arXiv subjects

Seunghyun Park

Publications and source records attributed to Seunghyun Park.

At least 19 recordsLinked to original sources

arXiv2026

Which Type Ia supernova observables best indicate the ages of their progenitor stars?

Type Ia supernovae (SNe Ia) observables, such as the light-curve shape and the colour, are expected to contain information about the progenitor star. In this work, we explore this information, with a particular focus on the age of the SN Ia progenitor star. For this, we construct the SN Ia progenitor age distribution (SPAD) and compare it to the observed distributions of light-curve shape (x1) and the colour (c) parameters in a volume-limited SN Ia sample. We find that SPAD and the x1 distribution share a common shape: a young/high-x1 peak and an old/low-x1 bump in the tail, and this shape varies systematically with redshift. In contrast, this behaviour is not evident in the c distribution. We then examine the correlation of the local age at the SN Ia explosion site, used as a proxy for the progenitor age, with x1 and c. The local age and x1 are well correlated (the linear correlation coefficient ~ -0.71), whereas the local age and c show no significant correlation (the coefficient ~ 0.08). Furthermore, we find that the x1 distribution systematically evolves with the local age. Lastly, we demonstrate that an empirical mapping approach based on SPAD successfully reproduces the observed x1 distribution across different redshift bins. Taken together, our results suggest that the light-curve shape distribution indicates progenitor age at the population level more robustly than the colour does. In particular, younger progenitors are more likely to have higher-x1 SNe Ia. We discuss an application for creating a more homogeneous sample of SNe Ia in terms of progenitor age across a wide redshift range without the Malmquist bias, thereby improving the accuracy of cosmological constraints derived from SNe Ia.

astro-ph.HE↗

arXiv2026

Still non-accelerating: age-bias correction in supernova cosmology is robust to host-progenitor age mapping

We re-examine the claim by Wiseman et al. (2026) that progenitor-age bias has a negligible impact on cosmological inferences from Type Ia supernovae (SNe Ia). We show that their inferred host-age-Hubble residual (HR) slope is severely underestimated because their combined SN Ia sample spans an unusually wide redshift range ($0.04 < z < 0.42$), over which the mean host age evolves by $\sim$\,3 Gyr. As a result, SNe Ia spanning substantial host-age differences are effectively assigned similar HR values prior to regression, artificially flattening the inferred age-HR relation. In addition, their application of the Pantheon+ host-mass correction further suppresses the slope, but the underlying dust model is highly incompatible with the measured dust attenuation curves of galaxies. We also demonstrate that our age bias correction is robust to uncertainties in host-progenitor age mapping arising from different choices of the SN Ia delay-time distribution. The reduced progenitor-age evolution argued by Wiseman et al. (2026) must, by the same logic, be accompanied by a steeper inferred progenitor-age-HR slope. When these two effects are consistently combined in computing the redshift-dependent magnitude correction, the final correction, and hence the resulting cosmological impact, remain largely unchanged from Son et al. (2025).

astro-ph.CO↗

arXiv2026

Strong Progenitor Age Bias in Supernova Cosmology. III. Progenitor Age as the Physical Origin of the Type Ia Supernova Magnitude Steps with Host Properties

The standardized magnitude of a type Ia supernova (SN Ia) correlates with host-galaxy properties, and a host mass-step correction is now routinely included in SN Ia luminosity standardization. Given that host mass cannot directly influence SN Ia luminosity, the root cause of the step must be another latent parameter associated with host mass. Identifying this driver is essential because different host properties evolve differently with redshift, so corrections based on them can lead to divergent cosmological inferences. In recent years, direct and extensive age measurements have revealed a significant relation between host age and Hubble residual (HR). Here, using a new dataset, we confirm that this relation arises from the age dependence of the SN Ia luminosity standardization process and the resulting overcorrection. Specifically, we show that while the mass-step correction reduces the age bias by about half, the host age-bias correction fully eliminates the mass step, supporting a progenitor-age origin of the host-age--HR relation. We further demonstrate that the SN Ia magnitude steps with host mass (and specific star formation rate; sSFR) emerge from a nonlinear, step-like relation between mass (and sSFR) and progenitor age, combined with a linear progenitor-age--HR relation: the SN Ia magnitude steps are therefore projected manifestations of an underlying dependence on progenitor age. Taken together, our results show that progenitor age is the primary driver of both the strong host-age--HR relation and the apparent host-mass and host-sSFR steps.

astro-ph.GA↗

arXiv2026

Where Reasoning Breaks: Logic-Aware Path Selection by Controlling Logical Connectives in LLMs Reasoning Chains

While LLMs demonstrate impressive reasoning capabilities, they remain fragile in multi-step logical deduction, where a single transition error can propagate through the entire reasoning chain, leading to unstable performance. In this work, we identify logical connectives as primary points of this structural fragility. Through empirical analysis, we show that connective tokens function as high entropy forking points, at which models frequently struggle to determine the correct logical direction. Motivated by this observation, we hypothesize that intervening in logical connective selection can guide LLMs toward more correct logical direction, thereby improving the overall reasoning chain. To validate this hypothesis, we propose a multi-layered framework that intervenes specifically at these logic-critical junctions in the reasoning process. Our framework includes (1) Gradient-based Logical Steering to guide LLMs internal representations towards valid reasoning subspaces, (2) Localized Branching to resolve ambiguity via targeted look-ahead search, and (3) Targeted Transition Preference Optimization, a surgical reinforcement learning objective that selectively optimizes single-token preferences at logical pivots. Crucially, by concentrating intervention solely on logic-critical transitions, our framework achieves a favorable accuracy--efficiency trade-off compared to global inference time scaling methods like beam search and self-consistency.

cs.CL↗

arXiv2026

How Robust is the Cosmic Distance with Tip of Red Giant Branch against Stellar Population Variations?

The tip of the red giant branch (TRGB) provides a key standard candle for extragalactic distance measurements and for refining the Hubble constant. We test its robustness by quantifying how metallicity, $α$-element enhancement, age, and initial helium abundance modulate the TRGB luminosity, using synthetic composite color--magnitude diagrams in the $I$ and $F814W$ bands. We find that metallicity and $α$-element enhancement are the primary drivers of TRGB variation, while age introduces only a modest effect and helium abundance is negligible. At fixed age and helium content, increasing the mean metallicity by 0.5 dex or the $α$-element enhancement by 0.3 dex produces the well-known systematic dimming of 0.046 and 0.050 mag, respectively, in $M_I^{\rm TRGB}$, and of 0.093 and 0.044 mag, respectively, in $M_{F814W}^{\rm TRGB}$. By comparison, changes in age of 3~Gyr and in initial helium abundance of 0.10 yield minor luminosity shifts, with average changes of 0.031 and 0.009~mag, respectively, in $M_I^{\rm TRGB}$, and of 0.035 and 0.027 mag, respectively, in $M_{F814W}^{\rm TRGB}$, substantially smaller than those caused by variations in metallicity or $α$-element enhancement. For mixed stellar populations under typical stellar-halo metallicity conditions, the net variation in $M_I^{\rm TRGB}$ arising from each combination of the $α$-element enhancement, age, and initial helium abundance remains below 0.028~mag, well within reported systematic uncertainties. Together, these results reaffirm the TRGB as a highly robust distance indicator and support its continued use as an independent anchor for precision cosmology in the era of the Hubble-tension debate.

astro-ph.GA↗

arXiv2026

Focus, Don't Prune: Identifying Instruction-Relevant Regions for Information-Rich Image Understanding

Large Vision-Language Models (LVLMs) have shown strong performance across various multimodal tasks by leveraging the reasoning capabilities of Large Language Models (LLMs). However, processing visually complex and information-rich images, such as infographics or document layouts, requires these models to generate a large number of visual tokens, leading to significant computational overhead. To address this, we propose PinPoint, a novel two-stage framework that first identifies instruction-relevant image regions and then refines them to extract fine-grained visual features for improved reasoning and efficiency. Central to our approach is the Instruction-Region Alignment, which localizes relevant regions using both visual input and textual instructions. We further introduce new annotations that provide richer ground-truth supervision for instruction-relevant regions across challenging VQA benchmarks: InfographicVQA, MultiPageDocVQA, and SinglePageDocVQA. Experimental results show that PinPoint not only achieves superior accuracy compared to existing methods but also reduces computational overhead by minimizing irrelevant visual tokens.

cs.CV↗

arXiv2025

Watermarking for Factuality: Guiding Vision-Language Models Toward Truth via Tri-layer Contrastive Decoding

Large Vision-Language Models (LVLMs) have recently shown promising results on various multimodal tasks, even achieving human-comparable performance in certain cases. Nevertheless, LVLMs remain prone to hallucinations -- they often rely heavily on a single modality or memorize training data without properly grounding their outputs. To address this, we propose a training-free, tri-layer contrastive decoding with watermarking, which proceeds in three steps: (1) select a mature layer and an amateur layer among the decoding layers, (2) identify a pivot layer using a watermark-related question to assess whether the layer is visually well-grounded, and (3) apply tri-layer contrastive decoding to generate the final output. Experiments on public benchmarks such as POPE, MME and AMBER demonstrate that our method achieves state-of-the-art performance in reducing hallucinations in LVLMs and generates more visually grounded responses.

cs.CV↗

arXiv2025

Strong Progenitor Age-bias in Supernova Cosmology. II. Alignment with DESI BAO and Signs of a Non-Accelerating Universe

Supernova (SN) cosmology is based on the key assumption that the luminosity standardization process of Type Ia SNe remains invariant with progenitor age. However, direct and extensive age measurements of SN host galaxies reveal a significant (5.5σ) correlation between standardized SN magnitude and progenitor age, which is expected to introduce a serious systematic bias with redshift in SN cosmology. This systematic bias is largely uncorrected by the commonly used mass-step correction, as progenitor age and host galaxy mass evolve very differently with redshift. After correcting for this age-bias as a function of redshift, the SN dataset aligns more closely with the w0waCDM model recently suggested by the DESI BAO project from a combined analysis using only BAO and CMB data. This result is further supported by an evolution-free test that uses only SNe from young, coeval host galaxies across the full redshift range. When the three cosmological probes (SNe, BAO, CMB) are combined, we find a significantly stronger (> 9σ) tension with the ΛCDM model than that reported in the DESI papers, suggesting a time-varying dark energy equation of state in a currently non-accelerating universe.

astro-ph.CO↗

arXiv2025

GDLNN: Marriage of Programming Language and Neural Networks for Accurate and Easy-to-Explain Graph Classification

We present GDLNN, a new graph machine learning architecture, for graph classification tasks. GDLNN combines a domain-specific programming language, called GDL, with neural networks. The main strength of GDLNN lies in its GDL layer, which generates expressive and interpretable graph representations. Since the graph representation is interpretable, existing model explanation techniques can be directly applied to explain GDLNN's predictions. Our evaluation shows that the GDL-based representation achieves high accuracy on most graph classification benchmark datasets, outperforming dominant graph learning methods such as GNNs. Applying an existing model explanation technique also yields high-quality explanations of GDLNN's predictions. Furthermore, the cost of GDLNN is low when the explanation cost is included.

cs.LG↗

arXiv2025

Strong progenitor age bias in supernova cosmology. I. Robust and ubiquitous evidence from a larger sample of host galaxies in a broader redshift range

Type Ia supernovae (SNe Ia) serve as the most crucial standardizable candles in cosmology, providing direct measurements of the universe's expansion history. However, it is well-known that the post-standardization brightness of SNe Ia is influenced by the properties of their host galaxies, such as mass and star formation rate, both of which are closely related to progenitor age. In this study, by measuring the stellar population ages of SN host galaxies, we reaffirm the ubiquitous and robust correlation between SN Ia luminosity and host age, showing that this host property dependence arises primarily from stellar population age of the host galaxy. This analysis was conducted using an expanded sample of over 300 hosts across a broad redshift range up to $z \sim 0.4$, ensuring sufficient statistical significance of the result. To quantify the relationship between host age and Hubble residual (HR), we employed two linear regression techniques: LINMIX, which assumes a Gaussian age error, and Bayesian hierarchical linear regression, which utilizes a full posterior for the age error. Both models demonstrate a robust correlation between host age and HR, with high statistical significance approaching $5.5 σ$. While our new regression analyses yield the slopes that are similar or slightly shallower compared to our previous results, the significance of these slopes has notably increased. These findings robustly validate our previous suggestions that post-standardization SN Ia luminosity varies with progenitor age, which is currently not properly accounted for in SN cosmology.

astro-ph.GA↗

arXiv2024

On-Chip Terahertz Spectroscopy for Dual-Gated van der Waals Heterostructures at Cryogenic Temperatures

Van der Waals heterostructures have emerged as a versatile platform to study correlated and topological electron physics. Spectroscopy experiments in the THz regime are crucial, since the energy of THz photons matches that of relevant excitations and charge dynamics. However, their micron-size and complex (dual-)gated structures have challenged such measurements. Here, we demonstrate on-chip THz spectroscopy on a dual-gated bilayer graphene device at liquid helium temperature. To avoid unwanted THz absorption by metallic gates, we developed a scheme of operation by combining semiconducting gates and optically controlled gating. This allows us to measure the clean THz response of graphene without being affected by the gates. We observed the THz signatures of electric-field-induced bandgap opening at the charge neutrality. We measured Drude conductivities at varied charge densities and extracted key parameters, including effective masses and scattering rates. This work paves the way for studying novel emergent phenomena in dual-gated two-dimensional materials.

cond-mat.mes-hall↗

arXiv2024

An antiferromagnetic diode effect in even-layered MnBi2Te4

In a PN junction, the separation between positive and negative charges leads to diode transport. In the past few years, the intrinsic diode transport in noncentrosymmetric polar conductors has attracted great interest, because it suggests novel nonlinear applications and provides a symmetry-sensitive probe of Fermi surface. Recently, such studies have been extended to noncentrosymmetric superconductors, realizing the superconducting diode effect. Here, we show that, even in a centrosymmetric crystal without directional charge separation, the spins of an antiferromagnet (AFM) can generate a spatial directionality, leading to an AFM diode effect. We observe large second-harmonic transport in a nonlinear electronic device enabled by the compensated AFM state of even-layered MnBi2Te4. We also report a novel electrical sum-frequency generation (SFG), which has been rarely explored in contrast to the well-known optical SFG in wide-gap insulators. We demonstrate that the AFM enables an in-plane field-effect transistor and harvesting of wireless electromagnetic energy. The electrical SFG establishes a powerful method to study nonlinear electronics built by quantum materials. The AFM diode effect paves the way for potential device concepts including AFM logic circuits, self-powered AFM spintronics, and other applications that potentially bridge nonlinear electronics with AFM spintronics.

cond-mat.str-el↗

arXiv2024

Current Induced Hidden States in Josephson Junctions

Josephson junctions enable dissipation-less electrical current through metals and insulators below a critical current. Despite being central to quantum technology based on superconducting quantum bits and fundamental research into self-conjugate quasiparticles, the spatial distribution of super current flow at the junction and its predicted evolution with current bias and external magnetic field remain experimentally elusive. Revealing the hidden current flow, featureless in electrical resistance, helps understanding unconventional phenomena such as the nonreciprocal critical current, i.e., Josephson diode effect. Here we introduce a platform to visualize super current flow at the nanoscale. Utilizing a scanning magnetometer based on nitrogen vacancy centers in diamond, we uncover competing ground states electrically switchable within the zero-resistance regime. The competition results from the superconducting phase re-configuration induced by the Josephson current and kinetic inductance of thin-film superconductors. We further identify a new mechanism for the Josephson diode effect involving the Josephson current induced phase. The nanoscale super current flow emerges as a new experimental observable for elucidating unconventional superconductivity, and optimizing quantum computation and energy-efficient devices.

cond-mat.mes-hall↗

arXiv2024

EGTR: Extracting Graph from Transformer for Scene Graph Generation

Scene Graph Generation (SGG) is a challenging task of detecting objects and predicting relationships between objects. After DETR was developed, one-stage SGG models based on a one-stage object detector have been actively studied. However, complex modeling is used to predict the relationship between objects, and the inherent relationship between object queries learned in the multi-head self-attention of the object detector has been neglected. We propose a lightweight one-stage SGG model that extracts the relation graph from the various relationships learned in the multi-head self-attention layers of the DETR decoder. By fully utilizing the self-attention by-products, the relation graph can be extracted effectively with a shallow relation extraction head. Considering the dependency of the relation extraction task on the object detection task, we propose a novel relation smoothing technique that adjusts the relation label adaptively according to the quality of the detected objects. By the relation smoothing, the model is trained according to the continuous curriculum that focuses on object detection task at the beginning of training and performs multi-task learning as the object detection performance gradually improves. Furthermore, we propose a connectivity prediction task that predicts whether a relation exists between object pairs as an auxiliary task of the relation extraction. We demonstrate the effectiveness and efficiency of our method for the Visual Genome and Open Image V6 datasets. Our code is publicly available at https://github.com/naver-ai/egtr.

cs.CV↗

arXiv2024

CREPE: Coordinate-Aware End-to-End Document Parser

In this study, we formulate an OCR-free sequence generation model for visual document understanding (VDU). Our model not only parses text from document images but also extracts the spatial coordinates of the text based on the multi-head architecture. Named as Coordinate-aware End-to-end Document Parser (CREPE), our method uniquely integrates these capabilities by introducing a special token for OCR text, and token-triggered coordinate decoding. We also proposed a weakly-supervised framework for cost-efficient training, requiring only parsing annotations without high-cost coordinate annotations. Our experimental evaluations demonstrate CREPE's state-of-the-art performances on document parsing tasks. Beyond that, CREPE's adaptability is further highlighted by its successful usage in other document understanding tasks such as layout analysis, document visual question answering, and so one. CREPE's abilities including OCR and semantic parsing not only mitigate error propagation issues in existing OCR-dependent methods, it also significantly enhance the functionality of sequence generation models, ushering in a new era for document understanding studies.

cs.CV↗

arXiv2023

Visually-Situated Natural Language Understanding with Contrastive Reading Model and Frozen Large Language Models

Recent advances in Large Language Models (LLMs) have stimulated a surge of research aimed at extending their applications to the visual domain. While these models exhibit promise in generating abstract image captions and facilitating natural conversations, their performance on text-rich images still requires improvement. In this paper, we introduce Contrastive Reading Model (Cream), a novel neural architecture designed to enhance the language-image understanding capability of LLMs by capturing intricate details that are often overlooked in existing methods. Cream combines vision and auxiliary encoders, fortified by a contrastive feature alignment technique, to achieve a more effective comprehension of language information in visually situated contexts within the images. Our approach bridges the gap between vision and language understanding, paving the way for the development of more sophisticated Document Intelligence Assistants. Through rigorous evaluations across diverse visually-situated language understanding tasks that demand reasoning capabilities, we demonstrate the compelling performance of Cream, positioning it as a prominent model in the field of visual document understanding. We provide our codebase and newly-generated datasets at https://github.com/naver-ai/cream .

cs.CL↗

arXiv2023

On the root cause of the host `mass-step' in the Hubble residuals of type Ia supernovae

It is well established that the Hubble residuals of type Ia supernovae (SNe Ia) show the luminosity step with respect to their host galaxy stellar masses. This `mass-step' is taken as an additional correction factor for the SN Ia luminosity standardization. Here we investigate the root cause of the mass-step and propose that the bimodal nature of the host $age$ distribution is responsible for the step. In particular, by using the empirical $nonlinear$ mass-to-age relation of local galaxies, we convert the mass function of SN Ia hosts to their age distribution. We find that the age distribution shows clear bimodality: a younger ($<$ 6 Gyr) group with lower mass ($\sim 10^{9.5}{\rm M}_{\rm sun}$) and an older ($>$ 6 Gyr) group with higher mass ($\sim 10^{10.5}{\rm M}_{\rm sun}$). On the Hubble residual versus host mass plane, the two groups create the mass-step at $\sim 10^{10}{\rm M}_{\rm sun}$. This leads us to conclude that the host galaxy mass-step can be attributed to the bimodal age distribution in relation to a nonlinear relation between galaxy mass and age. We suggest that the mass-step is another manifestation of the old `red sequence' and the young `blue cloud' observed in the galactic color--magnitude diagram.

astro-ph.GA↗

arXiv2022

OCR-free Document Understanding Transformer

Understanding document images (e.g., invoices) is a core but challenging task since it requires complex functions such as reading text and a holistic understanding of the document. Current Visual Document Understanding (VDU) methods outsource the task of reading text to off-the-shelf Optical Character Recognition (OCR) engines and focus on the understanding task with the OCR outputs. Although such OCR-based approaches have shown promising performance, they suffer from 1) high computational costs for using OCR; 2) inflexibility of OCR models on languages or types of document; 3) OCR error propagation to the subsequent process. To address these issues, in this paper, we introduce a novel OCR-free VDU model named Donut, which stands for Document understanding transformer. As the first step in OCR-free VDU research, we propose a simple architecture (i.e., Transformer) with a pre-training objective (i.e., cross-entropy loss). Donut is conceptually simple yet effective. Through extensive experiments and analyses, we show a simple OCR-free VDU model, Donut, achieves state-of-the-art performances on various VDU tasks in terms of both speed and accuracy. In addition, we offer a synthetic data generator that helps the model pre-training to be flexible in various languages and domains. The code, trained model and synthetic data are available at https://github.com/clovaai/donut.

cs.LG↗