arXiv subjects

Hongsheng Zhang

Publications and source records attributed to Hongsheng Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Turning-Point Count Discrepancy as a Diagnostic of Relativistic Orbital Chaos

We propose the turning-point count-discrepancy indicator (TPCD) for diagnosing orbital chaos from a single trajectory in relativistic Hamiltonian systems with two oscillatory degrees of freedom. TPCD measures the largest cumulative departure of one turning-event count from its mean rate per reference cycle, requiring neither a neighboring orbit nor a phase-space partition and applying to both massive particles and photons. We establish its long-time behavior under explicit event--phase assumptions. Rigid phases with an exact event--phase correspondence obey a strict discrepancy bound of unity, and linearizable regular tori with bounded degree-one phase deformations obey a finite, orbit-dependent bound; both imply that the normalized indicator decays to zero as the record grows. A diffusive fluctuation mechanism instead yields a Brownian-bridge scaling and a finite statistical scale. Integrable Kerr motion validates the construction, recovering prescribed frequency ratios from event counts to within $2.6\times10^{-5}$ for six targets, including an irrational ratio. In charged-particle scans around a Kerr black hole in an external test magnetic field, TPCD and the fast Lyapunov indicator agree for all 80 sampled trajectories. In the Schwarzschild--Melvin photon model, a trajectory with elevated finite-time TPCD but low fast-Lyapunov values is identified as regular once its indicator trends downward over an extended integration, showing that finite-time values must be read together with their long-time trend.

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arXiv2026

Accretion of a Plasma Vlasov Gas onto a Reissner-Nordström Black Hole

We develop a steady-state, spherically symmetric accretion model for a two-component plasma Vlasov gas in the background of a Reissner--Nordström black hole. Analytical expressions in parametric form for the critical angular momentum and impact parameter are derived for each species, leading to a rigorous partition of the absorption and scattering domains in phase space. For both monoenergetic and Jüttner distributions, electromagnetic attraction enhances, while repulsion suppresses, the particle number density, energy density, pressure and accretion rates of individual charged components relative to the neutral case. After proper normalization, the total physical quantities of the plasma (under non-extreme conditions) can be expressed exactly, or to high accuracy, as weighted averages of the two species. When the two components share identical distribution functions, accretion drives the Reissner--Nordström black hole naturally toward the Schwarzschild limit---a charge evolution that is absent in neutral-gas models. Numerical results further reveal that near the horizon the radial pressure is significantly smaller than the tangential pressure, sharply distinguishing the Vlasov gas from a perfect fluid.

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arXiv2026

Scalar absorption beyond geometric optics in Klein-Gordon-separable Johannsen black hole spacetimes

Johannsen metric is a natural and significant generalization of the Kerr metric, representing the most general stationary, axisymmetric spacetime that preserves the Carter constant of motion. The theoretical status furnishes a powerful, systematic framework for strong-field tests of the no-hair theorem and for investigations of deviations from Kerr black-hole geometries. We formulate massless scalar plane-wave absorption in a Klein-Gordon-separable subclass of Johannsen spacetimes. In the asymptotically flat Johannsen metric, we impose Klein-Gordon separability, derive the separated angular and radial equations, and build a partial wave framework for the leading deformation sectors $A_1(r)$, $A_2(r)$, and $A_5(r)$. The resulting description separates deformations that change the radial size function $X(r)$ from those that enter only the radial kinetic term. The former modify the low-frequency area law, the high-frequency null-capture cross section, and the finite-frequency absorption spectra, whereas a pure $A_5$ deformation leaves the leading null-capture observable unchanged while remaining detectable in wave propagation. We further examine off-axis incidence, co-/counter-rotating contributions, and superradiant modes, where changes in $X(r_+)$ shift the horizon angular velocity and hence the superradiant threshold. Our results identify finite-frequency absorption as a wave-optics diagnostic that can probe radial propagation sectors inaccessible to both the area law and null geodesic capture observables, offering a new tool for strong-field tests of black hole geometry.

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arXiv2026

Time-reversed Shannon entropy as a chaos indicator for non-integrable systems

We propose a novel chaos indicator -- time-reversed Shannon entropy (TRSE) -- that leverages the interplay between time-reversal symmetry breaking and information entropy in curved spacetimes. By quantifying statistical discrepancies between forward and backward temporal evolution of particle orbits, TRSE robustly distinguishes chaotic from regular dynamics in non-integrable systems. In contrast, integrable systems exhibit stable, symmetric probability distributions preserved by conserved quantities such as the Carter constant. We validate the method through high-precision numerical simulations in both Kerr and Schwarzschild-Melvin black hole geometries, evolving trajectories forward and backward in time. Furthermore, we refine our previously introduced particle-pair mutual information (MIPP) and perform comprehensive parameter-space scans, revealing a strong quantitative agreement between MIPP and TRSE. The two indicators emerge as complementary probes of chaos: TRSE captures symmetry breaking in orbital evolution, while MIPP measures statistical correlations. Together, they establish a unified framework for diagnosing chaos in general relativistic systems, paving a new path to understand the fundamental nature of chaos in non-integrable systems.

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arXiv2026

Absorption and scattering of massless scalar waves by Frolov black holes

We study the absorption and scattering of massless scalar waves by Frolov black holes, a regular deformation of the Reissner--Nordström geometry. A null-geodesic analysis provides the photon-sphere radius and the critical impact parameters governing capture and glory scattering. Using a partial-wave approach, we compute total/partial absorption cross sections and differential scattering cross sections over a broad frequency range. When the absorption spectrum is rescaled by photon-sphere scales, the high-frequency oscillations exhibit a pronounced data collapse in the variables $\hatσ=σ_{\rm abs}/σ_{\rm geo}$ and $x=ω/Ω_c$, highlighting photon-sphere control of the absorption fine structure. We clarify the parameter dependence under the horizon-radius normalization and relate the apparent trends to the variation of the dimensionless mass across parameter space. Finally, comparing Frolov, Reissner--Nordström, and Hayward black holes with matched critical or glory impact parameters, we find that their absorption and scattering patterns can become remarkably close, indicating that in the intermediate-to-high frequency regime the dominant signatures are largely governed by the unstable photon orbit, while core effects enter as subleading corrections.

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arXiv2026

Observability of gravitational waves excited by binary stars orbiting around a supermassive black hole by space-based gravitational wave observatory

We produce the gravitational waveforms for the extreme mass ratio inspiral systems (EMRIs) of binary stars moving around central supermassive black hole (SBH), or called B-EMRIs. We calculate the external orbits of the binary stars via the commonly used Hamilton-Jacobi (HJ) approach, and calculate the internal orbits of the binary stars via Lagrangian approach. To improve accuracy we adopt the quadrupole-octupole expression of gravitational wave (GW) and study the contribution of radiation reaction. Compared to the waveforms of EMRIs, there are higher frequency oscillations superposed on the waveforms of B-EMRIs. We perform frequency spectrum analysis of the GW waveforms, and find that higher frequency signals give their prominency in the waveforms of B-EMRIs. To obtain high precise result for future observation of GWs from space-based detector, we take into account gravito-electromagnetic (GEM) force, and compare the waveforms of B-EMRIs with GEM effects against those of B-EMRIs without GEM effects and against those of EMRIs. The result of mismatch shows that the waveforms of B-EMRIs are credibly distinguishable by the space-based GW detectors when GEM force is considered.

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arXiv2026

Consistency-Regularized GAN for Few-Shot SAR Target Recognition

Few-shot recognition in synthetic aperture radar (SAR) imagery remains a critical bottleneck for real-world applications due to extreme data scarcity. A promising strategy involves synthesizing a large dataset with a generative adversarial network (GAN), pre-training a model via self-supervised learning (SSL), and then fine-tuning on the few labeled samples. However, this approach faces a fundamental paradox: conventional GANs themselves require abundant data for stable training, contradicting the premise of few-shot learning. To resolve this, we propose the consistency-regularized generative adversarial network (Cr-GAN), a novel framework designed to synthesize diverse, high-fidelity samples even when trained under these severe data limitations. Cr-GAN introduces a dual-branch discriminator that decouples adversarial training from representation learning. This architecture enables a channel-wise feature interpolation strategy to create novel latent features, complemented by a dual-domain cycle consistency mechanism that ensures semantic integrity. Our Cr-GAN framework is adaptable to various GAN architectures, and its synthesized data effectively boosts multiple SSL algorithms. Extensive experiments on the MSTAR and SRSDD datasets validate our approach, with Cr-GAN achieving a highly competitive accuracy of 71.21% and 51.64%, respectively, in the 8-shot setting, significantly outperforming leading baselines, while requiring only ~5 of the parameters of state-of-the-art diffusion models. Code is available at: https://github.com/yikuizhai/Cr-GAN.

cs.CV↗

arXiv2026

Democratizing planetary-scale analysis: An ultra-lightweight Earth embedding database for accurate and flexible global land monitoring

The rapid evolution of satellite-borne Earth Observation (EO) systems has revolutionized terrestrial monitoring, yielding petabyte-scale archives. However, the immense computational and storage requirements for global-scale analysis often preclude widespread use, hindering planetary-scale studies. To address these barriers, we present Embedded Seamless Data (ESD), an ultra-lightweight, 30-m global Earth embedding database spanning the 25-year period from 2000 to 2024. By transforming high-dimensional, multi-sensor observations from the Landsat series (5, 7, 8, and 9) and MODIS Terra into information-dense, quantized latent vectors, ESD distills essential geophysical and semantic features into a unified latent space. Utilizing the ESDNet architecture and Finite Scalar Quantization (FSQ), the dataset achieves a transformative ~340-fold reduction in data volume compared to raw archives. This compression allows the entire global land surface for a single year to be encapsulated within approximately 2.4 TB, enabling decadal-scale global analysis on standard local workstations. Rigorous validation demonstrates high reconstructive fidelity (MAE: 0.0130; RMSE: 0.0179; CC: 0.8543). By condensing the annual phenological cycle into 12 temporal steps, the embeddings provide inherent denoising and a semantically organized space that outperforms raw reflectance in land-cover classification, achieving 79.74% accuracy (vs. 76.92% for raw fusion). With robust few-shot learning capabilities and longitudinal consistency, ESD provides a versatile foundation for democratizing planetary-scale research and advancing next-generation geospatial artificial intelligence.

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arXiv2026

Wetland mapping from sparse annotations with satellite image time series and temporal-aware segment anything model

Accurate wetland mapping is essential for ecosystem monitoring, yet dense pixel-level annotation is prohibitively expensive and practical applications usually rely on sparse point labels, under which existing deep learning models perform poorly, while strong seasonal and inter-annual wetland dynamics further render single-date imagery inadequate and lead to significant mapping errors; although foundation models such as SAM show promising generalization from point prompts, they are inherently designed for static images and fail to model temporal information, resulting in fragmented masks in heterogeneous wetlands. To overcome these limitations, we propose WetSAM, a SAM-based framework that integrates satellite image time series for wetland mapping from sparse point supervision through a dual-branch design, where a temporally prompted branch extends SAM with hierarchical adapters and dynamic temporal aggregation to disentangle wetland characteristics from phenological variability, and a spatial branch employs a temporally constrained region-growing strategy to generate reliable dense pseudo-labels, while a bidirectional consistency regularization jointly optimizes both branches. Extensive experiments across eight global regions of approximately 5,000 km2 each demonstrate that WetSAM substantially outperforms state-of-the-art methods, achieving an average F1-score of 85.58%, and delivering accurate and structurally consistent wetland segmentation with minimal labeling effort, highlighting its strong generalization capability and potential for scalable, low-cost, high-resolution wetland mapping.

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arXiv2025

OCCDiff: Occupancy Diffusion Model for High-Fidelity 3D Building Reconstruction from Noisy Point Clouds

A major challenge in reconstructing buildings from LiDAR point clouds lies in accurately capturing building surfaces under varying point densities and noise interference. To flexibly gather high-quality 3D profiles of the building in diverse resolution, we propose OCCDiff applying latent diffusion in the occupancy function space. Our OCCDiff combines a latent diffusion process with a function autoencoder architecture to generate continuous occupancy functions evaluable at arbitrary locations. Moreover, a point encoder is proposed to provide condition features to diffusion learning, constraint the final occupancy prediction for occupancy decoder, and insert multi-modal features for latent generation to latent encoder. To further enhance the model performance, a multi-task training strategy is employed, ensuring that the point encoder learns diverse and robust feature representations. Empirical results show that our method generates physically consistent samples with high fidelity to the target distribution and exhibits robustness to noisy data.

cs.CV↗

arXiv2025

Dynamics of photons and shadows for black holes haired with parity-odd fields

Strong self-gravitational fields enable the realization of macroscopic odd-parity quantum objects. Using ray-tracing methods, we systematically analyze the dynamics of photons and the shadow features of rotating black holes with parity-odd scalar hair and contrast them with those of Kerr black holes. Our results demonstrate measurable distinctions between scalar-haired black hole shadows and their Kerr counterparts. Notably, even for tiny scalar charge and negligible scalar hair mass contributions, these differences remain quantitatively resolvable. In particular, one of the hairy black hole reported here lies within the Event Horizon Telescope observational uncertainties, probing the scalar masses of $1.02\times10^{-20}$eV with M87*. These findings may provide related theoretical benchmarks for future observational campaigns targeting scalar-field dark matter candidates through black hole shadow imaging.

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arXiv2025

Multimodal Feature Fusion Network with Text Difference Enhancement for Remote Sensing Change Detection

Although deep learning has advanced remote sensing change detection (RSCD), most methods rely solely on image modality, limiting feature representation, change pattern modeling, and generalization especially under illumination and noise disturbances. To address this, we propose MMChange, a multimodal RSCD method that combines image and text modalities to enhance accuracy and robustness. An Image Feature Refinement (IFR) module is introduced to highlight key regions and suppress environmental noise. To overcome the semantic limitations of image features, we employ a vision language model (VLM) to generate semantic descriptions of bitemporal images. A Textual Difference Enhancement (TDE) module then captures fine grained semantic shifts, guiding the model toward meaningful changes. To bridge the heterogeneity between modalities, we design an Image Text Feature Fusion (ITFF) module that enables deep cross modal integration. Extensive experiments on LEVIRCD, WHUCD, and SYSUCD demonstrate that MMChange consistently surpasses state of the art methods across multiple metrics, validating its effectiveness for multimodal RSCD. Code is available at: https://github.com/yikuizhai/MMChange.

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arXiv2025

Spontaneous genesis of naked singularities through quantum-gravitational processes: conclusive evidence for violation of cosmic censorship

Cosmic censorship conjecture takes a pivotal status in general relativity. We demonstrate that quantum effects, Hawking effect together with Shwinger effect inevitably lead to violation of cosmic censorship. We find that naked singularity spontaneously appears in late time evolution of an isolated large dilatonic black hole. The critical discovery is that the power of Hawking radiation converges to a finite value for an extreme dilatonic black hole, which directly exposes the singularity in finite time. The spectrum of Hawking radiation of extreme dilatonic black holes becomes a truncated shrink Planck distribution. We analyze the underlying physics of the spectrum of Hawking radiation, which roots in extraordinarily wide potential around the horizon. We study the discharge mechanism of a dilatonic black hole through Schwinger effect. Amazingly, the Schwinger pair production naturally ceases for an extreme dilatonic black hole with mass larger than $1.7\times 10^5$ solar masses. Furthermore, we show that evaporation of charged particle because of the Schwinger effect do not save the cosmic censorship for black holes heavier than $1.784\times 10^7 $ solar masses in a significant region of initial charge parameter space. For the first time, we demonstrate that the naked singularities are {\rm spontaneously} formed driven by quantum effects, such that we learn quantum gravity directly from the information shedded from the singularity.

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arXiv2025

Phase Transition and Critical Phenomena of Charged Einstein-Maxwell-Scalar Black Holes

We study the phase transition and critical phenomenon of charged black holes in Einstein-Maxwell-scalar (EMs) theory. Through comprehensive analysis of thermodynamic behaviors manifested in P-V diagrams, G(T,P) surfaces, and C_P curves, we establish that these black holes exhibit van der Waals-type phase transition behavior. The derived critical exponents governing the phase transition show precise correspondence with both van der Waals gas-liquid systems, reinforcing the connection between black hole thermodynamics and mean field theory statistics. The findings reveal a crucial dependence of phase transition properties on the scalar charge parameter. A critical threshold emerges where phase transitions become prohibited when scalar charge exceeds a specific magnitude. However, the transition persists asymptotically as scalar charge approaches zero. The analysis further demonstrates nonlinear relationships between scalar charge and critical parameters: while small scalar charges induce increasing critical volume with charge magnitude, larger values produce an inverse trend. Critical temperature displays complementary behavior, maintaining monotonic variation under certain conditions while exhibiting inverse correlation with critical volume in others. Significantly, the transition points governing critical volume and temperature trends occur at distinct scalar charge values for different black holes, indicating a non-trivial parameter dependence. These results highlight the scalar charge's dual role as both an enabler and suppressor of phase transitions in EMs black holes, providing new insights into the interplay between geometric configurations and thermodynamic properties in modified gravity theories.

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arXiv2025

Screen chaotic motion by Shannon entropy in curved spacetimes

We find a novel characteristic for chaotic motion by introducing Shannon entropy for periodic orbits, quasiperiodic orbits, and chaotic orbits.We compare our approach with the previous methods including Poincaré Section, Lyapunov exponent, Fast Lyapunov Indicator, Recurrence plots(Rps), and Fast Fourier Transform(FFT) for orbits around black hole immersed in magnetic fields, and show that they agree with each other quite well. The approach of Shannon entropy is intuitively clear, and theoretically reasonable since it becomes larger and larger form a periodic orbit to chaotic orbit. We demonstrate that Shannon entropy can be a powerful probe to distinguish between chaotic and regular orbits in different spacetimes, and reversely may lead to a new road to define the entropy for a single orbit in phase space, and to find more fundamental relations between thermodynamics and dynamics.

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arXiv2025

Mutual Information for particle pair and its application to diagnose Chaos in Curved Spacetime

We propose the concept of mutual information for particle pair (MIPP) in curved spacetime, and show that MIPP has potential to be a proper chaos indicator. We tested this method in the Schwarzschild and Kerr spacetime and compared it with the fast Lyapunov indicator. The results show that the MIPP effectively identify orbital states and demonstrates prominent performance in recognizing transitions between orbital states. Our result show that information theory significantly deepen our understanding of dynamics of few-body system.

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arXiv2025

Quantum anomaly triggers the violation of scaling laws in gravitational system

Scaling laws for critical phenomena take pivotal status in almost all branches of physics. However, as scaling laws are commonly guaranteed by the renormalization group theory, systems that violate them have rarely been found. In this letter, we demonstrate that gravitational system can break scaling laws. We derive this result through investigating phase transition and critical phenomenon in a gravitational system with quantum anomaly. For the first time, we outline the key conditions to violate the scaling laws in generic gravitational system viewed from the equation of state $P=P(T,V)$. Our results indicate that quantum effects can magnify the distinctiveness of gravity, which may be significant to understand the microscopic structure of spacetime.

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arXiv2025

A comprehensive review of remote sensing in wetland classification and mapping

Wetlands constitute critical ecosystems that support both biodiversity and human well-being; however, they have experienced a significant decline since the 20th century. Back in the 1970s, researchers began to employ remote sensing technologies for wetland classification and mapping to elucidate the extent and variations of wetlands. Although some review articles summarized the development of this field, there is a lack of a thorough and in-depth understanding of wetland classification and mapping: (1) the scientific importance of wetlands, (2) major data, methods used in wetland classification and mapping, (3) driving factors of wetland changes, (4) current research paradigm and limitations, (5) challenges and opportunities in wetland classification and mapping under the context of technological innovation and global environmental change. In this review, we aim to provide a comprehensive perspective and new insights into wetland classification and mapping for readers to answer these questions. First, we conduct a meta-analysis of over 1,200 papers, encompassing wetland types, methods, sensor types, and study sites, examining prevailing trends in wetland classification and mapping. Next, we review and synthesize the wetland features and existing data and methods in wetland classification and mapping. We also summarize typical wetland mapping products and explore the intrinsic driving factors of wetland changes across multiple spatial and temporal scales. Finally, we discuss current limitations and propose future directions in response to global environmental change and technological innovation. This review consolidates our understanding of wetland remote sensing and offers scientific recommendations that foster transformative progress in wetland science.

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