arXiv subjects

Lei Hu

Publications and source records attributed to Lei Hu.

At least 55 records · Page 3Linked to original sources

arXiv2025

AT2025ulz and S250818k: Investigating early time observations of a subsolar mass gravitational-wave binary neutron star merger candidate

Over the past LIGO--Virgo--KAGRA (LVK) observing runs, it has become increasingly clear that identifying the next electromagnetic counterparts to gravitational-wave (GW) neutron star mergers will likely be more challenging compared to the case of GW170817. The rarity of these GW events, and their electromagnetic counterparts, motivates rapid searches of any candidate binary neutron star (BNS) merger detected by the LVK. We present our extensive photometric and spectroscopic campaign of the candidate counterpart AT2025ulz to the low-significance GW event S250818k, which had a ${\sim} 29\%$ probability of being a BNS merger. We demonstrate that during the first five days, the luminosity and color evolution of AT2025ulz are consistent with both kilonova and shock cooling models, although a Bayesian model comparison shows preference for the shock cooling model, underscoring the ambiguity inherent to early data obtained over only a few days. Continued monitoring beyond this window reveals a rise and color evolution incompatible with kilonova models and instead consistent with a supernova. This event emphasizes the difficulty in identifying the electromagnetic counterparts to BNS mergers and the significant allotment of observing time necessary to robustly differentiate kilonovae from impostors.

astro-ph.HE↗

arXiv2025

AT2025ulz and S250818k: Leveraging DESI spectroscopy in the hunt for a kilonova associated with a sub-solar mass gravitational wave candidate

On August 18th, 2025, the LIGO--Virgo--KAGRA collaboration reported a sub-threshold gravitational wave candidate detection consistent with a sub-solar-mass neutron star merger, denoted S250818k. An optical transient, AT2025ulz, was discovered within the localization region. AT2025ulz initially appeared to meet the expected behavior of kilonova (KN) emission, the telltale signature of a binary neutron star merger. The transient subsequently rebrightened after $\sim$\,$5$ days and developed spectral features characteristic of a Type IIb supernova. In this work, we analyze the observations of the host galaxy of AT2025ulz obtained by the Dark Energy Spectroscopic Instrument (DESI). From the DESI spectrum, we obtain a secure redshift of $z = 0.084840 \pm 0.000006$, which places the transient within $2σ$ of the gravitational wave distance and results in an integral overlap between the gravitational wave alert and the transient location of $\log_{10}\mathcal{I} \approx 3.9-4.2$. Our analysis of the host galaxy's spectral energy distribution reveals a star-forming, dusty galaxy with stellar mass ${\sim} 10^{10}~M_\odot$, broadly consistent with the population of both short gamma-ray bursts and core-collapse supernova host galaxies. We also present our follow-up of DESI-selected candidate host galaxies using the Fraunhofer Telescope at the Wendelstein Observatory, and show the promise of DESI for associating or rejecting candidate electromagnetic counterparts to gravitational wave alerts. These results emphasize the value of DESI's extensive spectroscopic dataset in rapidly characterizing host galaxies, enabling spectroscopic host subtraction, and guiding targeted follow-up.

astro-ph.HE↗

arXiv2025

phrosty: A difference imaging pipeline for Roman

NASA's Nancy Grace Roman Space Telescope (Roman) will provide an opportunity to study dark energy with unprecedented precision using several techniques, including measurements of Type Ia Supernovae (SNe Ia). Here, we present `phrosty` (PHotometry for ROman with SFFT for tYpe Ia supernovae): a difference imaging pipeline for measuring the brightness of transient point sources in the sky, primarily SNe Ia, using Roman data. `phrosty` is written in Python. We implement a GPU-accelerated version of the Saccadic Fast Fourier Transform (SFFT) method for difference imaging.

astro-ph.IM↗

arXiv2025

The curious case of EP241021a: Unraveling the mystery of its exceptional rebrightening

Fast X-ray Transients (FXTs) are a rare and poorly understood phenomenon with a variety of possible progenitors. The launch of the Einstein Probe (EP) mission has facilitated a rapid increase in the real-time discovery and follow-up of FXTs. We focus on the recent EP discovered transient EP241021a, which shows a peculiar panchromatic behavior. We obtained optical and near-infrared multi-band imaging and spectroscopy with the Fraunhofer Telescope at Wendelstein Observatory, the Hobby-Eberly Telescope, and the Very Large Telescope over the first 100 days of its evolution. EP241021a was discovered by EP as a soft X-ray trigger, but was not detected at gamma-ray frequencies. The observed soft X-ray prompt emission spectrum is consistent with non-thermal radiation, which requires at least a mildly relativistic outflow with bulk Lorentz factor $Γ\gtrsim 4$. The optical and near-infrared lightcurve has a two component behavior where an initially fading component $\sim t^{-1}$ turns to a rise steeper than $\sim t^{4}$ after a few days before peaking at $M_r\approx -21.8$ mag and quickly returning to the initial decay. The peak absolute magnitude is the most luminous optical emission associated to an FXT, superseding EP240414a. Standard supernova models are unable to reproduce either the absolute magnitude or rapid timescale ($<2$ d) of the rebrightening. The X-ray, optical and near-infrared spectral energy distributions display a red color $r-J\approx 0.8$ mag, and point to a non-thermal origin ($ν^{-1}$) for the broadband emission. By considering a gamma-ray burst as a plausible scenario, we favor a refreshed shock as the cause of the rebrightening. This is consistent with the inference of an at least mildly relativistic outflow based on the prompt trigger. Our results suggest a link between EP discovered FXTs and gamma-ray bursts.

astro-ph.HE↗

arXiv2025

LIGO/Virgo/KAGRA neutron star merger candidate S250206dm: Zwicky Transient Facility observations

We present the searches conducted with the Zwicky Transient Facility (ZTF) in response to S250206dm, a bona fide event with a false alarm rate of one in 25 years, detected by the International Gravitational Wave Network (IGWN). Although the event is significant, the nature of the compact objects involved remains unclear, with at least one likely neutron star. ZTF covered 68% of the localization region, though we did not identify any likely optical counterpart. We describe the ZTF strategy, potential candidates, and the observations that helped rule out candidates, including sources circulated by other collaborations. Similar to Ahumada et al. 2024, we perform a frequentist analysis, using simsurvey, as well as Bayesian analysis, using nimbus, to quantify the efficiency of our searches. We find that, given the nominal distance to this event of 373$\pm$104 Mpc, our efficiencies are above 10% for KNe brighter than $-17.5$ absolute magnitude. Assuming the optical counterpart known as kilonova (KN) lies within the ZTF footprint, our limits constrain the brightest end of the KN parameter space. Through dedicated radiative transfer simulations of KNe from binary neutron star (BNS) and black hole-neutron star (BHNS) mergers, we exclude parts of the BNS KN parameter space. Up to 35% of the models with high wind ejecta mass ($M_{\rm wind} \approx 0.13$ M$_{\odot}$) are ruled out when viewed face-on ($\cosθ_{\rm obs} = 1.0$). Finally, we present a joint analysis using the combined coverage from ZTF and the Gravitational Wave Multimessenger Dark Energy Camera Survey (GW-MMADS). The joint observations cover 73% of the localization region, and the combined efficiency has a stronger impact on rising and slowly fading models, allowing us to rule out 55% of the high-mass KN models viewed face-on.

astro-ph.HE↗

arXiv2025

Kilonova constraints for the LIGO/Virgo/KAGRA neutron star merger candidate S250206dm: GW-MMADS observations

Gravitational wave (GW) neutron star mergers with an associated electromagnetic counterpart constitute powerful probes of binary evolution, the production sites of heavy elements, general relativity, and the expansion of the universe. Only a handful of candidate GW binary mergers during the fourth LIGO/Virgo/KAGRA observing run (O4) so far are believed to include a neutron star. We present optical-near infrared follow-up observations of the candidate neutron-star black hole GW merger S250206dm. This is the first high-significance mass gap neutron star-black hole candidate observed by multiple GW detectors (thus having a significantly smaller sky localization than one-detector events), offering the first opportunity to effectively follow up a GW event of this kind. Our GW MultiMessenger Astronomy DECam Survey (GW-MMADS) campaign consisted of a wide-field search using the Dark Energy Camera (DECam) and T80-South (T80S), as well as galaxy-targeted observations using the Southern Astrophysical Research (SOAR) imager and the Wendelstein 2.1m 3-channel camera. No viable kilonova counterpart was found in our observations. We use our observation depths to place competitive constraints on kilonova models similar to or brighter than the GW170817 kilonova AT 2017gfo within our observed fields, ruling out 100\% of such models with SOAR galaxy-targeted observations and $\sim43$\% (48\%) with DECam (DECam and T80S).

astro-ph.HE↗

arXiv2025

AT 2018dyk: tidal disruption event or active galactic nucleus? Follow-up observations of an extreme coronal line emitter with the Dark Energy Spectroscopic Instrument

We present fresh insights into the nature of the tidal disruption event (TDE) candidate AT 2018dyk. AT 2018dyk has sparked a debate in the literature around its classification as either a bona-fide TDE or as an active galactic nucleus (AGN) turn-on state change. A new follow-up spectrum taken with the Dark Energy Spectroscopic Instrument, in combination with host-galaxy analysis using archival SDSS-MaNGA data, supports the identification of AT 2018dyk as a TDE. Specifically, we classify this object as a TDE that occurred within a gas-rich environment, which was responsible for both its mid-infrared (MIR) outburst and development of Fe coronal emission lines. Comparison with the known sample of TDE-linked extreme coronal line emitters (TDE-ECLEs) and other TDEs displaying coronal emission lines (CrL-TDEs) reveals similar characteristics and shared properties. For example, the MIR properties of both groups appear to form a continuum with links to the content and density of the material in their local environments. This includes evidence for a MIR colour-luminosity relationship in TDEs occurring within such gas-rich environments, with those with larger MIR outbursts also exhibiting redder peaks.

astro-ph.HE↗

arXiv2025

Structure-Accurate Medical Image Translation via Dynamic Frequency Balance and Knowledge Guidance

Multimodal medical images play a crucial role in the precise and comprehensive clinical diagnosis. Diffusion model is a powerful strategy to synthesize the required medical images. However, existing approaches still suffer from the problem of anatomical structure distortion due to the overfitting of high-frequency information and the weakening of low-frequency information. Thus, we propose a novel method based on dynamic frequency balance and knowledge guidance. Specifically, we first extract the low-frequency and high-frequency components by decomposing the critical features of the model using wavelet transform. Then, a dynamic frequency balance module is designed to adaptively adjust frequency for enhancing global low-frequency features and effective high-frequency details as well as suppressing high-frequency noise. To further overcome the challenges posed by the large differences between different medical modalities, we construct a knowledge-guided mechanism that fuses the prior clinical knowledge from a visual language model with visual features, to facilitate the generation of accurate anatomical structures. Experimental evaluations on multiple datasets show the proposed method achieves significant improvements in qualitative and quantitative assessments, verifying its effectiveness and superiority.

cs.CV↗

arXiv2025

ReplayCAD: Generative Diffusion Replay for Continual Anomaly Detection

Continual Anomaly Detection (CAD) enables anomaly detection models in learning new classes while preserving knowledge of historical classes. CAD faces two key challenges: catastrophic forgetting and segmentation of small anomalous regions. Existing CAD methods store image distributions or patch features to mitigate catastrophic forgetting, but they fail to preserve pixel-level detailed features for accurate segmentation. To overcome this limitation, we propose ReplayCAD, a novel diffusion-driven generative replay framework that replay high-quality historical data, thus effectively preserving pixel-level detailed features. Specifically, we compress historical data by searching for a class semantic embedding in the conditional space of the pre-trained diffusion model, which can guide the model to replay data with fine-grained pixel details, thus improving the segmentation performance. However, relying solely on semantic features results in limited spatial diversity. Hence, we further use spatial features to guide data compression, achieving precise control of sample space, thereby generating more diverse data. Our method achieves state-of-the-art performance in both classification and segmentation, with notable improvements in segmentation: 11.5% on VisA and 8.1% on MVTec. Our source code is available at https://github.com/HULEI7/ReplayCAD.

cs.CV↗

arXiv2025

Learning Transformation-Isomorphic Latent Space for Accurate Hand Pose Estimation

Vision-based regression tasks, such as hand pose estimation, have achieved higher accuracy and faster convergence through representation learning. However, existing representation learning methods often encounter the following issues: the high semantic level of features extracted from images is inadequate for regressing low-level information, and the extracted features include task-irrelevant information, reducing their compactness and interfering with regression tasks. To address these challenges, we propose TI-Net, a highly versatile visual Network backbone designed to construct a Transformation Isomorphic latent space. Specifically, we employ linear transformations to model geometric transformations in the latent space and ensure that {\rm TI-Net} aligns them with those in the image space. This ensures that the latent features capture compact, low-level information beneficial for pose estimation tasks. We evaluated TI-Net on the hand pose estimation task to demonstrate the network's superiority. On the DexYCB dataset, TI-Net achieved a 10% improvement in the PA-MPJPE metric compared to specialized state-of-the-art (SOTA) hand pose estimation methods. Our code will be released in the future.

cs.CV↗

arXiv2025

Entanglement-Assisted Coding for Arbitrary Linear Computations Over a Quantum MAC

We study a linear computation problem over a quantum multiple access channel (LC-QMAC), where $S$ servers share an entangled state and separately store classical data streams $W_1,\cdots, W_S$ over a finite field $\mathbb{F}_d$. A user aims to compute $K$ linear combinations of these data streams, represented as $Y = \mathbf{V}_1 W_1 + \mathbf{V}_2 W_2 + \cdots + \mathbf{V}_S W_S \in \mathbb{F}_d^{K \times 1}$. To this end, each server encodes its classical information into its local quantum subsystem and transmits it to the user, who retrieves the desired computations via quantum measurements. In this work, we propose an achievable scheme for LC-QMAC based on the stabilizer formalism and the ideas from entanglement-assisted quantum error-correcting codes (EAQECC). Specifically, given any linear computation matrix, we construct a self-orthogonal matrix that can be implemented using the stabilizer formalism. Also, we apply precoding matrices to minimize the number of auxiliary qudits required. Our scheme achieves more computations per qudit, i.e., a higher computation rate, compared to the best-known methods in the literature, and attains the capacity in certain cases.

cs.IT↗

arXiv2025

Searching for electromagnetic emission in an AGN from the gravitational wave binary black hole merger candidate S230922g

We carried out long-term monitoring of the LIGO/Virgo/KAGRA binary black hole (BBH) merger candidate S230922g in search of electromagnetic emission from the interaction of the merger remnant with an embedding active galactic nuclei (AGN) accretion disk. Using a dataset primarily composed of wide-field imaging from the Dark Energy Camera (DECam) and supplemented by additional photometric and spectroscopic resources, we searched ~ 70% of the sky area probability for transient phenomena, and discovered 6 counterpart candidates. One especially promising candidate - AT 2023aagj - exhibited temporally varying asymmetric components in spectral broad line regions, a feature potentially indicative of an off-center event such as a BBH merger. This represents the first live search and multiwavelength, photometric, and spectroscopic monitoring of a GW BBH optical counterpart candidate in the disk of an AGN.

astro-ph.HE↗

arXiv2024

Early-time millimeter observations of the nearby Type II SN 2024ggi

The short-lived ionized emission lines in early spectroscopy of the nearby type II supernova SN 2024ggi signify the presence of dense circumstellar matter (CSM) close to its progenitor star. We proposed the Atacama Large Millimeter/submillimeter Array (ALMA) observations by its Director's Discretionary Time program to catch the potential synchrotron radiation associated with the ejecta-CSM interaction. Multi-epoch observations were conducted using ALMA band 6 at +8, +13, and +17 days after the discovery. The data show non-detections at the position of SN 2024ggi with a 3sigma upper limit of less than 0.15 mJy, corresponding to a luminosity of approximately 8*10^24 erg/s/Hz. In this paper, we leverage the non-detections to place constraints on the properties of CSM surrounding SN 2024ggi. We investigate both the Wind and Eruptive models for the radial distribution of CSM, assuming a constant mass-loss rate in the Wind model and a distance-variant mass-loss rate in the Eruptive model. The derived CSM distribution for the Wind model does not align with the early-time spectral features, while the ALMA observations suggest a mass-loss rate of ~ 5*10^-3 Msun/year for the Eruptive model. Conducting multi-epoch millimeter/submillimeter observations shortly after the explosion, with a cadence of a few days, could offer a promising opportunity to capture the observable signature of the Eruptive model.

astro-ph.SR↗

arXiv2024

Formation of Fe-6.5wt%Si High Silicon Steel by Double Glow Plasma Surface Metallurgy Technology

High silicon steel with 6.5% silicon content is the best because of its excellent magnetic properties, such as high saturation magnetization, high resistivity, low iron loss and near zero magnetostriction. High silicon steel can greatly save energy, and reduce the weight and size of electrical appliances. This has a very important application prospect for energy and aerospace industry. The high brittleness of high silicon steel makes its production and processing very difficult. For more than 30 years, many steel companies and research institutions around the world have adopted various technical means to study the industrialization of high silicon steel, but they have not been successful . JFE-NKK steel company in Japan has realized the small batch production of high silicon steel by using SiCl4-CVD technology. However, due to the complex process, corrosion and pollution, high cost, its production scale is greatly limited. So far, large-scale production of high silicon steel is still a major challenge in the world. This paper will introduce the experimental results of successfully preparing high silicon steel by Double Glow Plasma Surface Metallurgy Technology. The process is simple and easy without any corrosion or pollution, which may provide a new way for the world to achieve large-scale production of high silicon steel. The large-scale production and wide application of high silicon steel is likely to change the pattern of the world's energy and electric power industry, save a lot of energy for mankind, and create huge economic benefits.

physics.app-ph↗

arXiv2024

The classification of real and bogus transients using active learning and semi-supervised learning

Deep-learning-based methods have been favored in astrophysics owing to their adaptability and remarkable performance and have been applied to the task of the classification of real and bogus transients. Different from most existing approaches which necessitate massive yet expensive annotated data, We aim to leverage training samples with only 1000 labels available to discover real sources that vary in brightness over time in the early stage of the WFST 6-year survey. Methods. We present a novel deep-learning method that combines active learning and semi-supervised learning to construct a competitive real/bogus classifier. Our method incorporates an active learning stage, where we actively select the most informative or uncertain samples for annotation. This stage aims to achieve higher model performance by leveraging fewer labeled samples, thus reducing annotation costs and improving the overall learning process efficiency. Furthermore, our approach involves a semi-supervised learning stage that exploits the unlabeled data to enhance the model's performance and achieve superior results compared to using only the limited labeled data.

astro-ph.IM↗

arXiv2024

Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model

Motion artifacts present in magnetic resonance imaging (MRI) can seriously interfere with clinical diagnosis. Removing motion artifacts is a straightforward solution and has been extensively studied. However, paired data are still heavily relied on in recent works and the perturbations in k-space (frequency domain) are not well considered, which limits their applications in the clinical field. To address these issues, we propose a novel unsupervised purification method which leverages pixel-frequency information of noisy MRI images to guide a pre-trained diffusion model to recover clean MRI images. Specifically, considering that motion artifacts are mainly concentrated in high-frequency components in k-space, we utilize the low-frequency components as the guide to ensure correct tissue textures. Additionally, given that high-frequency and pixel information are helpful for recovering shape and detail textures, we design alternate complementary masks to simultaneously destroy the artifact structure and exploit useful information. Quantitative experiments are performed on datasets from different tissues and show that our method achieves superior performance on several metrics. Qualitative evaluations with radiologists also show that our method provides better clinical feedback. Our code is available at https://github.com/medcx/PFAD.

eess.IV↗

arXiv2024

Obliquely interacting solitary waves and wave wakes in free-surface flows

This paper investigates the weakly nonlinear isotropic bi-directional Benney--Luke (BL) equation, which is used to describe oceanic surface and internal waves in shallow water, with a particular focus on soliton dynamics. Using the Whitham modulation theory, we derive the modulation equations associated with the BL equation that describe the evolution of soliton amplitude and slope. By analyzing rarefaction waves and shock waves within these modulation equations, we derive the Riemann invariants and modified Rankine--Hugoniot conditions. These expressions help characterize the Mach expansion and Mach reflection phenomena of bent and reverse bent solitons. We also derive analytical formulas for the critical angle and the Mach stem amplitude, showing that as the soliton speed is in the vicinity of unity, the results from the BL equation align closely with those of the Kadomtsev--Petviashvili (KP) equation. Corresponding numerical results are obtained and show excellent agreement with theoretical predictions. Furthermore, as a far-field approximation for the forced BL equation -- which models wave and flow interactions with local topography -- the modulation equations yield a slowly varying similarity solution. This solution indicates that the precursor wavefronts created by topography moving at subcritical or critical speeds take the shape of a circular arc, in contrast to the parabolic wavefronts observed in the forced KP equation.

nlin.PS↗

arXiv2024

Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Events

Retrieval and recommendation are two essential tasks in modern search tools. This paper introduces a novel retrieval-reranking framework leveraging Large Language Models (LLMs) to enhance the spatiotemporal and semantic associated mining and recommendation of relevant unusual climate and environmental events described in news articles and web posts. This framework uses advanced natural language processing techniques to address the limitations of traditional manual curation methods in terms of high labor cost and lack of scalability. Specifically, we explore an optimized solution to employ cutting-edge embedding models for semantically analyzing spatiotemporal events (news) and propose a Geo-Time Re-ranking (GT-R) strategy that integrates multi-faceted criteria including spatial proximity, temporal association, semantic similarity, and category-instructed similarity to rank and identify similar spatiotemporal events. We apply the proposed framework to a dataset of four thousand Local Environmental Observer (LEO) Network events, achieving top performance in recommending similar events among multiple cutting-edge dense retrieval models. The search and recommendation pipeline can be applied to a wide range of similar data search tasks dealing with geospatial and temporal data. We hope that by linking relevant events, we can better aid the general public to gain an enhanced understanding of climate change and its impact on different communities.

cs.IR↗