arXiv subjects

Jaehyun Lee

Publications and source records attributed to Jaehyun Lee.

At least 37 records · Page 2Linked to original sources

arXiv2026

K-DRIFT Science Theme: Galaxies in the Faint Universe

Low-surface-brightness (LSB) structures serve as evidence of the intricate mass assembly of galaxies, and dedicatedly studying them promises to give us profound insights into the evolutionary history of galaxies. Furthermore, delving into the properties of star formation (SF) in the LSB regime can broaden our understanding of SF activity in regions characterized by low surface gas density, thereby shedding light on fundamental cosmic processes. However, systematic uncertainties may hamper the exploration of the LSB universe by limiting detectable SB levels. Indeed, despite dedicated advancements in telescope and observing techniques over decades, achieving ultra-deep photometric depths in optical wavelengths remains a formidable challenge. To overcome this challenge and explore the LSB universe that we have yet to see, we have been developing a novel telescope called K-DRIFT. This paper outlines the telescope's specification and describes various LSB features we aim for, explicitly focusing on nearby individual galaxies. To further advance the capabilities of the K-DRIFT survey, focused on LSB detection, we present several feasible research topics that utilize other survey data together and discuss the role of LSB observation in understanding the evolution of galaxies.

astro-ph.GA↗

arXiv2026

Convolutional Neural Networks for classifying galaxy mergers: Can faint tidal features aid in classifying mergers?

Identifying mergers from observational data has been a crucial aspect of studying galaxy evolution and formation. Tidal features, typically fainter than 26 ${\rm mag\,arcsec^{-2}}$, exhibit a diverse range of appearances depending on the merger characteristics and are expected to be investigated in greater detail with the Rubin Observatory Large Synoptic Survey Telescope (LSST), which will reveal the low surface brightness universe with unprecedented precision. Our goal is to assess the feasibility of developing a convolutional neural network (CNN) that can distinguish between mergers and non-mergers based on LSST-like deep images. To this end, we used Illustris TNG50, one of the highest-resolution cosmological hydrodynamic simulations to date, allowing us to generate LSST-like mock images with a depth $\sim$ 29 ${\rm mag\,arcsec^{-2}}$ for low-redshift ($z=0.16$) galaxies, with labeling based on their merger status as ground truth. We focused on 151 Milky Way-like galaxies in field environments, comprising 81 non-mergers and 70 mergers. After applying data augmentation and hyperparameter tuning, a CNN model was developed with an accuracy of 65--67\%. Through additional image processing, the model was further optimized, achieving an accuracy of 67--70\% when trained on images containing only faint features. This represents an improvement of $\sim$ 5\% compared to training on images with bright features only. This suggests that faint tidal features can serve as effective indicators for distinguishing between mergers and non-mergers. The future direction for further improvement based on this study is also discussed.

astro-ph.GA↗

arXiv2026

The Role of Large-Scale Environment in Shaping the Stellar Mass-Gas Metallicity Relation Across Time

We study the stellar mass-gas metallicity relation (MZR) which shows a significant scatter for a fixed stellar mass. By defining global environments, nodes, filaments, and voids within the Horizon Run 5 cosmological hydrodynamical simulation, we explore when and where the enrichment of galaxies occurs, analysing key evolution parameters such as star-formation rate and changes in gas-fraction and gas-metallicity per unit time. At high redshift ($z>4.5$), there are minimal deviations from the MZR due to environment, however, larger deviations emerge as redshift decreases. Low stellar mass galaxies in nodes, $M_{\star} < 10^{9.8}\,\text{M}_{\odot}$, start showing deviations at $z = 3.5$, whilst other environments do not. For, $z < 2$, filaments and voids begin to show deviations above and below the MZR, respectively. By $z = 0.625$, the last epoch of HR5, deviations exist for all stellar masses and environments, with a maximum value of 0.13 dex at $M_{\star} \approx 10^{9.35}\,\text{M}_{\odot}$, between the median gas metallicities of node and void galaxies. To explain this environmental variance we discuss gas accretion, AGN, ram-pressure-stripping and strangulation as regulators of $Z_{g}$. Concurrently, at high metallicities, for $z < 2$, while massive galaxies in nodes show increasing $Z_{g}$ and decreasing [O/Fe], void galaxies show a turnover where $Z_{g}$ falls with decreasing [O/Fe]. This directly points to the importance of cold-gas accretion in retaining lower $Z_{g}$ in massive void galaxies for $z < 2$, whilst its absence in nodes allowed $Z_{g}$ to access higher values.

astro-ph.GA↗

arXiv2026

K-DRIFT Science Theme: Galactic Cirrus Clouds and Circumgalactic Medium

In this paper, we review the extended halo material and the circumgalactic medium (CGM), including both dust and gas, and discuss promising science cases that could be realized using the KASI Deep Rolling Imaging Fast Telescope (K-DRIFT). Scattered starlight from cirrus clouds in our Galaxy poses one of the major challenges to studying the low surface brightness features of extragalactic sources. Therefore, it is essential to investigate how to discriminate extragalactic sources from the cirrus cloud features. At the same time, interstellar dust clouds themselves are fundamental to understanding dust properties and the interstellar radiation field, both of which are essential for studies of chemical evolution and star formation in our Galaxy. Measuring the reddening of background sources, such as quasars, with K-DRIFT, which benefits from its broad field of view and accurate background subtraction, allows for effective detection of extended dust in galactic halos, the CGM, and intracluster space. Observations of the H-alpha emission lines can be used to identify signatures of star formation activity within galaxies, as well as the environmental effects acting on them. Galactic winds driven by active galactic nuclei and starbursts can be traced through H-alpha emission. Strong ram pressure stripping effectively removes the interstellar medium (ISM) from galaxies. The stripped ISM becomes ionized or dissociated through mixing with the hot intracluster medium (ICM), forming H-alpha tails. The surface brightness of these H-alpha tails correlates not only with the presence of star formation in the tails but also the mixing stage of the stripped ISM and ICM. The H-alpha survey with K-DRIFT will enable the investigation of the evolutionary stages of ram pressure stripped galaxies in cluster environments, as well as the multiphase gas reservoir around galaxies and in the CGM.

astro-ph.GA↗

arXiv2025

K-DRIFT Science Theme: Illuminating the Next Era of Galaxy Cluster Science

The KASI Deep Rolling Imaging Fast Telescope (K-DRIFT) is a pioneering instrument designed to explore low-surface-brightness (LSB) phenomena. This white paper presents a compelling array of science cases that showcase K-DRIFT's unique capabilities in unraveling the mysteries of intracluster light (ICL) and other LSB components within galaxy clusters. Exploring the origin of ICL in galaxy clusters and comparing the spatial distributions of ICL and dark matter will offer new insights into galaxy cluster dynamics. Moreover, investigating LSB objects in galaxy clusters, such as LSB structures in the brightest cluster galaxy, ultra-diffuse galaxies, and tidal features, will enhance our understanding of galaxy evolution within the cluster environment. We present our strategies for addressing scientific queries, encompassing LSB observation and analysis techniques, specialized simulations, and machine-learning approaches. Additionally, we examine the potential synergies between K-DRIFT and other ongoing or forthcoming multi-wavelength surveys. This white paper advocates for the recognition and support of K-DRIFT as a dedicated tool for advancing our understanding of the universe's subtlest phenomena.

astro-ph.GA↗

arXiv2025

ODIN: A New Lyman Alpha Blob Selection Method, Sample, and Statistical Analysis at $z\sim3.1$

Ly$α$ blobs (LABs) are large, spatially extended Ly$α$-emitting objects whose nature remains unclear. Their statistical properties such as number densities and luminosity functions are still uncertain because of small sample sizes and large cosmic variance. The One-hundred-deg$^2$ DECam Imaging in Narrowbands (ODIN) survey, with its large volume, offers an opportunity to overcome these limitations. We describe our LAB selection method and present 112 new LABs in the 9 deg$^2$ E-COSMOS field. We begin with the conventional LAB selection approach, cross-matching LAEs with extended Ly$α$ sources, yielding 89 LAB candidates. To obtain a more complete LAB sample, we introduce a new selection pipeline that models all galaxies detected in deep broadband imaging, subtracts them from the narrowband image, and then directly detects extended Ly$α$ emission. This method successfully identifies 23 additional low-surface-brightness LABs which could otherwise be missed by the conventional method. The number density of ODIN LABs near an ODIN protocluster ($n=7.5\times10^{-5}$ cMpc$^{-3}$) is comparable to that found in the SSA22 proto-cluster and is four times higher than the average across the field. The cumulative Ly$α$ luminosity function within the protocluster regions is similar to that measured for the LABs in the SSA22 proto-cluster, suggesting a large excess of luminous LABs relative to the average field. These findings suggest the Ly$α$ luminosities and number densities of LABs are environment-dependent. ODIN will provide an expansive LAB and protocluster samples across six additional fields and two more redshifts, allowing us to investigate the nature of LABs in relation to their environments.

astro-ph.GA↗

arXiv2025

Relating the dynamics of photo de-mixing in mixed bromide-iodide perovskites to ionic and electronic transport

The observation of reversible de-mixing phenomena in mixed-halide perovskites under illumination is one of the most challenging as well as intriguing aspects of this class of materials. On the one hand, it poses critical constraints to the compositional space that allows reliable design of absorbers for perovskite photovoltaics. On the other hand, it holds potential for the development of novel optoionic devices where an ionic response is triggered via optical stimuli. Funda-mental questions about the origin of such photo de-mixing process remain unanswered, both in terms of its mechanism as well as thermodynamic description. Here, we relate in-situ measurements of ionic and electronic transport of mixed bromide-iodide perovskite thin films performed during photo de-mixing with the evolution of their optical and morpho-logical properties. The results point to the definition of different stages of the de-mixing process which, based on micros-copy and spectroscopic measurements, we assign to regimes of spinodal decomposition and nucleation of quasi-equilibrium iodide- and bromide-rich phases. Combined with density functional theory calculations, we explore the role of dimensionality in the mechanism and reversibility of photo de-mixing and dark re-mixing processes, referring to elec-tronic and ionic contributions to the de-mixing driving force. Additionally, our data emphasizes the role of the surface, as significantly different de-mixing dynamics, in terms of extent and reversibility, are observed for films with or without encapsulation. Our comprehensive analysis of transport, phase and optical properties of mixed-halide perovskites pro-vides guidelines for future materials design as well as for the more general fundamental understanding of light-induced ionic phenomena.

cond-mat.mtrl-sci↗

arXiv2025

On the Origin of Intracluster Light based on the High-resolution Simulation, NewCluster

Intracluster light (ICL) is a key component of galaxy clusters, with the potential to trace their dynamical assembly histories and the underlying dark matter distribution. Despite these prospects, its faint nature makes a consensus on its origin or population properties difficult to achieve, both in observations and simulations. In the hope of finding a breakthrough, we utilize the ongoing high-resolution cluster simulation, NewCluster. By classifying billions of particles in and around the cluster with a rigorous tracking procedure, we find that the majority of the ICL originates from satellites, including surviving and disrupted galaxies. Another notable finding is that the preprocessed component follows the density profile of dark matter better than the other components and has distinctive properties: old age, low metallicity, and enhanced $α$-element abundance. We further investigate the orbital dynamics, and our results demonstrate that the stripped fraction of satellites is primarily determined by the time since infall and the pericenter distance. By linking the demographic, chemical, and orbital properties of ICL stars to their origins, this work proposes a quantitative approach for tracing the assembly history of galaxy clusters from the ICL.

astro-ph.GA↗

arXiv2025

On the Origin of Gas-stripping of Galaxies in Group Environments

We investigate how low-mass group environments ($M_{\rm vir} \sim 10^{12-13}\,M_{\odot}$) influence the gas content of their satellite galaxies with $M_* > 10^{7}\,M_{\odot}$ using the \NHtwo\ simulation. Many satellite galaxies preserve substantial gas reservoirs, yet show signs of outer gas stripping, reminiscent of jellyfish galaxies in clusters. In contrast, low-mass satellites ($<10^8 \, M_{\odot}$) are largely gas-deficient, and some of them undergo gas removal within their host group by external pressure triggered by either galaxy interactions or ram pressure exerted by the hot intragroup medium. Complete gas removal in these satellite galaxies occurs when the external hydrodynamic pressure exceeds the gravitational restoring force, typically due to stochastic events such as galaxy-galaxy interaction or nearby galactic outflows. The emergence of a characteristic stellar mass of $10^8 \, M_{\odot}$ which determines the efficiency of gas removal in groups, likely reflects the differing scaling relations of external pressure with halo mass and gravitational restoring force with stellar mass. While tidal interactions can be a significant cause of gas loss in satellite galaxies, those severe enough to affect the gas content in the central regions typically lead to the complete disruption of the galaxy. Consequently, gas loss driven by tidal interactions may be underestimated in the studies focusing solely on surviving galaxies. Group environments, where environmental effects are weaker and satellite galaxies tend to have lower restoring forces due to their low masses, exhibit complex manifestations of gas loss that are not seen in more massive environments such as clusters.

astro-ph.GA↗

arXiv2025

Point-to-Point: Sparse Motion Guidance for Controllable Video Editing

Accurately preserving motion while editing a subject remains a core challenge in video editing tasks. Existing methods often face a trade-off between edit and motion fidelity, as they rely on motion representations that are either overfitted to the layout or only implicitly defined. To overcome this limitation, we revisit point-based motion representation. However, identifying meaningful points remains challenging without human input, especially across diverse video scenarios. To address this, we propose a novel motion representation, anchor tokens, that capture the most essential motion patterns by leveraging the rich prior of a video diffusion model. Anchor tokens encode video dynamics compactly through a small number of informative point trajectories and can be flexibly relocated to align with new subjects. This allows our method, Point-to-Point, to generalize across diverse scenarios. Extensive experiments demonstrate that anchor tokens lead to more controllable and semantically aligned video edits, achieving superior performance in terms of edit and motion fidelity.

cs.CV↗

arXiv2025

ODIN: Using multiplicity of Lyman-Alpha Emitters to assess star formation activity in dark matter halos

We investigate if systems of multiple Lyman-alpha emitters (LAEs) can serve as a proxy for dark matter halo mass, assess how their radiative properties relate to the underlying halo conditions, and explore the physics of star formation activity in LAEs and its relation to possible physically related companions. We use data from the One-hundred-deg$^2$ DECam Imaging in Narrowbands (ODIN) survey, which targets LAEs in three narrow redshift slices. We identify physically associated LAE multiples in the COSMOS field at $z = 2.4$, $z = 3.1$, and $z=4.5$, and use a mock catalog from the IllustrisTNG100 simulation to assess the completeness and contamination affecting the resulting sample of LAE multiples. We then study their statistical and radiative properties as a function of multiplicity, where we adopt the term multiplicity to refer to the number of physically associated LAEs. We find a strong correlation between LAE multiplicity and host halo mass in the mocks, with higher multiplicity systems preferentially occupying more massive halos. In both ODIN and the mock sample, we find indications that the mean Ly$α$ luminosity and UV magnitude of LAEs in multiples increase with multiplicity. The halo-wide LAE surface brightness densities in Ly$α$ and UV increase with multiplicity, reflecting more compact and actively star-forming environments. The close agreement between the model and ODIN observations supports the validity of the Ly$α$ emission model in capturing key physical processes in LAE environments. Finally, a subhalo-based perturbation induced star formation model reproduces the minimum subhalo mass distribution in simulations at $z=2.4$, suggesting that local perturbations, rather than the presence of LAE companions, drive star formation in these systems. For the higher redshifts, neighbor perturbations do not seem to be the main driver that triggers star formation.

astro-ph.GA↗

arXiv2025

K-DRIFT: Unveiling New Imagery of the Hidden Universe

Low-surface-brightness (LSB) structures play a crucial role in understanding galaxy evolution by providing significant insights into galaxy interactions, the histories of mass assembly, and the distribution of dark matter. Nevertheless, their inherently faint nature, coupled with observational difficulties such as stray light interference and variations in the sky background, has significantly impeded comprehensive studies of LSB features. The KASI Deep Rolling Imaging Fast Telescope (K-DRIFT) project aims to address these observational challenges by developing off-axis freeform three-mirror telescopes and observational strategies specifically designed for LSB imaging surveys. The first generation of the K-DRIFT (K-DRIFT G1) has been successfully completed, and the forthcoming survey, scheduled to commence shortly, is expected to yield novel insights into the LSB universe. This paper outlines the scientific motivations of the project, discusses the technical challenges encountered, highlights the innovative solutions devised, and describes the future trajectory of the K-DRIFT.

astro-ph.GA↗

arXiv2025

Parameter-Free Hypergraph Neural Network for Few-Shot Node Classification

Few-shot node classification on hypergraphs requires models that generalize from scarce labels while capturing high-order structures. Existing hypergraph neural networks (HNNs) effectively encode such structures but often suffer from overfitting and scalability issues due to complex, black-box architectures. In this work, we propose ZEN (Zero-Parameter Hypergraph Neural Network), a fully linear and parameter-free model that achieves both expressiveness and efficiency. Built upon a unified formulation of linearized HNNs, ZEN introduces a tractable closed-form solution for the weight matrix and a redundancy-aware propagation scheme to avoid iterative training and to eliminate redundant self information. On 11 real-world hypergraph benchmarks, ZEN consistently outperforms eight baseline models in classification accuracy while achieving up to 696x speedups over the fastest competitor. Moreover, the decision process of ZEN is fully interpretable, providing insights into the characteristic of a dataset. Our code and datasets are fully available at https://github.com/chaewoonbae/ZEN.

cs.LG↗

arXiv2025

Modeling the Optical Colors of Galactic Cirrus Clouds in the Stripe 82 Region

Observations have shown that the optical colors of Galactic cirrus clouds differ significantly from those of extragalactic sources; thus, they can be used to distinguish Galactic cirrus from extragalactic low surface brightness (LSB) features. To understand these properties, we calculate radiative transfer models in dust clouds, where photons are incident from the ambient interstellar medium (ISM). Dust clouds are modeled to mimic a turbulent medium using a fractional Brownian motion algorithm, resulting in a lognormal density distribution and a power-law power spectral density that are appropriate for the ISM. The results are compared with optical observations of cirrus clouds in the Stripe 82 region. The observed color--color ($g-r$, $r-i$, and $i-z$) diagrams of the cirrus clouds can be reproduced by scattered light if the interstellar radiation field (ISRF) of Mathis et al. (as updated by Draine) is modified, either by reducing the intensities in the $i$ and $z$ bands or by enhancing those in the $g$ and $r$ bands. Similar results can also be obtained by adjusting the scattering albedos at the corresponding wavelengths. This demonstrates that the color--color diagrams are effective not only for identifying extragalactic LSB features but also for studying the ISRF and the properties of interstellar dust.

astro-ph.GA↗

arXiv2025

BPL: Bias-adaptive Preference Distillation Learning for Recommender System

Recommender systems suffer from biases that cause the collected feedback to incompletely reveal user preference. While debiasing learning has been extensively studied, they mostly focused on the specialized (called counterfactual) test environment simulated by random exposure of items, significantly degrading accuracy in the typical (called factual) test environment based on actual user-item interactions. In fact, each test environment highlights the benefit of a different aspect: the counterfactual test emphasizes user satisfaction in the long-terms, while the factual test focuses on predicting subsequent user behaviors on platforms. Therefore, it is desirable to have a model that performs well on both tests rather than only one. In this work, we introduce a new learning framework, called Bias-adaptive Preference distillation Learning (BPL), to gradually uncover user preferences with dual distillation strategies. These distillation strategies are designed to drive high performance in both factual and counterfactual test environments. Employing a specialized form of teacher-student distillation from a biased model, BPL retains accurate preference knowledge aligned with the collected feedback, leading to high performance in the factual test. Furthermore, through self-distillation with reliability filtering, BPL iteratively refines its knowledge throughout the training process. This enables the model to produce more accurate predictions across a broader range of user-item combinations, thereby improving performance in the counterfactual test. Comprehensive experiments validate the effectiveness of BPL in both factual and counterfactual tests. Our implementation is accessible via: https://github.com/SeongKu-Kang/BPL.

cs.LG↗

arXiv2025

Real Space Imaging of Spin Scattering in Chirality-Induced Spin Selectivity

The interaction between electron spin and molecular chirality plays a fundamental role in quantum phenomena, with significant implications for spintronics and quantum computing. The chirality-induced spin selectivity (CISS) effect, where chiral materials preferentially transmit electrons of a particular spin, has sparked intense interest and debate regarding its underlying mechanism. Despite extensive research, the spatial distribution of spin polarization in chiral systems, the key evidence to reveal the spin scattering mechanism in CISS, has remained experimentally elusive particularly due to complications arising from spin-orbit coupling in metal electrodes typically used in such studies. Here we show, through reflective magnetic circular dichroism measurements on chiral tellurium nanowires with graphene electrodes, that current-induced spin polarization exhibits identical signs in both the nanowire and electrodes, distinct from the presumed spin filter scenario. The observed spin polarization scales linearly with current amplitude, aligns parallel to the current direction, reverses with chirality or current flow, and demonstrates spin relaxation lengths of several micrometers into graphene. Our findings provide the first direct visualization of spatial spin distribution in chiral devices. This work establishes a new paradigm for investigating spin-dependent phenomena in chiral materials and opens avenues for developing chirality-based spintronic and quantum devices.

cond-mat.mes-hall↗

arXiv2025

Redshift Evolution of the Intrinsic Alignments of Early-Type Galaxies and Subhalos in the Horizon Run 5 Simulation

We investigate the redshift evolution of intrinsic alignments of the shapes of galaxies and subhalos with the large-scale structures of the universe using the cosmological hydrodynamic simulation, $\textit{Horizon Run 5}$. To this end, early-type galaxies are selected from the simulated galaxy catalogs based on stellar mass and kinematic morphology. The shapes of galaxies and subhalos are computed using the reduced inertia tensor derived from mass-weighted particle positions. We find that the misalignment between galaxies and their corresponding dark-matter subhalos decreases over time. We further analyze the two-point correlation between galaxy or subhalo shapes and the large-scale density field traced by their spatial distribution, and quantify the amplitude using the nonlinear alignment model across a wide redshift range from $z = 0.625$ to $z = 2.5$. We find that the intrinsic alignment amplitude, $A_{\rm NLA}$, of galaxies remains largely constant with redshift, whereas that of dark matter subhalos exhibits moderate redshift evolution, with a power-law slope that deviates from zero at a significance level exceeding $3σ$. Additionally, $A_{\rm NLA}$ is found to depend on both the stellar mass and kinematic morphology of galaxies. Notably, our results are broadly consistent with existing observational constraints. Our findings are in good agreement with previous results of other cosmological simulations.

astro-ph.CO↗

arXiv2025

Style Composition within Distinct LoRA modules for Traditional Art

Diffusion-based text-to-image models have achieved remarkable results in synthesizing diverse images from text prompts and can capture specific artistic styles via style personalization. However, their entangled latent space and lack of smooth interpolation make it difficult to apply distinct painting techniques in a controlled, regional manner, often causing one style to dominate. To overcome this, we propose a zero-shot diffusion pipeline that naturally blends multiple styles by performing style composition on the denoised latents predicted during the flow-matching denoising process of separately trained, style-specialized models. We leverage the fact that lower-noise latents carry stronger stylistic information and fuse them across heterogeneous diffusion pipelines using spatial masks, enabling precise, region-specific style control. This mechanism preserves the fidelity of each individual style while allowing user-guided mixing. Furthermore, to ensure structural coherence across different models, we incorporate depth-map conditioning via ControlNet into the diffusion framework. Qualitative and quantitative experiments demonstrate that our method successfully achieves region-specific style mixing according to the given masks.

cs.CV↗