arXiv subjects

Chen Du

Publications and source records attributed to Chen Du.

18 recordsLinked to original sources

arXiv2026

A Missing Tool for Calculating Auto/Cross-correlation Function under Nonuniform Sampling Observations

Nonuniform sampling presents a long-standing challenge in astrophysical time-domain analysis, invalidating the standard autocorrelation and cross-correlation functions and forcing researchers to adopt ad-hoc methods like interpolation or binning, which introduce unquantified biases and lack rigorous error estimation. Here we introduce a new method for calculating the nonuniform autocorrelation function (NUACF) and nonuniform cross-correlation function (NUCCF) for irregularly sampled time series. Instead of relying on interpolation, it naturally evaluates the correlation function by incorporating time-interval weights and misalignment penalties. Monte Carlo simulations provide confidence bands for significance assessment and a complete error budget for the time delays that accounts for both flux uncertainties and sampling irregularity (essential but generally absent from existing methods). Through extensive simulations, we demonstrate our method outperforms traditional methods across various conditions, from strictly periodic to complex repeating variability patterns (e.g., intermittent but aperiodic). Its effectiveness is demonstrated via various real astrophysical data sets, revealing repetitive variability in stellar light curves, measuring time delays for multi-band disc reverberation in the AGN Fairall 9, and providing model-independent validation of time delays for the gravitationally lensed quasar HE 0435-1223. The method provides a rigorous and general solution to the ubiquitous problem of nonuniform sampling, positioning it as a useful tool for large-scale time-domain survey data analysis. The framework is also directly applicable to emerging time-domain phenomena such as fast radio bursts (FRBs), enabling, e.g., the study of correlations between persistent radio source luminosity and repeating FRB activity, or among the multi-parameter variability curves of FRB emission itself.

astro-ph.IM↗

arXiv2026

Constraints on the Low-frequency Radio Emission of the Galactic FRB Source SGR 1935+2154

We present a search for radio pulses from the Galactic magnetar SGR 1935+2154, a well-known source of fast radio bursts (FRBs), at $\sim$110 MHz using the Large Phased Array (LPA) of the Pushchino Radio Astronomy Observatory. Data from two active periods in 2020 (March -- May and September -- November, with $\sim 3.5$ minutes of daily coverage) were analyzed with new methods tailored to both FRB-like single pulses and pulsar-like periodic signals. No significant FRB-like pulses were found. Using Monte Carlo simulations, $3\sigma$ upper limits were derived for the burst rate: for a log-normal energy distribution the limit is $\sim$${10}^{1.5}~{\rm{d}}^{-1}$ for a mean of average monochromatic isotropic luminosity $L_{\nu{\rm ,mean}}\sim1.3\times{10}^{29}~{\rm{erg~s^{-1}~ {Hz}^{-1}}}$ and a natural log-space scatter of $\sigma\sim0.85$; while for a power-law distribution it is $\sim$${10}^{1.8}~{\rm{d}}^{-1}$ for an index $\beta\lesssim3.0$ and a minimum average monochromatic isotropic luminosity $L_{\nu{\rm{,min}}}\lesssim0.7\times{10}^{25}~{\rm{erg~s^{-1}~{Hz}^{-1}}}$. When folded at the known 3.24781628 s period of SGR 1935+2154, a weak pulse was noted (S/N $<$ 3.16), but the significance is insufficient for a secure detection of the pulsar-like emission signal. A conservative upper limit on the average monochromatic isotropic luminosity of any possible periodic emission is $2.08\times{10}^{19}~{\rm{erg~s^{-1}~{Hz}^{-1}}}$. Our results offer meaningful low-frequency upper limits on the burst rate of SGR 1935+2154, and hint for very faint pulsar-like radiation at meter wavelengths.

astro-ph.HE↗

arXiv2026

Diverse Morphologies of GRB X-Ray Plateaus within a Common Magnetar Framework

The origin of the X-ray plateau phase in gamma-ray bursts (GRBs) remains an open problem. In particular, it is unclear whether GRBs with different temporal morphologies (i.e., with a rising, flat, or decaying plateau) arise from a common underlying mechanism. Although magnetar energy injection is a leading explanation, previous studies have primarily inferred magnetar properties on a burst-by-burst basis and have not tested the model at the population level. Here we perform the first hierarchical population inference of magnetar parameters for a uniform sample of 185 long GRBs with X-ray plateaus within a conditional Poisson point-process framework. It is found that the observed plateau population is well reproduced by physically plausible magnetar populations. The inferred parameter distributions show no strong statistical separation among subclasses with different plateau morphologies. Nevertheless, all subclasses show a substantial intrinsic luminosity scatter, $\sigma_{L,\rm int}\sim0.5$--1.0 dex, whereas the intrinsic duration scatter remains considerably smaller. The results provide a population-level test of the magnetar interpretation of GRB X-ray plateaus, showing that the observed diversity of plateau morphologies does not require distinct magnetar populations.

astro-ph.HE↗

arXiv2025

Magnetic Reconnection as a Potential Driver of X-ray Variability in Active Galactic Nuclei

We present a systematic analysis on the X-ray variability in 13 bright quasars at z > 4.5, combining recent Swift observations from 2021 to 2023 and archival multi-epoch observations. Upper limits of the luminosity measurements were included in the analysis by using the Kaplan-Meier estimator method. It is found that the high-z quasars exhibit X-ray variability on both short-term (hours-to-days) and intermediate-term (weeks-to-months) timescales, with short-term variability dominating the overall variation. A linear correlation exists between the global mean ($\mu_{\mathrm{L_{2-10\,keV}}}$) and standard deviation ($\sigma_{\mathrm{L_{2-10\,keV}}}$) of X-ray luminosities, which is independent of the X-ray photon index and optical-to-X-ray spectral slope. The localized stochastic magnetic reconnection mechanism is strongly favored, which can naturally lead to a scale-invariant power-law energy distribution and satisfactorily explain the correlation. The $\sigma$-$\mu$ correlation parallels with the well-documented rms-flux relation of low-z active galactic nuclei (AGNs), implying the magnetic reconnection mechanism could drive short-timescale X-ray variability in both high- and low-z AGNs. The highest-z quasar in our sample, J142952+544717 (z = 6.18), shows a luminosity distribution extending to ${10}^{47}\ \rm{erg\ {s}^{-1}}$ with a not conspicuous median luminosity. On the other hand, J143023+420436 (z = 4.7), which hosts the most relativistic jet among known high-z blazars, is dominated in the high-luminosity regime (${10}^{47}\ \rm{erg\ {s}^{-1}}$ ), making it an ideal target for multi-wavelength follow-up observations. J090630+693030 is found to have a rest-frame period of 182.46 days and J143023+420436 has a period of 16.89 days, both could be explained by the global evolution of plasmoid chains, in which magnetic islands formed during reconnection may merge successively.

astro-ph.HE↗

arXiv2025

A comprehensive search for Long and Short Periodic Features from an Extremely Active Cycle of FRB 20240114A

Possible periodic features in fast radio bursts (FRBs) may provide insights into their astrophysical origins. Using extensive observations from the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we conduct a multi-timescale periodicity search for the exceptionally active repeater FRB~20240114A. Our analysis is based on different datasets for different timescales: for short-timescale periodicity in Time of Arrivals (TOAs), we use 57 observations from January to August 2024; for long-timescale periodicity, we employ an extended TOA dataset comprising 111 observations spanning from January 2024 to October 2025; and for burst time series analysis, we utilize individual burst data from the 57 FAST observations. We identify three candidate short-timescale periodic signals (0.673~s, 0.635~s, and 0.536~s) with significances of $3.2\sigma$--$6\sigma$, each detected in two independent observations. On longer timescales, we detect a significant $143.40\pm7.19$-day periodicity with $5.2\sigma$ significance, establishing FRB~20240114A as a periodic repeater. In burst time series, we find quasi-periodic oscillations in the few hundred Hz range ($3.4\sigma$ and $3.7\sigma$) and periodic burst trains with periods of several to tens of milliseconds ($3\sigma$--$3.9\sigma$), though these periodic features appear transient and short-lived. The detection of periodic signals at these different time scales indicates that FRB 20240114A exhibits intriguing periodic self-similar characteristics. Despite the comprehensive dataset, no definitive periodicity linked to the source's rotation is confirmed, placing stringent constraints on the intrinsic source properties and the modulation mechanisms. All data are available via the Science Data Bank.

astro-ph.HE↗

arXiv2025

Two Periodic Activity Epochs in FRB 20201124A: Coincident with Critical RM Evolution Epochs and Its Implications

Recent observations of the repeating fast radio burst FRB 20201124A by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) revealed a second-scale periodic modulation ($\sim$1.7\,s) in burst activity during two distinct observational windows. We find that these two periodic activity epochs temporally coincide with the transitional states of the source's Faraday rotation measure (RM), and the chance coincidence is only about 0.07$\%$. This correlation is can be understood within the magnetar/Be-star binary system framework. Considering that only the polar cap region can remain stable for such an extended period, we apply a coherent linear periodic evolution model to jointly constrain the initial burst period $ P_0 $ and the period derivative $ \dot{P} $ across both observation windows (MJD 59310 and MJD 59347). We obtain spin parameters consistent with blind search results: an initial spin period $P_0 = 1.7060155$\,s at the reference time and spin period derivative $\dot{P} = 6.1393 \times 10^{-10}$\,s\,s$^{-1}$. We conclude that during these two observational windows, the magnetar was just crossing the disk of the Be star. The disk-magnetar interaction at these two geometric positions may surpress the multi-polar magnetic fields at low latitudes of the magnetar, which enhances the dominance of the polar cap region emissions and makes the periodic activity detectable.

astro-ph.HE↗

arXiv2025

A second-scale periodicity in an active repeating fast radio burst source

Fast radio bursts (FRBs) are fierce radio flashes from the deep sky. Abundant observations have indicated that highly magnetized neutron stars might be involved in these energetic bursts, but the underlying trigger mechanism is still enigmatic. Especially, the widely expected periodicity connected to the spin of the central engine has never been discovered, which leads to further debates on the nature of FRBs. Here we report the first discovery of a $\sim$ 1.7 s period in the repeating source of FRB 20201124A. This is an active repeater, from which more than 2800 bursts have been detected over a total of 49 days. The phase-folding method is adopted to analyze the bursts on each day separately. While no significant periodic signal is found in most days, a clear periodicity does appear on two specific days: a period of 1.706024(13) s on MJD 59310, and a slightly larger period of 1.707968(9) s on MJD 59347. A global Monte Carlo analysis based on all single-day datasets yields a significance level of $5.5 \sigma$ for the periodicity. A period derivative of $6.11(5)\times10^{-10}$ s s$^{-1}$ can be derived from these two periods, which further implies a surface magnetic field strength of $1.03\times10^{15}$ G and a spin-down age of $44$ years for the central engine. It is concluded that FRB 20201124A should be associated with a young magnetar.

astro-ph.HE↗

arXiv2025

GlossGau: Efficient Inverse Rendering for Glossy Surface with Anisotropic Spherical Gaussian

The reconstruction of 3D objects from calibrated photographs represents a fundamental yet intricate challenge in the domains of computer graphics and vision. Although neural reconstruction approaches based on Neural Radiance Fields (NeRF) have shown remarkable capabilities, their processing costs remain substantial. Recently, the advent of 3D Gaussian Splatting (3D-GS) largely improves the training efficiency and facilitates to generate realistic rendering in real-time. However, due to the limited ability of Spherical Harmonics (SH) to represent high-frequency information, 3D-GS falls short in reconstructing glossy objects. Researchers have turned to enhance the specular expressiveness of 3D-GS through inverse rendering. Yet these methods often struggle to maintain the training and rendering efficiency, undermining the benefits of Gaussian Splatting techniques. In this paper, we introduce GlossGau, an efficient inverse rendering framework that reconstructs scenes with glossy surfaces while maintaining training and rendering speeds comparable to vanilla 3D-GS. Specifically, we explicitly model the surface normals, Bidirectional Reflectance Distribution Function (BRDF) parameters, as well as incident lights and use Anisotropic Spherical Gaussian (ASG) to approximate the per-Gaussian Normal Distribution Function under the microfacet model. We utilize 2D Gaussian Splatting (2D-GS) as foundational primitives and apply regularization to significantly alleviate the normal estimation challenge encountered in related works. Experiments demonstrate that GlossGau achieves competitive or superior reconstruction on datasets with glossy surfaces. Compared with previous GS-based works that address the specular surface, our optimization time is considerably less.

cs.CV↗

arXiv2025

Generalized quantum two level model and its application in astrophysics

Complicated time-dependent curved spacetime and electric field are involved in many astrophysical situations, including the early universe, Hawking radiation, the Schwinger effect, and gravitational pair production. In this Letter, a generalized quantum two-level model (GQTLM) is developed, which is applicable to arbitrary time-dependent curved spacetime and electric field. The model is found to be consistent with quantum kinetic theory, and is characterized by its simplicity and versatility. The momentum distribution of particles and the effects of gravitational distortions can be correctly described. Quantum properties concerning vortex structures, such as the intrinsic orbital angular momentum of particles and antiparticles can also be conveniently calculated. The model is expected to significantly advance the quantum exploration of the universe. It could refine the prediction of primordial gravitational waves and relevant non-Gaussian signals, extend the calculation of Hawking radiation to general black hole configurations, help to distinguish neutron stars from strange quark stars, and elucidate the gravitational pair production mechanism.

gr-qc↗

arXiv2025

Axial current as the origin of quantum intrinsic orbital angular momentum

We show that the axial current density is the physical origin (generator) of quantum intrinsic orbital angular momentum (IOAM). Without the axial current, the IOAM of particles vanishes. Broadly speaking, we argue that the spiral or interference characteristics of the axial current density determine the occurrence of nonlinear or tunneling effects in any spacetime-dependent quantum systems. Our findings offer a comprehensive theoretical framework that addresses the limitations of Keldysh's ionization theory and provides new insights into the angular momentum properties of quantum systems, particularly in tunneling-dominated regimes. Using Wigner function methods, fermionic generalized two-level model, and Berry phase simulations, we predict that IOAM effect can persist even in pure quantum tunneling processes. These results open the door for experimental verification of IOAM effects in future high-intensity QED experiments, such as those using X-ray free electron lasers.

hep-ph↗

arXiv2024

MUSE-VL: Modeling Unified VLM through Semantic Discrete Encoding

We introduce MUSE-VL, a Unified Vision-Language Model through Semantic discrete Encoding for multimodal understanding and generation. Recently, the research community has begun exploring unified models for visual generation and understanding. However, existing vision tokenizers (e.g., VQGAN) only consider low-level information, which makes it difficult to align with language tokens. This results in high training complexity and necessitates a large amount of training data to achieve optimal performance. Additionally, their performance is still far from dedicated understanding models. This paper proposes Semantic Discrete Encoding (SDE), which effectively aligns the information of visual tokens and language tokens by adding semantic constraints to the visual tokenizer. This greatly reduces the amount of training data and improves the performance of the unified model. With the same LLM size, our method improved the understanding performance by 4.8% compared to the previous SOTA Emu3 and surpassed the dedicated understanding model LLaVA-NeXT 34B by 3.7%. Our model also surpasses the existing unified models on visual generation benchmarks.

cs.CV↗

arXiv2024

ZALM3: Zero-Shot Enhancement of Vision-Language Alignment via In-Context Information in Multi-Turn Multimodal Medical Dialogue

The rocketing prosperity of large language models (LLMs) in recent years has boosted the prevalence of vision-language models (VLMs) in the medical sector. In our online medical consultation scenario, a doctor responds to the texts and images provided by a patient in multiple rounds to diagnose her/his health condition, forming a multi-turn multimodal medical dialogue format. Unlike high-quality images captured by professional equipment in traditional medical visual question answering (Med-VQA), the images in our case are taken by patients' mobile phones. These images have poor quality control, with issues such as excessive background elements and the lesion area being significantly off-center, leading to degradation of vision-language alignment in the model training phase. In this paper, we propose ZALM3, a Zero-shot strategy to improve vision-language ALignment in Multi-turn Multimodal Medical dialogue. Since we observe that the preceding text conversations before an image can infer the regions of interest (RoIs) in the image, ZALM3 employs an LLM to summarize the keywords from the preceding context and a visual grounding model to extract the RoIs. The updated images eliminate unnecessary background noise and provide more effective vision-language alignment. To better evaluate our proposed method, we design a new subjective assessment metric for multi-turn unimodal/multimodal medical dialogue to provide a fine-grained performance comparison. Our experiments across three different clinical departments remarkably demonstrate the efficacy of ZALM3 with statistical significance.

cs.CL↗

arXiv2024

On the dynamical evolution of the asteroid belt in a massive star-neutron star binary

Some fast radio bursts (FRBs) exhibit repetitive behaviors and their origins remain enigmatic. It has been argued that repeating FRBs could be produced by the interaction between a neutron star and an asteroid belt. Here we consider the systems in which an asteroid belt dwells around a massive star, while a neutron star, as a companion of the massive star, interacts with the belt through gravitational force. Various orbital configurations are assumed for the system. Direct N-body simulations are performed to investigate the dynamical evolution of the asteroids belt. It is found that a larger orbital eccentricity of the neutron star will destroy the belt more quickly, with a large number of asteroids being scattered out of the system. A low inclination not only suppresses the collisions but also inhibits the ejection rate at early stages. However, highly inclined systems may undergo strong oscillations, resulting in the Kozai--Lidov instabilities. Among the various configurations, a clear periodicity is observed in the collision events for the case with an orbital eccentricity of 0.7 and mutual inclination of $0^{\circ}$. It is found that such a periodicity can be sustained for at least 8 neutron star orbital periods, supporting this mechanism as a possible explanation for periodically repeating FRBs. Our studies also suggest that the active stage of these kinds of FRB sources should be limited, since the asteroid belt would finally be destroyed by the neutron star after multiple passages.

astro-ph.HE↗

arXiv2023

A Thorough Search for Short Timescale Periodicity in Four Active Repeating Fast Radio Bursts

Fast Radio Bursts (FRBs) are bright radio transients with millisecond durations which typically occur at extragalactic distances. The association of FRB 20200428 with the Galactic magnetar SGR J1935+2154 strongly indicates that they could originate from neutron stars, which naturally leads to the expectation that periodicity connected with the spinning of magnetars should exist in the activities of repeating FRBs. However, previous studies have failed to find any signatures supporting such a conjecture. Here we perform a thorough search for short timescale periodicity in the four most active repeating sources, i.e. FRBs 20121102A, 20200120E, 20201124A, and 20220912A. Three different methods are employed, including the phase folding algorithm, the H-test and the Lomb-Scargle periodogram. For the three most active repeaters from which more than 1000 bursts have been detected, i.e. FRBs 20121102A, 20201124A, and 20220912A, more in-depth period searches are conducted by considering various burst properties such as the pulse width, peak flux, fluence, and the brightness temperature. No clear periodicity is found in a period range of 0.001--1000 s in all the efforts. Implications of such a null result on the theoretical models of FRBs are discussed.

astro-ph.HE↗

arXiv2022

Agriculture-Vision Challenge 2022 -- The Runner-Up Solution for Agricultural Pattern Recognition via Transformer-based Models

The Agriculture-Vision Challenge in CVPR is one of the most famous and competitive challenges for global researchers to break the boundary between computer vision and agriculture sectors, aiming at agricultural pattern recognition from aerial images. In this paper, we propose our solution to the third Agriculture-Vision Challenge in CVPR 2022. We leverage a data pre-processing scheme and several Transformer-based models as well as data augmentation techniques to achieve a mIoU of 0.582, accomplishing the 2nd place in this challenge.

cs.CV↗

arXiv2021

A CNN Segmentation-Based Approach to Object Detection and Tracking in Ultrasound Scans with Application to the Vagus Nerve Detection

Ultrasound scanning is essential in several medical diagnostic and therapeutic applications. It is used to visualize and analyze anatomical features and structures that influence treatment plans. However, it is both labor intensive, and its effectiveness is operator dependent. Real-time accurate and robust automatic detection and tracking of anatomical structures while scanning would significantly impact diagnostic and therapeutic procedures to be consistent and efficient. In this paper, we propose a deep learning framework to automatically detect and track a specific anatomical target structure in ultrasound scans. Our framework is designed to be accurate and robust across subjects and imaging devices, to operate in real-time, and to not require a large training set. It maintains a localization precision and recall higher than 90% when trained on training sets that are as small as 20% in size of the original training set. The framework backbone is a weakly trained segmentation neural network based on U-Net. We tested the framework on two different ultrasound datasets with the aim to detect and track the Vagus nerve, where it outperformed current state-of-the-art real-time object detection networks.

cs.CV↗

arXiv2018

Selective Feature Connection Mechanism: Concatenating Multi-layer CNN Features with a Feature Selector

Different layers of deep convolutional neural networks(CNNs) can encode different-level information. High-layer features always contain more semantic information, and low-layer features contain more detail information. However, low-layer features suffer from the background clutter and semantic ambiguity. During visual recognition, the feature combination of the low-layer and high-level features plays an important role in context modulation. If directly combining the high-layer and low-layer features, the background clutter and semantic ambiguity may be caused due to the introduction of detailed information. In this paper, we propose a general network architecture to concatenate CNN features of different layers in a simple and effective way, called Selective Feature Connection Mechanism (SFCM). Low-level features are selectively linked to high-level features with a feature selector which is generated by high-level features. The proposed connection mechanism can effectively overcome the above-mentioned drawbacks. We demonstrate the effectiveness, superiority, and universal applicability of this method on multiple challenging computer vision tasks, including image classification, scene text detection, and image-to-image translation.

cs.CV↗

arXiv2018

Accurate and efficient video de-fencing using convolutional neural networks and temporal information

De-fencing is to eliminate the captured fence on an image or a video, providing a clear view of the scene. It has been applied for many purposes including assisting photographers and improving the performance of computer vision algorithms such as object detection and recognition. However, the state-of-the-art de-fencing methods have limited performance caused by the difficulty of fence segmentation and also suffer from the motion of the camera or objects. To overcome these problems, we propose a novel method consisting of segmentation using convolutional neural networks and a fast/robust recovery algorithm. The segmentation algorithm using convolutional neural network achieves significant improvement in the accuracy of fence segmentation. The recovery algorithm using optical flow produces plausible de-fenced images and videos. The proposed method is experimented on both our diverse and complex dataset and publicly available datasets. The experimental results demonstrate that the proposed method achieves the state-of-the-art performance for both segmentation and content recovery.

cs.CV↗