arXiv subjects

Jason Sang Hun Lee

Publications and source records attributed to Jason Sang Hun Lee.

9 recordsLinked to original sources

arXiv2026

Test-Beam Performance of the AstroPix Silicon Sensor for Imaging Calorimetry

AstroPix is a high-voltage CMOS HVCMOS monolithic active pixel sensor MAPS developed for future space-based gamma-ray missions. It is also a candidate technology for the imaging layer of the Barrel Imaging Calorimeter BIC in the ePIC experiment at the future Electron-Ion Collider EIC. We report the first AstroPix test-beam results obtained at the KEK Photon Factory Advanced Ring PF-AR and the CERN Proton Synchrotron PS T10 beam line in 2025, using the third prototype AstroPix-v3. AstroPix-v3 sensors were operated as both standalone tracking layers and imaging layers interleaved with prototype lead/scintillating-fiber Pb/SciFi calorimeter modules, using electron and hadron beams in the few-GeV/c momentum range. Event synchronization between the continuous readout of AstroPix-v3 and the trigger-based readout of the Pb/SciFi calorimeter was achieved using a common timestamp. The AstroPix-v3 sensors exhibit stable performance, reaching a maximum hit efficiency of 68 percent at a bias voltage of -400 V under pion-dominated beam conditions. When combined with the Pb/SciFi calorimeter, the AstroPix layers successfully capture the development of electromagnetic showers. Using Cherenkov-based particle identification, electron-induced events exhibit significantly higher hit multiplicities and broader spatial distributions than pion-induced events, thereby providing clear discrimination between electromagnetic and hadronic showers. These results demonstrate that AstroPix-v3 provides effective, high-granularity imaging of shower development and is well suited as an imaging layer in future calorimeter systems for both collider and space-based experiments.

physics.ins-det↗

arXiv2026

Cold Neutron Imaging and Efficiency Measurements with a Boron-10 Coated Double-GEM Detector

A ${}^{10}\mathrm{B}$-coated double-GEM neutron detector (BGEM) was developed as a ${}^{3}\mathrm{He}$-free cold-neutron beamline detector using a single $\mathrm{B}_{4}\mathrm{C}$ converter cathode and a 512-channel APV25 orthogonal-strip readout over an active area of $10 \times 10~\mathrm{cm}^{2}$. The detector was tested at the HANARO Bio-REF beamline with a monochromatic $4.5~\mathring{\mathrm{A}}$ beam ($E_{n}=4.03~\mathrm{meV}$). The absolute detection efficiency relative to a ${}^{6}\mathrm{Li}$-based Ce:LiCAF reference detector was $\varepsilon_{\mathrm{BGEM}}=(8.69 \pm 0.20)\%$ (stat.). The pulse-height spectrum was qualitatively consistent with Geant4 energy-deposition simulations, and Cd-mask imaging yielded a Gaussian-equivalent edge-spread width of $σ= 555 \oplus 102~μ\mathrm{m}$. These results establish a cold-neutron beamline benchmark for a single-converter BGEM detector with full-strip APV25 readout.

physics.ins-det↗

arXiv2025

Improving the Direct Determination of $|V_{ts}|$ using Deep Learning

An $s$-jet tagging approach to determine the Cabibbo-Kobayashi-Maskawa matrix component $|V_{ts}|$ directly in the dileptonic final state events of the top pair production in proton-proton collisions has been previously studied by measuring the branching fraction of the decay of one of the top quarks by $t \to sW$. The main challenge is improving the discrimination performance between strange jets from top decays and other jets. This study proposes novel jet discriminators, called DISAJA, using a Transformer-based deep learning method. The first model, DISAJA-H, utilizes multi-domain inputs (jets, leptons, and missing transverse momentum). An additional model, DISAJA-L, further improves the setup by using lower-level jet constituent information, rather than the high-level clustered information. DISAJA-L is a novel model that combines low-level jet constituent analysis with event classification using multi-domain inputs. The model performance is evaluated via a CMS-like LHC Run 2 fast simulation by comparing various statistical test results to those from a Transformer-based jet classifier which considers only the individual jets. This study shows that the DISAJA models have significant performance gains over the individual jet classifier, and we show the potential of the measurement during Run 3 of the LHC and the HL-LHC.

hep-ph↗

arXiv2023

Zero-Permutation Jet-Parton Assignment using a Self-Attention Network

In high-energy particle physics events, it can be advantageous to find the jets associated with the decays of intermediate states, for example, the three jets produced by the hadronic decay of the top quark. Typically, a goodness-of-association measure, such as a $χ^2$ related to the mass of the associated jets, is constructed, and the best jet combination is found by optimizing this measure. As this process suffers from a combinatorial explosion with the number of jets, the number of permutations is limited by using only the $n$ highest $p_T$ jets. The self-attention block is a neural network unit used for the neural machine translation problem, which can highlight relationships between any number of inputs in a single iteration without permutations. In this paper, we introduce the Self-Attention for Jet Assignment (SaJa) network. SaJa can take any number of jets for input and outputs probabilities of jet-parton assignment for all jets in a single step. We apply SaJa to find jet-parton assignments of fully-hadronic $t\bar{t}$ events to evaluate the performance. We show that SaJa achieves better performance than a likelihood-based approach.

hep-ex↗

arXiv2022

Dual-Readout Calorimetry for Future Experiments Probing Fundamental Physics

In this White Paper for the 2021 Snowmass process, we detail the status and prospects for dual-readout calorimetry. While all calorimeters allow estimation of energy depositions in their active material, dual-readout calorimeters aim to provide additional information on the light produced in the sensitive media via, for example, wavelength and polarization, and/or a precision timing measurements, allowing an estimation of the shower-by-shower particle content. Utilizing this knowledge of the shower particle content may allow unprecedented energy resolution for hadronic particles and jets and new types of particle flow algorithms. We also discuss the impact continued development of this kind of calorimetry could have on precision on Higgs boson property measurements at future colliders.

physics.ins-det↗

arXiv2021

Measuring $|V_{ts}|$ directly using strange-quark tagging at the LHC

The Cabibbo-Kobayashi-Maskawa (CKM) element $V_{ts}$, representing the coupling between the top and strange quarks, is currently best determined through fits based on the unitarity of the CKM matrix, and measured indirectly through box-diagram oscillations, and loop-mediated rare decays of the $B$ or $K$ mesons. It has been previously proposed to use the tree level decay of the $t$ quark to the $s$ quark to determine $|V_{ts}|$ at the LHC, which has become a top factory. In this paper, we extend the proposal by performing a detailed analysis of measuring $t \to sW$ in dileptonic $t\bar{t}$ events. In particular, we perform detector response simulation, including the reconstruction of $K_S$, which are used for tagging jets produced by $s$ quarks against the dominant $t \to bW$ decay. We show that it should be possible to exclude $|V_{ts}| = 0$ at 6.0$σ$ with the expected High Luminosity LHC luminosity of 3000 fb$^{-1}$.

hep-ph↗

arXiv2020

Quark-Gluon Jet Discrimination Using Convolutional Neural Networks

Currently, newly developed artificial intelligence techniques, in particular convolutional neural networks, are being investigated for use in data-processing and classification of particle physics collider data. One such challenging task is to distinguish quark-initiated jets from gluon-initiated jets. Following previous work, we treat the jet as an image by pixelizing track information and calorimeter deposits as reconstructed by the detector. We test the deep learning paradigm by training several recently developed, state-of-the-art convolutional neural networks on the quark-gluon discrimination task. We compare the results obtained using various network architectures trained for quark-gluon discrimination and also a boosted decision tree (BDT) trained on summary variables.

hep-ex↗

arXiv2020

Quark Gluon Jet Discrimination with Weakly Supervised Learning

Deep learning techniques are currently being investigated for high energy physics experiments, to tackle a wide range of problems, with quark and gluon discrimination becoming a benchmark for new algorithms. One weakness is the traditional reliance on Monte Carlo simulations, which may not be well modelled at the detail required by deep learning algorithms. The weakly supervised learning paradigm gives an alternate route to classification, by using samples with different quark--gluon proportions instead of fully labeled samples. The paradigm has, therefore, huge potential for particle physics classification problems as these weakly supervised learning methods can be applied directly to collision data. In this study, we show that realistically simulated samples of dijet and Z+jet events can be used to discriminate between quark and gluon jets by using weakly supervised learning. We implement and compare the performance of weakly supervised learning for quark--gluon jet classification using three different machine learning methods: the jet image-based convolutional neural network, the particle-based recurrent neural network and the feature-based boosted decision tree.

hep-ph↗

arXiv2020

Neutron Detection using a Gadolinium-Cathode GEM Detector

A gas electron multiplier (GEM) detector with a gadolinium cathode has been developed to explore its potential application as a neutron detector. It consists of three standard-sized ($10\times 10$ cm${}^{2}$) GEM foils and a thin gadolinium plate as the cathode, which is used as a neutron converter. The neutron detection efficiencies were measured for two different cathode setups and for two different drift gaps. The thermal neutron source at the Korea Research Institute of Standards and Science (KRISS) was used to measure the neutron detection efficiency. Based on the neutron flux measured by KRISS, the neutron detection efficiency of our gadolinium GEM detector was $4.630 \pm 0.034(stat.) \pm 0.279(syst.) \%$.

physics.ins-det↗