arXiv subjects

Sungwook E. Hong

Publications and source records attributed to Sungwook E. Hong.

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.

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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.

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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.

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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.

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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.

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arXiv2026

Constructing a Mock Galaxy Catalog for the All-sky SPECtroscopic Survey of Nearby Galaxies (A-SPEC) Using the Machine-assisted Semi-Simulation Model

We present a methodology for constructing a mock galaxy catalog for the All-sky SPECtroscopic survey of nearby galaxies (A-SPEC) using the Machine-assisted Semi-Simulation Model. The model is trained on the cosmological magnetohydrodynamical simulation IllustrisTNG to predict baryonic properties of subhalos from dark-matter-only features and is applied to our own N-body simulation tailored to satisfy the requirements of A-SPEC. We have improved the model's accuracy by introducing additional features such as subhalo anisotropy parameters and modified definitions of the subhalo environment, which result in the coefficient of determination R^2=0.96, 0.90, 0.70, 0.79 for stellar mass, gas mass, star formation rate, and gas metallicity, respectively. The resulting mock galaxies reproduce the luminosity-dependent clustering of the target galaxies when tuned to match the number density. We discuss avenues for further improvement, including the role of environment in the predictions. We release the mock galaxy catalog with the baryonic properties predicted from the model.

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arXiv2026

A New Collision Avoidance Fiber Assignment Algorithm for Robotic Fiber Positioners in Multi-Object Spectroscopy

We present a new fiber assignment algorithm for a robotic fiber positioner system in multi-object spectroscopy. Modern fiber positioner systems typically have overlapping patrol regions, resulting in the number of observable targets being highly dependent on the fiber assignment scheme. To maximize observable targets without fiber collisions, the algorithm proceeds in three steps. First, it assigns the maximum number of targets for a given field of view without considering any collisions between fiber positioners. Then, the fibers in collision are grouped, and the algorithm finds the optimal solution resolving the collision problem within each group. We compare the results from this new algorithm with those from a simple algorithm that assigns targets in descending order of their rank by considering collisions. As a result, we could increase the overall completeness of target assignments by 10% with this new algorithm in comparison with the case using the simple algorithm in a field with 150 fibers. Our new algorithm is designed for the All-sky SPECtroscopic survey of nearby galaxies (A-SPEC) based on the K-SPEC spectrograph system, but can also be applied to similar fiber-based systems with heavily overlapping fiber positioners.

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arXiv2025

Revealing Hidden Cosmic Flows through the Zone of Avoidance with Deep Learning

We present a refined deep-learning-based method to reconstruct the three-dimensional dark matter density, gravitational potential, and peculiar velocity fields in the Zone of Avoidance (ZOA), a region near the galactic plane with limited observational data. Using a convolutional neural network (V-Net) trained on A-SIM simulation data, our approach reconstructs density or potential fields from galaxy positions and radial peculiar velocities. The full 3D peculiar velocity field is then derived from the reconstructed potential. We validate the method with mocks that mimic the spatial distribution of the Cosmicflows-4 (CF4) catalog and apply it to actual data. Given CF4's significant observational uncertainties and since our model does not yet account for them, we use peculiar velocities corrected via an existing Hamiltonian Monte Carlo reconstruction, rather than raw catalog distances. Our results demonstrate that the reconstructed density field recovers known galaxy clusters detected in an H \textsc{i} survey of the ZOA, despite this dataset not being used in the reconstruction. This agreement underscores the potential of our method to reveal structures in data-sparse regions. Most notably, streamline convergence and watershed analysis identify a mass concentration consistent with the Great Attractor, at $(l, b) = (308.4^\circ \pm 2.4^\circ, 29.0^\circ \pm 1.9^\circ)$ and $cz = 4960.1 \pm 404.4,{\rm km/s}$, for 64\% of realizations. Our method is particularly valuable as it does not rely on data point density, enabling accurate reconstruction in data-sparse regions and offering strong potential for future surveys with more extensive galaxy datasets.

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arXiv2025

Weak-lensing Mass Reconstruction of Galaxy Clusters with a Convolutional Neural Network -- II: Application to Next-Generation Wide-Field Surveys

Traditional weak-lensing mass reconstruction techniques suffer from various artifacts, including noise amplification and the mass-sheet degeneracy. In Hong et al. (2021), we demonstrated that many of these pitfalls of traditional mass reconstruction can be mitigated using a deep learning approach based on a convolutional neural network (CNN). In this paper, we present our improvements and report on the detailed performance of our CNN algorithm applied to next-generation wide-field observations. Assuming the field of view ($3°.5 \times 3°.5$) and depth (27 mag at $5σ$) of the Vera C. Rubin Observatory, we generated training datasets of mock shear catalogs with a source density of 33 arcmin$^{-2}$ from cosmological simulation ray-tracing data. We find that the current CNN method provides high-fidelity reconstructions consistent with the true convergence field, restoring both small and large-scale structures. In addition, the cluster detection utilizing our CNN reconstruction achieves $\sim75$% completeness down to $\sim 10^{14}M_{\odot}$. We anticipate that this CNN-based mass reconstruction will be a powerful tool in the Rubin era, enabling fast and robust wide-field mass reconstructions on a routine basis.

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arXiv2024

Emergence of the Galaxy Morphology-Star Formation Activity-Clustercentric Radius Relations in Galaxy Clusters

We investigate when and how the relations of galaxy morphology and star forming activity with clustercentric radius become evident in galaxy clusters. We identify 162 galaxy clusters with total mass $M_{\rm tot}^{\rm cl} > 5 \times 10^{13} {\rm M}_\odot$ at $z = 0.625$ in the Horizon Run 5 (HR5) cosmological hydrodynamical simulation and study how the properties of the galaxies with stellar mass $M_\ast > 5 \times 10^9 {\rm M}_\odot$ near the cluster main progenitors have evolved in the past. Galaxies are classified into disk, spheroid, and irregular morphological types according to the asymmetry and Sersic index of their stellar mass distribution. We also classify galaxies into active and passive ones depending on their specific star-formation rate. We find that the morphology-clustercentric radius relation (MRR) emerges at $z \simeq 1.8$ as the fraction of spheroidal types exceeds 50% in the central region ($d \lesssim 0.1 R_{200}$). Galaxies outside the central region remain disk-dominated. Numerous encounters between galaxies in the central region seem to be responsible for the morphology transformation from disks to spheroids. We also find that the star formation activity-clustercentric radius relation emerges at an epoch different from that of MRR. At $z\simeq0.8$, passive galaxies start to dominate the intermediate radius region ($0.1\lesssim d/R_{200} \lesssim0.3$) and this "quenching region" grows inward and outward thereafter. The region dominated by early-type galaxies (spheroids and passive disks) first appears at the central region at $z\simeq 1.8$, expands rapidly to larger radii as the population of passive disks grows in the intermediate radii, and clusters are dominated by early types after $z\simeq 0.8$.

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arXiv2024

Final parsec problem of black hole mergers and ultralight dark matter

When two galaxies merge, they often produce a supermassive black hole binary (SMBHB) at their center. Numerical simulations with stars and cold dark matter show that SMBHBs typically stall out at a distance of a few parsecs apart and take billions of years to coalesce. This is known as the final parsec problem. We suggest that ultralight dark matter (ULDM) halos around SMBHBs can generate dark matter waves due to dynamical friction. These waves can effectively carry away orbital energy from the black holes, rapidly driving them together. To test this hypothesis, we performed numerical simulations of black hole binaries inside ULDM halos. Due to gravitational cooling and quasi-normal modes, the loss-cone problem can be avoided. The decay time scale gives lower bounds on masses of the ULDM particles and SMBHBs comparable to observational data. Our results imply that ULDM waves can lead to the rapid orbital decay of black hole binaries.

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arXiv2024

The Environmental Dependence of the Stellar Mass - Gas Metallicity Relation in Horizon Run 5

Metallicity offers a unique window into the baryonic history of the cosmos, being instrumental in probing evolutionary processes in galaxies between different cosmic environments. We aim to quantify the contribution of these environments to the scatter in the mass-metallicity relation (MZR) of galaxies. By analysing the galaxy distribution within the cosmic skeleton of the Horizon Run 5 cosmological hydrodynamical simulation at redshift $z = 0.625$, computed using a careful calibration of the T-ReX filament finder, we identify galaxies within three main environments: nodes, filaments and voids. We also classify galaxies based on the dynamical state of the clusters and the length of the filaments in which they reside. We find that the cosmic environment significantly contributes to the scatter in the MZR; in particular, both the gas metallicity and its average relative standard deviation increase when considering denser large-scale environments. The difference in the average metallicity between galaxies within relaxed and unrelaxed clusters is $\approx 0.1 \text{ dex}$, with both populations displaying positive residuals, $δZ_{g}$, from the averaged MZR. Moreover, the difference in metallicity between node and void galaxies accounts for $\approx 0.14 \, \text{dex}$ in the scatter of the MZR at stellar mass $M_{\star} \approx 10^{9.35}\,\text{M}_{\odot}$. Finally, both the average [O/Fe] in the gas and the galaxy gas fraction decrease when moving to higher large-scale densities in the simulation, suggesting that the cores of cosmic environments host, on average, older and more massive galaxies, whose enrichment is affected by a larger number of Type Ia Supernova events.

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arXiv2024

Merger Tree-based Galaxy Matching: A Comparative Study Across Different Resolutions

We introduce a novel halo/galaxy matching technique between two cosmological simulations with different resolutions, which utilizes the positions and masses of halos along their subhalo merger tree. With this tool, we conduct a study of resolution biases through the {\it galaxy-by-galaxy} inspection of a pair of simulations that have the same simulation configuration but different mass resolutions, utilizing a suite of {\sc IllustrisTNG} simulations to assess the impact on galaxy properties. We find that, with the subgrid physics model calibrated for TNG100-1, subhalos in TNG100-1 (high resolution) have $\lesssim0.5$ dex higher stellar masses than their counterparts in the TNG100-2 (low-resolution). It is also discovered that the subhalos with $M_{\mathrm{gas}}\sim10^{8.5}\,{\rm M}_\odot$ in TNG100-1 have $\sim0.5$ dex higher gas mass than those in TNG100-2. The mass profiles of the subhalos reveal that the dark matter masses of subhalos in TNG100-2 converge well with those from TNG100-1, except within 4 kpc of the resolution limit. The differences in stellar mass and hot gas mass are most pronounced in the central region. We exploit machine learning to build a correction mapping for the physical quantities of subhalos from low- to high-resolution simulations (TNG300-1 and TNG100-1), which enables us to find an efficient way to compile a high-resolution galaxy catalog even from a low-resolution simulation. Our tools can easily be applied to other large cosmological simulations, testing and mitigating the resolution biases of their numerical codes and subgrid physics models.

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arXiv2024

The effects of non-linearity on the growth rate constraint from velocity correlation functions

The two-point statistics of the cosmic velocity field, measured from galaxy peculiar velocity (PV) surveys, can be used as a dynamical probe to constrain the growth rate of large-scale structures in the universe. Most works use the statistics on scales down to a few tens of Megaparsecs, while using a theoretical template based on the linear theory. In addition, while the cosmic velocity is volume-weighted, the observable line-of-sight velocity two-point correlation is density-weighted, as sampled by galaxies, and therefore the density-velocity correlation term also contributes, which has often been neglected. These effects are fourth order in powers of the linear density fluctuation $δ_{\rm L}^4$, compared to $δ_{\rm L}^2$ of the linear velocity correlation function, and have the opposite sign. We present these terms up to $δ_{\rm L}^4$ in real space based on the standard perturbation theory, and investigate the effect of non-linearity and the density-velocity contribution on the inferred growth rate $fσ_8$, using $N$-body simulations. We find that for a next-generation PV survey of volume $\sim {\cal O}(500 \, h^{-1} \, {\rm Mpc})^3$, these effects amount to a shift of $fσ_8$ by $\sim 10$ per cent and is comparable to the forecasted statistical error when the minimum scale used for parameter estimation is $r_{\rm min} = 20 \, h^{-1} \, {\rm Mpc}$.

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arXiv2024

MulGuisin, a Topological Network Finder and its Performance on Galaxy Clustering

We introduce a new clustering algorithm, MulGuisin (MGS), that can identify distinct galaxy over-densities using topological information from the galaxy distribution. This algorithm was first introduced in an LHC experiment as a Jet Finder software, which looks for particles that clump together in close proximity. The algorithm preferentially considers particles with high energies and merges them only when they are closer than a certain distance to create a jet. MGS shares some similarities with the minimum spanning tree (MST) since it provides both clustering and network-based topology information. Also, similar to the density-based spatial clustering of applications with noise (DBSCAN), MGS uses the ranking or the local density of each particle to construct clustering. In this paper, we compare the performances of clustering algorithms using controlled data and some realistic simulation data as well as the SDSS observation data, and we demonstrate that our new algorithm finds networks most efficiently and defines galaxy networks in a way that most closely resembles human vision.

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arXiv2024

Maunakea Spectroscopic Explorer exposure time calculator for end-to-end simulator: to optimizing spectrograph design and observing simulation

The Maunakea Spectroscopic Explorer (MSE) project will provide multi-object spectroscopy in the optical and near-infrared bands using an 11.25-m aperture telescope, repurposing the original Canada-France-Hawaii Telescope (CFHT) site. MSE will observe 4,332 objects per single exposure with a field of view of 1.5 square degrees, utilizing two spectrographs with low-moderate (R$\sim$3,000, 6,000) and high (R$\approx$30,000) spectral resolution. In general, an exposure time calculator (ETC) is used to estimate the performance of an observing system by calculating a signal-to-noise ratio (S/N) and exposure time. We present the design of the MSE exposure time calculator (ETC), which has four calculation modes (S/N, exposure time, S/N trend with wavelength, and S/N trend with magnitude) and incorporates the MSE system requirements as specified in the Conceptual Design. The MSE ETC currently allows for user-defined inputs of target AB magnitude, water vapor, airmass, and sky brightness AB magnitude (additional user inputs can be provided depending on computational mode). The ETC is built using Python 3.7 and features a graphical user interface that allows for cross-platform use. The development process of the ETC software follows an Agile methodology and utilizes the Unified Modeling Language (UML) diagrams to visualize the software architecture. We also describe the testing and verification of the MSE ETC.

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arXiv2023

Modeling Cosmological Perturbations of Thermal Inflation

We consider a simple system consisting of matter, radiation and vacuum components to model the impact of thermal inflation on the evolution of primordial perturbations. The vacuum energy magnifies the primordial modes entering the horizon before its domination, making them potentially observable, and the resulting transfer function reflects the phase changes and energy contents. To determine the transfer function, we follow the curvature perturbation from well outside the horizon during radiation domination to well outside the horizon during vacuum domination and evaluate it on a constant radiation density hypersurface, as is appropriate for the case of thermal inflation. The shape of the transfer function is determined by the ratio of vacuum energy to radiation at matter-radiation equality, which we denote by $\upsilon$, and has two characteristic scales, $k_{\rm a}$ and $k_{\rm b}$, corresponding to the horizon sizes at matter radiation equality and the beginning of the inflation, respectively. If $\upsilon \ll 1$, the universe experiences radiation, matter and vacuum domination eras and the transfer function is flat for $k \ll k_{\rm b}$, oscillates with amplitude $1/5$ for $ k_{\rm b} \ll k \ll k_{\rm a}$ and oscillates with amplitude $1$ for $k \gg k_{\rm a}$. For $\upsilon \gg 1$, the matter domination era disappears, and the transfer function reduces to being flat for $k \ll k_{\rm b}$ and oscillating with amplitude $1$ for $k \gg k_{\rm b}$.

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arXiv2023

The Universe is worth $64^3$ pixels: Convolution Neural Network and Vision Transformers for Cosmology

We present a novel approach for estimating cosmological parameters, $Ω_m$, $σ_8$, $w_0$, and one derived parameter, $S_8$, from 3D lightcone data of dark matter halos in redshift space covering a sky area of $40^\circ \times 40^\circ$ and redshift range of $0.3 < z < 0.8$, binned to $64^3$ voxels. Using two deep learning algorithms, Convolutional Neural Network (CNN) and Vision Transformer (ViT), we compare their performance with the standard two-point correlation (2pcf) function. Our results indicate that CNN yields the best performance, while ViT also demonstrates significant potential in predicting cosmological parameters. By combining the outcomes of Vision Transformer, Convolution Neural Network, and 2pcf, we achieved a substantial reduction in error compared to the 2pcf alone. To better understand the inner workings of the machine learning algorithms, we employed the Grad-CAM method to investigate the sources of essential information in activation maps of the CNN and ViT. Our findings suggest that the algorithms focus on different parts of the density field and redshift depending on which parameter they are predicting. This proof-of-concept work paves the way for incorporating deep learning methods to estimate cosmological parameters from large-scale structures, potentially leading to tighter constraints and improved understanding of the Universe.

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