arXiv subjects

Hyeonjae Kim

Publications and source records attributed to Hyeonjae Kim.

3 recordsLinked to original sources

arXiv2026

Magnetic characterization of electronic components for portable atomic sensors using a zero-field optically pumped magnetometry platform

We present a zero-field optically pumped magnetometry platform for magnetic characterization of a photodetector (PD) board and a resistance temperature detector (RTD) used in a portable atomic magnetometer. For each component, the static magnetic field along the measurement axis is determined from the shift in the center of the dispersive response, while response distortion caused by off-axis magnetic-field components is assessed from the absorptive admixture. Magnetic-field noise is evaluated based on the quadrature difference in the amplitude spectral density (ASD). The platform achieved a -3 dB bandwidth of 71.5 Hz and a median ASD of 57.6 $\mathrm{fT}/\sqrt{\mathrm{Hz}}$ over 20-70 Hz. For the unpowered PD board, static magnetic fields of approximately 8 nT in magnitude and opposite signs were measured in the front- and back-facing orientations. The back-facing orientation also exhibited a degraded dispersive response consistent with magnetic-field inhomogeneity. The magnetic-noise contributions associated with the PD board were estimated at 36.0 and 63.2 $\mathrm{fT}/\sqrt{\mathrm{Hz}}$ in the front- and back-facing orientations, respectively. By contrast, operation of the RTD readout generated a static magnetic field of -0.6 nT, with no measurable response degradation or additional magnetic-field noise. These results provide guidance for the design and placement of electronic components in portable atomic magnetometers targeting sensitivities below 0.1 $\mathrm{pT}/\sqrt{\mathrm{Hz}}$.

physics.atom-ph↗

arXiv2026

Continuous Degradation Modeling via Latent Flow Matching for Real-World Super-Resolution

While deep learning-based super-resolution (SR) methods have shown impressive outcomes with synthetic degradation scenarios such as bicubic downsampling, they frequently struggle to perform well on real-world images that feature complex, nonlinear degradations like noise, blur, and compression artifacts. Recent efforts to address this issue have involved the painstaking compilation of real low-resolution (LR) and high-resolution (HR) image pairs, usually limited to several specific downscaling factors. To address these challenges, our work introduces a novel framework capable of synthesizing authentic LR images from a single HR image by leveraging the latent degradation space with flow matching. Our approach generates LR images with realistic artifacts at unseen degradation levels, which facilitates the creation of large-scale, real-world SR training datasets. Comprehensive quantitative and qualitative assessments verify that our synthetic LR images accurately replicate real-world degradations. Furthermore, both traditional and arbitrary-scale SR models trained using our datasets consistently yield much better HR outcomes.

cs.CV↗

arXiv2025

CLIMATEAGENT: Multi-Agent Orchestration for Complex Climate Data Science Workflows

Climate science demands automated workflows to transform comprehensive questions into data-driven statements across massive, heterogeneous datasets. However, generic LLM agents and static scripting pipelines lack climate-specific context and flexibility, thus, perform poorly in practice. We present ClimateAgent, an autonomous multi-agent framework that orchestrates end-to-end climate data analytic workflows. ClimateAgent decomposes user questions into executable sub-tasks coordinated by an Orchestrate-Agent and a Plan-Agent; acquires data via specialized Data-Agents that dynamically introspect APIs to synthesize robust download scripts; and completes analysis and reporting with a Coding-Agent that generates Python code, visualizations, and a final report with a built-in self-correction loop. To enable systematic evaluation, we introduce Climate-Agent-Bench-85, a benchmark of 85 real-world tasks spanning atmospheric rivers, drought, extreme precipitation, heat waves, sea surface temperature, and tropical cyclones. On Climate-Agent-Bench-85, ClimateAgent achieves 100% task completion and a report quality score of 8.32, outperforming GitHub-Copilot (6.27) and a GPT-5 baseline (3.26). These results demonstrate that our multi-agent orchestration with dynamic API awareness and self-correcting execution substantially advances reliable, end-to-end automation for climate science analytic tasks.

cs.LG↗