arXiv subjects

Sang Hyun Lee

Publications and source records attributed to Sang Hyun Lee.

At least 19 recordsLinked to original sources

arXiv2026

3.5-meter Segmented-Mirror Robotic Space Telescope Mission White Paper I. Overall Architecture and Scientific Mission

We present the preliminary science concept and mission architecture of a 3.5-meter segmented-mirror robotic space telescope currently under study. The observatory is conceived as a versatile platform supporting wide-field cosmology and galaxy evolution, direct imaging and characterization of nearby planetary systems, time-domain and multi-messenger observations, compact-object studies, and Solar-System small-body science. These programs share requirements for angular resolution, photometric stability, rapid target acquisition, spectroscopy, and long-term observing efficiency. The telescope employs an 18-segment 3.5-meter primary mirror for high-angular-resolution imaging from the near-ultraviolet through the optical and near-infrared. The current baseline covers 0.2--1.5 $μ$m, with the wavelength for diffraction-limited performance to be set by the final wavefront-error budget. Wide-field imaging is intended for deep surveys, precision photometry, and repeated monitoring over approximately 10' $\times$ 10' to 30' $\times$ 30'. Spectroscopic modes with $R \sim 1000$ and higher-resolution options approaching $R \sim 5000$ are being considered for galaxy surveys, transient classification, compact-object spectroscopy, and targeted studies. A dedicated coronagraph is also being studied for direct observations of nearby exoplanetary systems, with a current raw-contrast goal of order $10^{-8}$ and further gains expected from calibration and post-processing. Candidate mission configurations include the Sun--Earth L2 region and alternative Earth orbits, with the final choice driven by science performance, thermal stability, communications, operations, and mission cost. This paper defines the current science requirements, baseline technical configuration, and engineering trade space for further development of the 3.5mST concept.

astro-ph.IM↗

arXiv2026

3.5-meter Segmented-Mirror Robotic Space Telescope Mission White Paper II. Key Scientific Mission: Wide-Field Cosmology and Galaxy Evolution

The 3.5-meter Segmented-Mirror Robotic Space Telescope uses an image slicer for all spectroscopic observations. The planning baseline uses $R \simeq 1000$ for the wide survey and retains selectable $R \simeq 5000$ bands for precision line measurements. The central science case is a dense emission-line galaxy redshift survey for baryon acoustic oscillations and redshift-space distortions. Supernova and quasar programs exploit the stability, multiplexing, and repeatability of space operations. The supernova tier measures rest-frame U and near-ultraviolet magnitudes that separate optical twins at subgroup precision to $z \simeq 0.9$--$1.1$ in standard visits and to $z \simeq 1.3$--$1.5$ in ten-hour stacks. Every wide-survey tile receives three spectroscopic orientations, and a joint scene reconstruction uses their different overlap geometries to recover the spectra. The flagship survey covers 100--300 deg$^2$ and targets $10^6$--$3 \times 10^6$ emission-line galaxies. A deep pencil-beam tier and a supernova time-domain tier complement the wide survey. The same observations provide a census of ultra-diffuse and low-surface-brightness galaxies, map intracluster light, and test cold, self-interacting, and fuzzy dark matter through dwarf-galaxy structure and low-mass halo abundance.

astro-ph.IM↗

arXiv2026

3.5-meter Segmented-Mirror Robotic Space Telescope Mission White Paper III. Key Scientific Mission: Exoplanet Science with a Coronagraph

This volume defines the exoplanet science program enabled by the dedicated high-contrast coronagraph in the baseline science payload of the 3.5-meter Segmented-Mirror Robotic Space Telescope. The observatory architecture incorporates the optical interfaces, wavefront sensing and control, pointing stability, and operations software required for coronagraphic observations from the outset. The observing strategy gives priority to the nearest stellar systems because they provide the most accessible laboratories for planetary exploration and the most likely destinations of future interstellar missions. The diffraction limit sets a reflected-light horizon of roughly 10--15 pc for planets at 1 AU and roughly 50--80 pc for Jupiter analogs. Within those horizons, the telescope can image nearby giant planets, obtain reflected-light spectra of their atmospheres, survey young systems and circumstellar disks, and support the habitability and biosignature programs that larger future missions will pursue. The wide-field imager complements the coronagraph through transit photometry, occurrence-rate statistics, and long-term monitoring of stellar magnetic activity. A systematic census of the nearest stellar neighbors provides a lasting reference for exoplanet science and future space exploration.

astro-ph.IM↗

arXiv2026

3.5-meter Segmented-Mirror Robotic Space Telescope Mission White Paper IV. Key Scientific Mission: Solar-System Small Bodies and Planetary Defense

The baseline 0.2--1.5 $μ$m observatory provides rapid-response astrometry, visible and near-infrared taxonomy, rotation and phase curves, recovery, and long-arc orbit improvement for near-Earth objects and other small bodies. The instrument study also evaluates calibrated throughput to 2.70 $μ$m with a 3.0 $μ$m operational band-edge goal. A reduction to 2.5 $μ$m remains the formal engineering off-ramp if thermal, detector, cooling, mass, power, or cost constraints require it. The 3.5-meter Segmented-Mirror Robotic Space Telescope does not carry a mid-infrared channel. Coordinated ground-based mid-infrared telescopes provide the thermal fluxes required to infer diameter and albedo, while the space mission supplies contemporaneous reflected-light measurements and observing geometry. The program combines recovery, physical characterization, orbit refinement, and covariance-based hazard assessment. Its CODES dynamics system and OGFinder-to-OpenOrb processing path connect measured astrometry to reproducible orbit solutions and close-approach predictions.

astro-ph.IM↗

arXiv2026

3.5-meter Segmented-Mirror Robotic Space Telescope Mission White Paper V. Key Scientific Mission: Compact-Object Time-Domain Science

An isolated compact object retains the point-source resolving power of the space-based slitless spectrograph. The baseline wavelength range is 0.2--1.5 $μ$m. The planning baseline uses $R \simeq 1000$ for broad and faint transient spectra and reserves selectable bands at $R \simeq 5000$ for accretion-disk profiles, velocity structure, and precision line ratios. Broad features can be measured after binning the native $R \simeq 5000$ data to lower resolution. Rapid-response spectroscopy follows gravitational-wave counterparts and kilonovae from hours to days. Repeated spectra of dwarf novae and compact binaries trace accretion state and orbital phase, while uninterrupted imaging of white dwarfs measures pulsation frequencies. The program combines mission-based monitoring with external alerts, including KGMT transient detections. The instrument study must preserve calibrated throughput to 2.70 $μ$m and evaluate a 3.0 $μ$m operational band edge, with 2.5 $μ$m retained as the formal engineering off-ramp. Mid-infrared imaging is not part of the adopted compact-object baseline.

astro-ph.IM↗

arXiv2025

Autonomous Task Offloading of Vehicular Edge Computing with Parallel Computation Queues

This work considers a parallel task execution strategy in vehicular edge computing (VEC) networks, where edge servers are deployed along the roadside to process offloaded computational tasks of vehicular users. To minimize the overall waiting delay among vehicular users, a novel task offloading solution is implemented based on the network cooperation balancing resource under-utilization and load congestion. Dual evaluation through theoretical and numerical ways shows that the developed solution achieves a globally optimal delay reduction performance compared to existing methods, which is also validated by the feasibility test over a real-map virtual environment. The in-depth analysis reveals that predicting the instantaneous processing power of edge servers facilitates the identification of overloaded servers, which is critical for determining network delay. By considering discrete variables of the queue, the proposed technique's precise estimation can effectively address these combinatorial challenges to achieve optimal performance.

cs.NI↗

arXiv2025

Constraint-Compliant Network Optimization through Large Language Models

This work develops an LLM-based optimization framework ensuring strict constraint satisfaction in network optimization. While LLMs possess contextual reasoning capabilities, existing approaches often fail to enforce constraints, causing infeasible solutions. Unlike conventional methods that address average constraints, the proposed framework integrates a natural language-based input encoding strategy to restrict the solution space and guarantee feasibility. For multi-access edge computing networks, task allocation is optimized while minimizing worst-case latency. Numerical evaluations demonstrate LLMs as a promising tool for constraint-aware network optimization, offering insights into their inference capabilities.

cs.NI↗

arXiv2025

Chiral quantum magnets with optically and catalytically active spin ladders

Chiral quantum magnets with spin-states separated by a large energy gap are technologically attractive but difficult to realize. Geometrically frustrated topological states with nanoscale chirality may offer a chemical pathway to such materials. However, room temperature spin misalignment, weakness of Dzyaloshinskii-Moriya interactions, and high energy requirements for lattice distortions set high physicochemical barriers for their realization. Here, we show that layered iron oxyhydroxides (LIOX) address these challenges due to chirality transfer from surface ligands into spin-states of dimerized FeO6 octahedra with zig-zag stacking. The intercalation of chiral amino acids induces angular displacements in the antiferromagnetic spin pairs with a helical coupling of magnetic moments along the screw axis of the zig-zag chains, or helical spin-ladders. Unlike other chiral magnets, the spin states in LIOX are chemically and optically accessible, they display strong optical resonances with helicity-matching photons and enable spin-selective charge transport. The static rather than dynamic polarization of spin ladders in LIOX makes them particularly suitable for catalysis. Room-temperature spin pairing, field-tunability, environmental robustness, and synthetic simplicity make LIOX and its intercalates a uniquely practical family of quantum magnets.

cond-mat.mtrl-sci↗

arXiv2024

Wireless Interconnection Network (WINE) for Post-Exascale High-Performance Computing

Interconnection networks, or `interconnects,' play a crucial role in administering the communication among computing units of high-performance computing (HPC) systems. Efficient provisioning of interconnects minimizes the processing delay wherein computing units await information sharing between each other, thereby enhancing the overall computation efficiency. Ideally, interconnects are designed with topologies tailored to match specific workflows, requiring diverse structures for different applications. However, since modifying their structures mid-operation renders impractical, indirect communication incurs across distant units. In managing numerous long-routed data deliveries, heavy burdens on the network side may lead to the under-utilization of computing resources. In view of state-of-the-art HPC paradigms that solicit dense interconnections for diverse computation-hungry applications, this article presents a versatile wireless interconnecting framework, coined as Wireless Interconnection NEtwork (WINE). The framework exploits cutting-edge wireless technologies that promote workload adaptability and scalability of modern interconnects. Design and implementation of wirelessly reliable links are strategized under network-oriented scrutiny of HPC architectures. A virtual HPC platform is developed to assess WINE's feasibilities, verifying its practicality for integration into modern HPC infrastructures.

eess.SY↗

arXiv2024

Machine Learning-Aided Cooperative Localization under Dense Urban Environment

Future wireless network technology provides automobiles with the connectivity feature to consolidate the concept of vehicular networks that collaborate on conducting cooperative driving tasks. The full potential of connected vehicles, which promises road safety and quality driving experience, can be leveraged if machine learning models guarantee the robustness in performing core functions including localization and controls. Location awareness, in particular, lends itself to the deployment of location-specific services and the improvement of the operation performance. The localization entails direct communication to the network infrastructure, and the resulting centralized positioning solutions readily become intractable as the network scales up. As an alternative to the centralized solutions, this article addresses decentralized principle of vehicular localization reinforced by machine learning techniques in dense urban environments with frequent inaccessibility to reliable measurement. As such, the collaboration of multiple vehicles enhances the positioning performance of machine learning approaches. A virtual testbed is developed to validate this machine learning model for real-map vehicular networks. Numerical results demonstrate universal feasibility of cooperative localization, in particular, for dense urban area configurations.

cs.IT↗

arXiv2023

Layer-by-Layer Assembled Nanowire Networks Enable Graph Theoretical Design of Multifunctional Coatings

Multifunctional coatings are central for information, biomedical, transportation and energy technologies. These coatings must possess hard-to-attain properties and be scalable, adaptable, and sustainable, which makes layer-by-layer assembly (LBL) of nanomaterials uniquely suitable for these technologies. What remains largely unexplored is that LBL enables computational methodologies for structural design of these composites. Utilizing silver nanowires (NWs), we develop and validate a graph theoretical (GT) description of their LBL composites. GT successfully describes the multilayer structure with nonrandom disorder and enables simultaneous rapid assessment of several properties of electrical conductivity, electromagnetic transparency, and anisotropy. GT models for property assessment can be rapidly validated due to (1) quasi-2D confinement of NWs and (2) accurate microscopy data for stochastic organization of the NW networks. We finally show that spray-assisted LBL offers direct translation of the GT-based design of composite coatings to additive, scalable manufacturing of drone wings with straightforward extensions to other technologies.

physics.app-ph↗

arXiv2021

Learning Autonomy in Management of Wireless Random Networks

This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes. Individual nodes decide their optimal states with distributed coordination among other nodes through randomly varying backhaul links. This poses a technical challenge in distributed universal optimization policy robust to a random topology of the wireless network, which has not been properly addressed by conventional deep neural networks (DNNs) with rigid structural configurations. We develop a flexible DNN formalism termed distributed message-passing neural network (DMPNN) with forward and backward computations independent of the network topology. A key enabler of this approach is an iterative message-sharing strategy through arbitrarily connected backhaul links. The DMPNN provides a convergent solution for iterative coordination by learning numerous random backhaul interactions. The DMPNN is investigated for various configurations of the power control in wireless networks, and intensive numerical results prove its universality and viability over conventional optimization and DNN approaches.

cs.IT↗

arXiv2019

A Deep Learning Approach to Universal Binary Visible Light Communication Transceiver

This paper studies a deep learning (DL) framework for the design of binary modulated visible light communication (VLC) transceiver with universal dimming support. The dimming control for the optical binary signal boils down to a combinatorial codebook design so that the average Hamming weight of binary codewords matches with arbitrary dimming target. An unsupervised DL technique is employed for obtaining a neural network to replace the encoder-decoder pair that recovers the message from the optically transmitted signal. In such a task, a novel stochastic binarization method is developed to generate the set of binary codewords from continuous-valued neural network outputs. For universal support of arbitrary dimming target, the DL-based VLC transceiver is trained with multiple dimming constraints, which turns out to be a constrained training optimization that is very challenging to handle with existing DL methods. We develop a new training algorithm that addresses the dimming constraints through a dual formulation of the optimization. Based on the developed algorithm, the resulting VLC transceiver can be optimized via the end-to-end training procedure. Numerical results verify that the proposed codebook outperforms theoretically best constant weight codebooks under various VLC setups.

cs.IT↗

arXiv2019

207 New Open Star Clusters within 1 kpc from Gaia Data Release 2

We conducted a survey of open clusters within 1 kpc from the Sun using the astrometric and photometric data of the Gaia Data Release 2. We found 655 cluster candidates by visual inspection of the stellar distributions in proper motion space and spatial distributions in l-b space. All of the 655 cluster candidates have a well defined main-sequence except for two candidates if we consider that the main sequence of very young clusters is somewhat broad due to differential extinction. Cross-matching of our 653 open clusters with known open clusters in various catalogs resulted in 207 new open clusters. We present the physical properties of the newly discovered open clusters. The majority of the newly discovered open clusters are of young to intermediate age and have less than ~50 member stars.

astro-ph.SR↗

arXiv2019

Deep Learning for Distributed Optimization: Applications to Wireless Resource Management

This paper studies a deep learning (DL) framework to solve distributed non-convex constrained optimizations in wireless networks where multiple computing nodes, interconnected via backhaul links, desire to determine an efficient assignment of their states based on local observations. Two different configurations are considered: First, an infinite-capacity backhaul enables nodes to communicate in a lossless way, thereby obtaining the solution by centralized computations. Second, a practical finite-capacity backhaul leads to the deployment of distributed solvers equipped along with quantizers for communication through capacity-limited backhaul. The distributed nature and the nonconvexity of the optimizations render the identification of the solution unwieldy. To handle them, deep neural networks (DNNs) are introduced to approximate an unknown computation for the solution accurately. In consequence, the original problems are transformed to training tasks of the DNNs subject to non-convex constraints where existing DL libraries fail to extend straightforwardly. A constrained training strategy is developed based on the primal-dual method. For distributed implementation, a novel binarization technique at the output layer is developed for quantization at each node. Our proposed distributed DL framework is examined in various network configurations of wireless resource management. Numerical results verify the effectiveness of our proposed approach over existing optimization techniques.

cs.IT↗

arXiv2018

Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications

Optical wireless communication (OWC) is a promising technology for future wireless communications owing to its potentials for cost-effective network deployment and high data rate. There are several implementation issues in the OWC which have not been encountered in radio frequency wireless communications. First, practical OWC transmitters need an illumination control on color, intensity, and luminance, etc., which poses complicated modulation design challenges. Furthermore, signal-dependent properties of optical channels raise non-trivial challenges both in modulation and demodulation of the optical signals. To tackle such difficulties, deep learning (DL) technologies can be applied for optical wireless transceiver design. This article addresses recent efforts on DL-based OWC system designs. A DL framework for emerging image sensor communication is proposed and its feasibility is verified by simulation. Finally, technical challenges and implementation issues for the DL-based optical wireless technology are discussed.

cs.IT↗

arXiv2014

VLBI observations of bright AGN jets with KVN and VERA Array (KaVA): Evaluation of Imaging Capability

The Korean very-long-baseline interferometry (VLBI) network (KVN) and VLBI Exploration of Radio Astrometry (VERA) Array (KaVA) is the first international VLBI array dedicated to high-frequency (23 and 43 GHz bands) observations in East Asia. Here, we report the first imaging observations of three bright active galactic nuclei (AGNs) known for their complex morphologies: 4C 39.25, 3C 273, and M 87. This is one of the initial result of KaVA early science. Our KaVA images reveal extended outflows with complex substructure such as knots and limb brightening, in agreement with previous Very Long Baseline Array (VLBA) observations. Angular resolutions are better than 1.4 and 0.8 milliarcsecond at 23 GHz and 43 GHz, respectively. KaVA achieves a high dynamic range of ~1000, more than three times the value achieved by VERA. We conclude that KaVA is a powerful array with a great potential for the study of AGN outflows, at least comparable to the best existing radio interferometric arrays.

astro-ph.IM↗

arXiv2014

The First Very Long Baseline Interferometry Image of 44 GHz Methanol Maser with the KVN and VERA Array (KaVA)

We have carried out the first very long baseline interferometry (VLBI) imaging of 44 GHz class I methanol maser (7_{0}-6_{1}A^{+}) associated with a millimeter core MM2 in a massive star-forming region IRAS 18151-1208 with KaVA (KVN and VERA Array), which is a newly combined array of KVN (Korean VLBI Network) and VERA (VLBI Exploration of Radio Astrometry). We have succeeded in imaging compact maser features with a synthesized beam size of 2.7 milliarcseconds x 1.5 milliarcseconds (mas). These features are detected at a limited number of baselines within the length of shorter than approximately 650 km corresponding to 100 Mlambda in the uv-coverage. The central velocity and the velocity width of the 44 GHz methanol maser are consistent with those of the quiescent gas rather than the outflow traced by the SiO thermal line. The minimum component size among the maser features is ~ 5 mas x 2 mas, which corresponds to the linear size of ~ 15 AU x 6 AU assuming a distance of 3 kpc. The brightness temperatures of these features range from ~ 3.5 x 10^{8} to 1.0 x 10^{10} K, which are higher than estimated lower limit from a previous Very Large Array observation with the highest spatial resolution of ~ 50 mas. The 44 GHz class I methanol maser in IRAS 18151-1208 is found to be associated with the MM2 core, which is thought to be less evolved than another millimeter core MM1 associated with the 6.7 GHz class II methanol maser.

astro-ph.GA↗