arXiv subjects

Hengyu Wu

Publications and source records attributed to Hengyu Wu.

8 recordsLinked to original sources

arXiv2026

Identification of Lensed Gravitational-Wave Beat Patterns by LISA

Strong lensing of massive black hole binaries can produce multiple gravitational-wave images with different magnifications and arrival times. LISA signals remain in band for months to years, allowing multiple lensed images to overlap during the inspiral stage and generate beat patterns. A singular isothermal sphere lens model is adopted to describe the lensing configuration, and two-image beat waveforms are constructed from massive black hole binary signals. To isolate the beat pattern itself, waveform mismatch is evaluated only during the overlapping inspiral stage before the coalescence of the first image, excluding contributions from the delayed merger peak of the second image. Using the HS-nod-SN (B+20) strong-lensing population, the occurrence rate of identifiable beat events is estimated, and Bayesian parameter estimation is performed with a beat template. Beat patterns are most readily identified when the lensing time delay is short and the delayed image has a relatively large magnification. Among 196 detectable two-image lensed events, 92 satisfy the temporal-overlap condition and 14 satisfy the beat-identification criterion, corresponding to an identifiable beat fraction of about 7\%. Posterior inference shows that the beat template can recover the lensing time delay and magnification parameters for a representative beat event. These results indicate that lensed beat patterns constitute a distinguishable subset of strongly lensed LISA events and provide a unique observational signature of strong lensing in the LISA band.

astro-ph.CO↗

arXiv2026

Mock Catalogs of Strongly Lensed Gravitational Waves via a Halo Model Approach with Space-borne Detectors

Future space-borne gravitational-wave (GW) detectors, such as LISA and DECIGO, are expected to detect a large number of GW events, a fraction of which may be strongly lensed by intervening galaxies or galaxy clusters. In this work, we develop a comprehensive framework to simulate strongly lensed GWs in the context of space-borne detectors. Based on realistic astrophysical models for both the source population and the lens distribution, we construct mock catalogs of lensed GW events, referred to as \textbf{GW-LMC-Space}. Our results show that, for a four-year LISA observation, the expected number of lensed events ranges from $0$ to $131$, depending on the adopted formation model of massive black hole binaries (MBHBs). The corresponding lensing probability for MBHBs can reach up to $\sim 0.3\%$. For DECIGO, we find that the number of lensed events in a one-year observation is expected to lie in the range of $0$--$44$, with a lensing probability of $\sim 0.15\%$ for stellar-mass binary black holes (BBHs), binary neutron stars (BNSs), and neutron star--black hole binaries (NSBHs). We further show that the overlap of lensed signals is a common feature in space-borne detectors, which can significantly affect both the signal-to-noise ratio (SNR) estimation and event identification. These results highlight the importance of accounting for signal overlap in the analysis of strongly lensed GW events in future space-borne GW observations.

astro-ph.CO↗

arXiv2026

Certified Robustness under Heterogeneous Perturbations via Hybrid Randomized Smoothing

Randomized smoothing provides strong, model-agnostic robustness certificates, but existing guarantees are limited to single modalities, treating continuous and discrete inputs in isolation. This limitation becomes critical in multimodal models, where decisions depend on cross-modal semantics and adversaries can jointly perturb heterogeneous inputs, rendering unimodal certificates insufficient. We introduce a unified randomized smoothing framework for mixed discrete--continuous inputs based on an analytically tractable Neyman--Pearson formulation of the joint worst-case problem. By analyzing the joint likelihood ordering induced by factorized discrete and continuous noise, our approach yields a closed-form, one-dimensional certificate that strictly generalizes both Gaussian (image-only) and discrete (text-only) randomized smoothing. We validate the framework on multimodal safety filtering, providing, to our knowledge, the first model-agnostic Neyman--Pearson certificate for joint discrete-token and continuous-image perturbations in interaction-dependent text--image safety filtering.

cs.LG↗

arXiv2026

SLIM: Stealthy Low-Coverage Black-Box Watermarking via Latent-Space Confusion Zones

Training data is a critical and often proprietary asset in Large Language Model (LLM) development, motivating the use of data watermarking to embed model-transferable signals for usage verification. We identify low coverage as a vital yet largely overlooked requirement for practicality, as individual data owners typically contribute only a minute fraction of massive training corpora. Prior methods fail to maintain stealthiness, verification feasibility, or robustness when only one or a few sequences can be modified. To address these limitations, we introduce SLIM, a framework enabling per-user data provenance verification under strict black-box access. SLIM leverages intrinsic LLM properties to induce a Latent-Space Confusion Zone by training the model to map semantically similar prefixes to divergent continuations. This manifests as localized generation instability, which can be reliably detected via hypothesis testing. Experiments demonstrate that SLIM achieves ultra-low coverage capability, strong black-box verification performance, and great scalability while preserving both stealthiness and model utility, offering a robust solution for protecting training data in modern LLM pipelines.

cs.CR↗

arXiv2026

Testing General Relativity on Galactic Scales via DESI-BAO and Strong Lensing: Circumventing Assumptions on the Hubble Constant, Sound Horizon, and Dark Energy

We present a cosmological model-independent framework for testing general relativity (GR) on galactic scales by combining baryon acoustic oscillation (BAO) angular scale measurements with 120 galaxy-scale strong gravitational lensing systems. Using artificial neural networks (ANNs) and cubic spline reconstruction, we reconstruct the BAO angular scale from SDSS, BOSS, eBOSS, and DESI Data Release 2 (DR2), and infer the angular diameter distances to lenses and sources. Crucially, All the quantities used in the GR test are derived from observations and are independent of cosmological parameters such as the Hubble constant, the sound horizon, or the dark energy equation of state, minimizing potential biases from model-dependent distance priors. These distances are then incorporated into the strong lensing likelihood to constrain the parameterized post-Newtonian (PPN) parameter $γ_{\rm PPN}$ under two lens mass models: a constant-density-slope model ($P_1$) and a redshift-evolving model ($P_2$). For the $P_1$ model, the ANN reconstruction yields $γ_{\rm PPN} = 1.102^{+0.148}_{-0.125}$, consistent with GR at $1σ$ confidence level, while the cubic spline gives $γ_{\rm PPN} = 1.150^{+0.139}_{-0.118}$, consistent with GR at $2σ$ confidence level. For the $P_2$ model, the ANN reconstruction gives $γ_{\rm PPN} = 1.315^{+0.181}_{-0.155}$, compatible with GR at $2σ$, while the spline gives $γ_{\rm PPN} = 1.485^{+0.193}_{-0.168}$, showing mild tension at $\sim2.5σ$. The constraints exhibit a clear dependence on the adopted lens mass model, underscoring the critical role of lens modeling. No significant correlation is observed between $γ_{\rm PPN}$ and the Einstein radius. Overall, current galaxy-scale observations are consistent with GR, providing no evidence for deviations from Einstein's theory on kiloparsec scales.

astro-ph.CO↗

arXiv2025

CSST Strong Lensing Preparation: Cosmological Constraints Forecast from CSST Galaxy-Scale Strong Lensing

Strong gravitational lensing by galaxies is a powerful tool for studying cosmology and galaxy structure. The China Space Station Telescope (CSST) will revolutionize this field by discovering up to $\sim$100,000 galaxy-scale strong lenses, a huge increase over current samples. To harness the statistical power of this vast dataset, we forecast its cosmological constraining power using the gravitational-dynamical mass combination method. We create a realistic simulated lens sample and test how uncertainties in redshift and velocity dispersion measurements affect results under ideal, optimistic, and pessimistic scenarios. We find that increasing the sample size from 100 to 10,000 systems dramatically improves precision: in the $Λ$CDM model, the uncertainty on the matter density parameter, $Ω_m$, drops from 0.2 to 0.01; in the $w$CDM model, the uncertainty on the dark energy equation of state, $w$, decreases from 0.3 to 0.04. With 10,000 lenses, our constraints on dark energy are twice as tight as those from the latest DESI BAO measurements. We also compare two parameter estimation techniques -- MultiNest sampling and Bayesian Hierarchical Modeling (BHM). While both achieve similar precision, BHM provides more robust estimates of intrinsic lens parameters, whereas MultiNest is about twice as fast. This work establishes an efficient and scalable framework for cosmological analysis with next-generation strong lensing surveys.

astro-ph.CO↗

arXiv2025

Membership Inference Attacks on Large-Scale Models: A Survey

As large-scale models such as Large Language Models (LLMs) and Large Multimodal Models (LMMs) see increasing deployment, their privacy risks remain underexplored. Membership Inference Attacks (MIAs), which reveal whether a data point was used in training the target model, are an important technique for exposing or assessing privacy risks and have been shown to be effective across diverse machine learning algorithms. However, despite extensive studies on MIAs in classic models, there remains a lack of systematic surveys addressing their effectiveness and limitations in large-scale models. To address this gap, we provide the first comprehensive review of MIAs targeting LLMs and LMMs, analyzing attacks by model type, adversarial knowledge, and strategy. Unlike prior surveys, we further examine MIAs across multiple stages of the model pipeline, including pre-training, fine-tuning, alignment, and Retrieval-Augmented Generation (RAG). Finally, we identify open challenges and propose future research directions for strengthening privacy resilience in large-scale models.

cs.LG↗

arXiv2024

Determination of cosmic curvature independent of the sound horizon and $H_0$ using BOSS/eBOSS and DESI DR1 BAO observations

We present an improved model-independent method for determining the cosmic curvature using the observations of Baryon Acoustic Oscillations (BAOs) and the Hubble parameter. The purpose of this work is to provide insights into late-universe curvature measurements using available observational data and techniques. Thus, we use two sources of BAO data sets, BOSS/eBOSS and latest DESI DR1, and two reconstruction methods, Gaussian process (GP) and artificial neural network (ANN). It is important to highlight that our method circumvents influence induced by the sound horizon in BAO observations and the Hubble constant. Combining BAO data from BOSS/eBOSS plus DESI DR1, we find that the constraint on the cosmic curvature results in $Ω_K=-0.040^{+0.142}_{-0.145}$ with an observational uncertainty of $1σ$ in the framework of GP method. This result changes to $Ω_K=-0.010^{+0.405}_{-0.424}$ when the ANN method is applied. Further comparative analysis of samples from two BAO data sources, we find that there is almost no difference between the two samples. Although the curvature values obtained from the data samples using DESI DR1 are on the slightly positive and the samples using BOSS/eBOSS are on the slightly negative, these results both report that our universe has a flat spatial curvature within uncertainties, and the precision of constraining the curvature with two BAO samples is almost equal.

astro-ph.CO↗