arXiv subjects

Minhyeok Kim

Publications and source records attributed to Minhyeok Kim.

6 recordsLinked to original sources

arXiv2026

Mechanisms of Electrostatic Charge Formation and Retention in Lunar Regolith

As the Artemis program advances toward the lunar south pole and permanently shadowed regions (PSRs), understanding lunar charging is increasingly important for protecting astronauts, robotic systems, instruments, and infrastructure. Persistent darkness, cryogenic temperatures, low regolith conductivity, and long charge-relaxation times may allow energetic-particle-induced charge to accumulate beneath the surface. This Technical Memorandum addresses a key unresolved question: how is electrostatic charge generated, separated, retained, and accumulated within lunar regolith? Existing models predict that solar energetic particles and galactic cosmic rays may produce subsurface electric fields approaching dielectric breakdown thresholds, but the microscopic connection between incident particles and macroscopic volumetric charge sources remains unclear. Energy deposition alone does not determine retained charge. Incident particles and their secondary particles may stop, implant, backscatter, transmit, recombine, become trapped, or escape. This memorandum therefore defines the required microscopic input as the average signed retained-charge distribution per unit depth and incident particle, resolved by particle species and energy. Combined with incident flux and energy spectra, this response provides a depth-dependent volumetric charge-source rate that can be coupled with charge continuity, conduction, dielectric relaxation, and Poisson's equation. Key uncertainties include the effects of mineralogy, grain and pore geometry, temperature, and pre-existing potential on charge retention. Particle-resolved modeling and cryogenic high-vacuum irradiation experiments are needed to constrain these processes and assess subsurface electric fields, dielectric breakdown, dust transport, contamination, and charge-mitigation requirements for sustained lunar polar exploration.

astro-ph.IM↗

arXiv2026

Bayesian Repulsive Mixture Modeling with Matérn Point Processes

Mixture models are a standard tool in statistical analyses, widely used for density modeling and model-based clustering. In this work, we propose a Bayesian mixture model with repulsion between mixture components. Such repulsion helps address the problem of overlapping or poorly separated clusters, and assists with model interpretibility and robustness. Our modeling approach introduces repulsion via a generalized Matérn type-III repulsive point process model, and proceeds by applying a dependent sequential thinning scheme to a latent Poisson point process. A key feature of our model is that in contrast to most existing approaches to modeling repulsion, efficient posterior inference is possible via a Gibbs sampler, one that exploits the latent Poisson of our problem. This novel sampler also allows posterior inference over the number of clusters, and is of independent interest even in standard clustering applications without repulsion. We demonstrate the utility of the proposed method on a number of synthetic and real-world problems.

stat.ME↗

arXiv2026

Exact Gibbs sampling for stochastic differential equations with gradient drift and constant diffusion

Stochastic differential equations (SDEs) are an important class of time-series models, used to describe stochastic systems evolving in continuous time. Simulating paths from these processes, particularly after conditioning on noisy observations of the latent path, remains a challenge. Existing methods often introduce bias through time-discretization, require involved rejection sampling or debiasing schemes or are restricted to a narrow family of diffusions. In this work, we propose an exact Markov chain Monte Carlo (MCMC) sampling algorithm that is applicable to a broad subset of all SDEs with unit diffusion coefficient; after suitable transformation, this includes an even larger class of multivariate SDEs and most 1-d SDEs. We develop a Gibbs sampling framework that allows exact MCMC for such diffusions, without any discretization error. We demonstrate how our MCMC methodology requires only fairly straightforward simulation steps. Our framework can be extended to include parameter simulation, and allows tools from the Gaussian process literature to be easily applied. We evaluate our method on synthetic and real datasets, demonstrating superior performance to particle MCMC approaches.

stat.CO↗

arXiv2025

Sensitivity threshold defines the optimal spin subset for ensemble quantum sensing

Inhomogeneous broadening and spatial gradients in control fields inevitably produce large variations in characteristic sensitivity across spin ensembles. We derive an analytic expression for the sensitivity of an inhomogeneous ensemble and introduce a sensitivity threshold that identifies the optimal subset of spins. For both pulsed and continuous-wave magnetometry, the optimal ensembles deliver up to an eightfold improvement over conventional schemes relying on nominally uniform regions of the ensemble. We demonstrate phase-only digital holography to implement the optimal ensembles and show that the measured illumination non-uniformity limits the sensitivity by 14%. This approach enables optimal quantum sensing in optically scattering environments.

quant-ph↗

arXiv2025

Autonomously Designed Pulses for Precise, Site-Selective Control of Atomic Qubits

Quantum computers based on cold-atom arrays offer long-lived qubits with programmable connectivity, yet their progress toward fault-tolerant operation is limited by the relatively low fidelity of site-selective local control. We introduce an artificial-intelligence (AI) framework that overcomes this limitation. Trained on atom-laser dynamics, a deep neural network autonomously designs composite pulses that improve local control fidelities tenfold while remaining compatible with existing control hardware. We further demonstrate the robustness of these pulses against optical aberrations and beam misalignment. This approach establishes AI-trained pulse compilation for high-fidelity qubit control and can be readily extended to other atom-like platforms, such as trapped ions and solid-state color centers.

quant-ph↗

arXiv2024

The Mechanical Behavior of Macroscale Single-crystal Graphene

Despite extensive microscale studies, the macroscopic mechanical properties of monolayer graphene remain underexplored. Here, we report the Young's modulus ($E$ = 1.11 $\pm$ 0.04 TPa), tensile strength ($σ$ = 27.40 $\pm$ 4.36 GPa), and failure strain ($ε_f$ = 6.01 $\pm$ 0.92 %) of centimeter-scale single-crystal monolayer graphene (SCG) 'dog bone' samples with edges aligned along the zigzag (zz) direction, supported by an ultra-thin polymer (polycarbonate) film. For samples with edges along the armchair (ac) direction, we obtain $E$ = 1.01 $\pm$ 0.10 TPa, $σ$ = 20.21 $\pm$ 3.22 GPa, $ε_f$ = 3.69 $\pm$ 0.38 %, and for chiral samples whose edges were between zz and ac, we obtain $E$= 0.75 $\pm$ 0.12 TPa, $σ$ = 23.56 $\pm$ 3.42 GPa, and $ε_f$ = 4.53 $\pm$ 0.40 %. The SCG is grown on single crystal Cu(111) foils by chemical vapor deposition (CVD). We used a home-built 'float-on-water' (FOW) tensile testing system for tensile loading measurements that also enabled in situ crack observation. The quantized fracture mechanics (QFM) analysis predicts an edge defect size from several to tens of nanometers based on chirality and notch angle. Through Weibull analysis and given that the fatal defects are confined on the edges of macroscale samples, we projected strength ranging from 13.67 to 18.43 GPa for an A4-size SCG according to their chirality. The exceptional mechanical performance of macroscale single crystal graphene (SCG) paves the way for its widespread use in a very wide variety of applications.

cond-mat.mes-hall↗