- Research: Experimental particle physics, Particle detectors, Machine Learning, Deep Learning
- Software & computing: C++, python, ROOT, Geant4, numpy, pytorch, tensorflow, CUDA, FPGA, HLS, bash, html, javascript, php
- System administration: Linux, Grid / HTC, HPC, condor, pbs(torque), kubernetes, docker, singularity

LHC/CMS experiment
- Top quark reconstruction using Deep Learning
- Search for Flavour Changing Neutral Currents
- RPC detector efficiency measurement, Data quality monitoring
- RPC software release validation
Neutrino experiments
- Search for sterile neutrinos at JSNS2 experiment using Machine Learning
- Pulse shape discrimination using Deep Learning
- Reactor neutrino experiment closest to the core
Computing/Deep Learning
- Large scale deep learning with the supercomputers
  - HEP-CNN github
- Accelerated Deep Learning using the FPGAs
- KCMS computing resources

Local servers (Monitoring page):
- Main server: Xeon Silver 4210R (k8s master & file server & auth.)
- High perf nodes: EPYC 7702, EPYC 7302 (Alveo U200 FPGA)
- GPU: Ryzen Threadripper 399X (GPUx4), Xeon E5-1650v4 (legacy GPU)
Networking
- Kreonet (1G KISTI Seoul - KHU Dept. of Physics)
- Internal networks (Dell EMC X4012 12 SFP/10G, HP 1420-24g/1G)
External computing resources
- CMS: CERN (lxplus), KISTI GSDC, etc
- JSNS: KEK (kekcc)
- KISTI supercomputing center