arXiv subjects

Kihong Park

Publications and source records attributed to Kihong Park.

4 recordsLinked to original sources

arXiv2026

Development and Initial Performance of an Upgraded NaI(Tl) Crystal Encapsulation for COSINE-100U

The COSINE-100 experiment was designed to test the DAMA/LIBRA annual-modulation claim using low-background NaI(Tl) detectors. For the COSINE-100U upgrade, we developed a new crystal-encapsulation system to increase light-collection efficiency while preserving long-term detector stability, thereby improving sensitivity to low-mass dark matter. The upgraded design eliminates the quartz optical windows used in COSINE-100 and directly couples the photomultiplier tubes (PMTs) to the crystal end faces through 2-mm-thick silicone optical pads, thereby reducing the number of optical interfaces. For the larger crystals, the crystal edges were beveled to guide scintillation light more efficiently onto 3-inch high-quantum-efficiency PMTs. The performance study uses 2462~h (102.6~days) of room-temperature COSINE-100U data and, for direct background comparisons, reference COSINE-100 data acquired near the end of operation. 698~h (29.1~days) of COSINE-100 data acquired near the end of operation in March 2023. All eight crystals showed higher light yields than in COSINE-100, with values ranging from 15.8 to 27.7~p.e./keV; six crystals exceeded 20~p.e./keV. The measured bulk-$α$ rates were lower than the COSINE-100 values and consistent with the expected time evolution of internal $^{210}$Pb, while the 1--2-MeV surface-$α$ rates were substantially reduced. The upgrade also restored two crystals that had previously been excluded from the COSINE-100 physics analysis because of poor optical performance. Independent validation tests demonstrated that the encapsulation remains mechanically robust and optically stable during long-term immersion in liquid scintillator at low temperature. This paper presents the encapsulation design, the room-temperature detector performance, and the reduction in surface-related backgrounds achieved at the Yemilab facility.

physics.ins-det↗

arXiv2026

Validation of the COSINE-100U NaI(Tl) Encapsulation for Low-Temperature Operation in Liquid Scintillator

The COSINE-100U (upgrade) will enhance the sensitivity of the COSINE-100 dark matter search by operating the detector array immersed in liquid scintillator (LS) at $-30^oC$. To validate the detector design for these conditions, we constructed a module using the COSINE-100U encapsulation and performed a dedicated long-term stability study. The module was first monitored at room temperature for ~110 days in air, followed by a one-week immersion in LAB-based LS to verify initial compatibility. Upon confirming stable optical performance, the temperature was lowered to $-33^oC$. During approximately 150 days of continuous operation at low temperature, we observed no degradation in performance. These results demonstrate the chemical and mechanical robustness of the encapsulation, confirming its suitability for the COSINE-100U physics run.

physics.ins-det↗

arXiv2025

Joint Trajectory and Resource Optimization for HAPs-SAR Systems with Energy-Aware Constraints

This paper investigates the joint optimization of trajectory planning and resource allocation for a high-altitude platform stations synthetic aperture radar (HAPs-SAR) system. To support real-time sensing and conserve the limited energy budget of the HAPs, the proposed framework assumes that the acquired radar data are transmitted in real time to a ground base station for SAR image reconstruction. A dynamic trajectory model is developed, and the power consumption associated with radar sensing, data transmission, and circular flight is comprehensively analyzed. In addition, solar energy harvesting is considered to enhance system sustainability. An energy-aware mixed-integer nonlinear programming (MINLP) problem is formulated to maximize radar beam coverage while satisfying operational constraints. To solve this challenging problem, a sub-optimal successive convex approximation (SCA)-based framework is proposed, incorporating iterative optimization and finite search. Simulation results validate the convergence of the proposed algorithm and demonstrate its effectiveness in balancing SAR performance, communication reliability, and energy efficiency. A final SAR imaging simulation on a 9-target lattice scenario further confirms the practical feasibility of the proposed solution.

eess.SY↗

arXiv2016

Unified Depth Prediction and Intrinsic Image Decomposition from a Single Image via Joint Convolutional Neural Fields

We present a method for jointly predicting a depth map and intrinsic images from single-image input. The two tasks are formulated in a synergistic manner through a joint conditional random field (CRF) that is solved using a novel convolutional neural network (CNN) architecture, called the joint convolutional neural field (JCNF) model. Tailored to our joint estimation problem, JCNF differs from previous CNNs in its sharing of convolutional activations and layers between networks for each task, its inference in the gradient domain where there exists greater correlation between depth and intrinsic images, and the incorporation of a gradient scale network that learns the confidence of estimated gradients in order to effectively balance them in the solution. This approach is shown to surpass state-of-the-art methods both on single-image depth estimation and on intrinsic image decomposition.

cs.CV↗