Hit Matching between the MTD and ME0 System for Muon Identification in the High-η Endcap region
권도훈 (서울시립대학교) 파일 보기
This study focus on hit matching between MTD and ME0 in high- η region of the
CMS Phase-2 upgrade. The goal is to match MTD and ME0 hits based on η–φ coordinates and verify the hits originate from the same muon using precise timing and position information.
Resolution of Standalone Muon Reconstruction with and without GE1/1
최효규 (서울시립대학교) 파일 보기
We evaluated the performance improvement of standalone muon reconstruction by incorporating GE1/1 triple-GEM rechits in CMS .Using MC samples, we compared the p_T, eta, and phi resolutions with and without GEM hits. The results show that including GE1/1 improves p_T resolution by about 10%, particularly in the forward region. This demonstrates the value of GE1/1 information for accurate muon reconstruction under high-pileup conditions at the HL-LHC.
Exploring Non-Thermal Dark Matter through Monojet Events
전우철 (서울시립대학교 물리학과) 파일 보기
Although the Standard Model (SM) successfully describes a wide range of particle interactions, it does not account for key phenomena such as dark matter and the matter–antimatter asymmetry. Various Beyond the Standard Model (BSM) scenarios have been proposed to address these limitations. This study investigates a BSM model featuring baryon-number-violating processes and a dark matter candidate. The model introduces two iso-singlet color-triplet scalars X at the TeV scale and one singlet Majorana fermion with a mass of approximately that of the proton. The fermion serves as the dark matter candidate, manifesting as missing transverse energy. The monojet channel is studied here.
Deep Learning for the Level-1 ME0 Triggerin the CMS Experiment
허우현 (서울시립대학교 물리학과) 파일 보기
The ME0 detector is a Gas Electron Multiplier detector which will be installed as part of the phase-2 upgrade of the CMS. The ME0 is located at the very forward of the endcap area in the CMS as part of the muon system and is the only muon detector that covers |\eta| > 2.4. Due to its extremely high background environment, keeping the minbias rate low while maintaining high efficiency is very challenging. In this study, we aim to improve the performance of the Level-1 Trigger algorithm for ME0 - especially the minbias rate - by applying machine learning techniques.
