arXiv subjects

Bao Wang

Publications and source records attributed to Bao Wang.

At least 19 recordsLinked to original sources

arXiv2026

Probing Extragalactic Ultralight Axion Dark Matter with Decade-long Blazar Optical Polarimetry of 1ES 1959+650

Axions or axion-like particles (ALPs) are well-motivated dark matter (DM) candidates whose coupling to photons induces periodic oscillations in the polarization angle of astrophysical light. This work reports the first search for such a signature using ten years of optical polarimetric monitoring of the blazar 1ES~1959+650. No statistically significant periodicity is detected, determined by a Lomb-Scargle periodogram combined with Monte Carlo analysis. Assuming a central DM density in the host galaxy, this null result places tight upper limits on the ALP-photon coupling constant at $g_{aγ}<(5.8 \times 10^{-14}-1.8\times 10^{-10})\,\mathrm{GeV}^{-1}$ across a broad ALP mass range of $m_a \sim (1.4\times10^{-23}-5.2\times10^{-20})\,\mathrm{eV}$. Our constraints surpass those from Very Long Baseline Array polarimetry of active galactic jets and from polarimetric data of the European Pulsar Timing Array, and are competitive with those from long-term Galactic pulsar timing of PSR J0437-4715 over the same ALP mass window. These results establish long-term blazar polarimetry as a competitive and complementary approach for probing axion-like DM on extragalactic scales.

astro-ph.HE↗

arXiv2026

Detecting Extragalactic Axion-like Dark Matter with Polarization Measurements of Fast Radio Bursts

Axions or axion-like particles (ALPs) are one of the promising dark matter (DM) candidates. A prevalent method to detect axion-like DM is to seek periodic oscillation in the polarization angles (PAs) of linearly polarized light emitted from astrophysical sources. In this work, we use the time-resolved polarization measurements of the hyperactive repeating fast radio burst, FRB 20220912A, detected by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) to search for extragalactic axion-like DM. Given a DM density profile of FRB 20220912A's host, we obtain upper limits on the ALP-photon coupling constant of $g_{a γ}<(3.4 \times 10^{-11}-1.9\times 10^{-9})\,\mathrm{GeV}^{-1}$ for the ALP masses $m_a \sim (1.4\times10^{-21}-5.2\times10^{-20})\,\mathrm{eV}$. Persistent polarimetric observations with FAST would extend the constraints to lower masses. Although the $g_{a γ}$ constraints derived from FRBs are less competitive than those from other methods, FRBs offer an alternative way to detect axion-like DM on extragalactic distance scales, complementary to galactic DM probes.

astro-ph.HE↗

arXiv2026

CyclOT: Learning Quadratic Optimal Transport Maps via Synchronized Forward-Backward Interpolants

We study the recovery of forward and reverse quadratic optimal-transport maps from unpaired samples in high dimensions. We introduce a bidirectional neural framework in which the learned maps induce forward and backward displacement interpolants, while the training objective combines bidirectional quadratic action, discriminator-restricted Jensen-Shannon endpoint objectives, and two-sided cycle consistency. The construction requires neither precomputed sample pairings nor an explicit convex-potential parameterization. For absolutely continuous probability measures supported on a compact convex set, and under the stated generator-approximation, discriminator-richness, and minimizer-attainment conditions, we prove a population recovery theorem: for every prescribed accuracy, the sum of the corresponding $L^2$ errors between any global minimizer and the forward and reverse quadratic Brenier maps is below that accuracy, provided the discriminator level is sufficiently large and the annealing action weight becomes sufficiently small. Moreover, the cycle loss is bounded above by $λW_2^2(μ_0,μ_1)$. Complementary results quantify approximate invertibility and show that exact endpoint Jensen-Shannon divergence and cycle consistency control missing target mass and many-to-one map collapse, respectively. Experiments on Swiss roll, MNIST, CelebA, single-cell perturbation data, and chest X-ray images evaluate endpoint fidelity, transport cost, inverse consistency, and the geometry of the induced interpolations.

math.OC↗

arXiv2026

Tightening Bounds on Warm Dark Matter with High-Redshift Gamma-Ray Bursts

The cold dark matter paradigm successfully explains large-scale structure but faces persistent tensions on small scales. Warm dark matter (WDM) with $\mathrm{keV}$-scale particles can alleviate these issues by suppressing small-scale structure formation. The presence of collapsed structures at high redshifts places strong lower limits on the WDM particle mass $m_x$. Gamma-ray bursts (GRBs) are ideal high-redshift probes due to their extreme brightness. Using the most recent \emph{Swift} GRB data accumulated over the past two decades, we derive robust constraints on $m_x$ by conservatively assuming that the comoving GRB formation rate is proportional to the cosmic star formation rate (SFR), with an additional redshift evolution parameterized as $(1+z)^α$. Applying a maximum-likelihood analysis to 118 GRBs with redshift $z<10$ and luminosity $L\ge 4.0\times10^{52}\,\mathrm{erg\,s^{-1}}$, we obtain $m_x \gtrsim 1.23\,\mathrm{keV}$ and $α=1.57^{+1.14}_{-0.57}$ at the 95\% confidence level (CL). The no-evolution scenario ($α=0$), in which the GRB rate exactly traces the SFR without additional redshift evolution, is excluded at the $5σ$ level. Adopting the best-fit value $α=1.57$ as a prior tightens the lower limit on $m_x$ to $m_x \gtrsim 1.59\,\mathrm{keV}$ at the 95\% CL. These robust constraints demonstrate that GRBs are a powerful probe of the early Universe. A better understanding of the relationship between the GRB rate and the SFR would enable even tighter limits on WDM models.

astro-ph.HE↗

arXiv2026

Second Order Drifting Models

Drifting models are a recent class of one-step generative models that evolve the model distribution during training using a predefined sample-based drift field. Although they avoid iterative inference, their kernel-based drift fields induce frequency-dependent training dynamics: In the linearized regime, each Fourier mode of the density residual decays at a rate determined by the kernel spectrum, leading to slow recovery of fine-scale structure. We propose Second-Order Drifting Models, which lift drifting dynamics into phase space by augmenting generated samples with artificial velocity variables. We show that the resulting density perturbations obey accelerated second-order dynamics in Fourier space, connecting drifting models to the celebrated Nesterov acceleration from optimization theory. This provides a principled mechanism for mitigating the spectral stiffness of first-order drifting while preserving one-step inference. We derive a practical semi-implicit training algorithm and evaluate it on synthetic distribution matching, sequential data generation, and robotic control. Across these settings, the second-order drifting model improves convergence behavior and achieves competitive or superior performance over first-order drifting baselines.

cs.LG↗

arXiv2026

Pinning Down the Geometry of the Type Ic Broad-Line Supernova 2026gzf

Type Ic broad-line supernovae (SNe Ic-BL) are often associated with energetic explosions that display a prompt outburst of high-energy emission. Since their progenitor lost the H and He envelopes before the explosion exposing the C/O core, their explosion dynamics and geometry can be seen in an unobscured and undistorted way. We present imaging polarimetry and spectropolarimetry of the Type Ic-BL SN 2026gzf obtained 4.6 and 16.5 days after the X-ray shock breakout, which was recorded by the Einstein Probe satellite as EP260321a, showing it to be one of the softest and intrinsically dimmest extragalactic fast X-ray transients. The persistent low continuum polarization indicates that the outer layer of SN 2026gzf is mostly spherical, suggesting the explosion did not significantly disrupt the progenitor envelope. At day 16.5, the calcium near-infrared triplet displays a peak polarization above 1.5%. The geometry of the associated line opacity is also compatible with an axisymmetric configuration. The spatial distribution of such oxygen-burning ashes thus indicates the presence of a symmetry axis of the excitation structure within the nearly spherical ejecta. The Ca II triplet profile is dominated by a primary component spanning ~25,000--40,000 km/s, alongside a distinct secondary component extending above 28,000 km/s whose polarization implies a non-axisymmetric, complex excitation geometry toward the outer ejecta By implementing a three-dimensional Monte-Carlo calculation, we infer that a viewing angle of ~40 degree from the symmetry axis of the excitation structure could plausibly reproduce the observed spectral and polarization profiles of the Ca II triplet.

astro-ph.HE↗

arXiv2026

Searching for Gamma Ray Bursts associated with CHIME Fast Radio bursts

Fast radio bursts (FRBs) and gamma-ray bursts (GRBs) are both linked to compact-object activity, yet their possible connection remains unclear. Here we perform a systematic search for spatial and temporal associations between FRBs in the second CHIME/FRB catalog and Swift GRBs. Instead of using the positional ellipses reported in the catalog, the full CHIME localization probability maps are adopted for spatial cross-matching. This yields 130 candidate pairs and increases the number of spatially consistent matches by a factor of several. Applying a distance-consistency criterion based on DM-inferred FRB redshifts and GRB distances inferred via the Amati relation reduces the sample to 37 pairs, including 26 GRB-preceding-FRB candidates (24 LGRB--FRB and 2 SGRB--FRB). Monte Carlo simulations show that the overall excess of associations is not statistically significant, and the distribution of matches across localization confidence levels is consistent with random expectations. These pairs are therefore not claimed as secure associations, but are used to constrain a possible subdominant FRB--GRB connection. These results place constraints on any FRB--GRB connection and highlight the need for improved localization and larger samples.

astro-ph.HE↗

arXiv2026

X-rays breaking out of pre-explosion ejecta mark a supernova's first light

Massive stars die as core-collapse supernovae, whose optical light emerges days after the implosion. Theory predicts that the initial collapse-driven shock, upon breaking through the star and dense circumstellar medium, emits a brief thermal flash of soft X-rays and ultraviolet. Yet these elusive first signals have remained largely undetected, owing to limited wide-field soft X-ray monitoring. Here we report the discovery of a soft X-ray flash, EP260321a, followed days later by a broad-lined supernova from an envelope-stripped progenitor. Its X-ray spectrum, best modeled with blackbody, establishes it as the long-sought archetypal shock breakout. The burst's duration and energetics place the breakout at a radius of 300 solar radii, tracing a dense surrounding shell and revealing abrupt mass ejection within the final month before collapse.

astro-ph.HE↗

arXiv2026

Measuring the Angular Auto-power Spectrum of Fast Radio Burst Dispersion Measures as a Robust Cosmological Probe and Baryon Tracer

Fluctuations in the cosmic electron density are imprinted on the dispersion measures (DMs) of fast radio bursts (FRBs), making DMs a promising probe of cosmology and the spatial distribution of ionized baryons. In this work, we present the first measurement of the angular auto-power spectrum of FRB DMs, using 3455 apparently non-repeating bursts from the CHIME/FRB Catalog 2. We detect an angular correlation signal at $>3σ$ significance, associated with large-scale electron-density fluctuations. By fitting the measured spectrum to theoretical models, we constrain two key parameter combinations: $Ω_{\rm b}h^2$-$H_0$, which probes the cosmic baryon density and expansion rate, and $Ω_{\rm b}h^2$-$f_{\rm d}$, which traces the baryon fraction in cosmic large-scale structure (LSS). We further assess the robustness of the power-spectrum method against systematic uncertainties arising from the assumed FRB redshift distribution and from the DM contributions of host galaxies (${\rm DM}_{\rm host}$), the Galactic halo (${\rm DM}^{\rm MW}_{\rm halo}$), and the Milky Way interstellar medium (${\rm DM}^{\rm MW}_{\rm ISM}$), using mock samples. Our results demonstrate that the angular power spectrum is largely insensitive to uncorrelated DM components such as ${\rm DM}_{\rm host}$, thereby effectively mitigating the impact of poorly constrained host-galaxy systematics. In contrast to the traditional ${\rm DM}_{\rm LSS}$-$z$ relation, this method does not require individual redshift measurements--it relies only on the overall redshift distribution--and it partially breaks the parameter degeneracies in the $Ω_{\rm b}h^2$-$H_0$ and $Ω_{\rm b}h^2$-$f_{\rm d}$ planes. These findings establish the DM angular power spectrum as a robust cosmological probe and a powerful baryon tracer.

astro-ph.HE↗

arXiv2026

Integrable pentagram-type maps on polyhedra via partial difference operators

This paper introduces a family of natural generalizations of the pentagram map from polygons to (twisted) polyhedra and proves their integrability through the partial difference operators. A canonical special case, which corresponds to the discrete Laplace transformation of discrete conjugate nets, is investigated in detail. We first establish a canonical bijection between the projective equivalence classes of these polyhedra in $\mathbb{RP}^3$ and the spectral data of doubly periodic partial difference operators modulo the gauge actions. Furthermore, we prove the complete integrability of these pentagram-type maps by explicitly identifying them with the refactorization maps on the Poisson-Lie group of pseudo partial difference operators. This algebraic identification naturally yields an explicit Lax representation and an $r$-matrix induced Poisson bracket for the geometric dynamics.

nlin.SI↗

arXiv2026

Flow Matching for Efficient and Scalable Data Assimilation

Data assimilation (DA) estimates a dynamical system's state from noisy observations. Recent generative models like the ensemble score filter (EnSF) improve DA in high-dimensional nonlinear settings but are computationally expensive. We introduce the ensemble flow filter (EnFF), a training-free, flow matching (FM)-based framework that accelerates sampling and offers flexibility in flow design. EnFF uses Monte Carlo estimators for the marginal flow field, localized guidance for observation assimilation, and utilizes a novel flow path that exploits the Bayesian DA formulation. It generalizes classical filters such as the bootstrap particle filter and ensemble Kalman filter. Experiments on high-dimensional benchmarks demonstrate EnFF's improved cost-accuracy tradeoffs and scalability, highlighting FM's potential for efficient, scalable DA. Code is available at https://github.com/Utah-Math-Data-Science/Data-Assimilation-Flow-Matching.

stat.ML↗

arXiv2026

AT 2024wpp: the most luminous fast-evolving optical transient linked to the merger explosion of a black-hole binary

Fast blue optical transients (FBOTs) represent one of the most exotic astrophysical transients, exhibiting unusually strong emission across X-ray, optical, and radio wavelengths. Their physical origins remain highly debated, with proposed explanations ranging from stellar explosion to tidal disruption event (TDE). Here we report observations of the most luminous FBOT, AT 2024wpp whose post-peak luminosity rebrightens in X ray and becomes flattening in optical in a manner follows the decay rate characteristic of TDEs ($L_{\rm bol} \propto t^{-5/3}$). This invokes energy contribution of accretion by a central compact object, getting further corroborations from hardening of X-ray spectral index and detection of outflow inferred from the emission lines at similar phase. Detailed modeling of luminsoity evolution favors a coalesce explosion of a 34 M$_{\odot}$ Wolf-Rayet star with a 15 M$_{\odot}$ black hole (BH), demonstrating that some FBOTs may be associated with TDE of a stellar blackhole.

astro-ph.HE↗

arXiv2026

RMFlow: Refined Mean Flow by a Noise-Injection Step for Multimodal Generation

Mean flow (MeanFlow) enables efficient, high-fidelity image generation, yet its single-function evaluation (1-NFE) generation often cannot yield compelling results. We address this issue by introducing RMFlow, an efficient multimodal generative model that integrates a coarse 1-NFE MeanFlow transport with a subsequent tailored noise-injection refinement step. RMFlow approximates the average velocity of the flow path using a neural network trained with a new loss function that balances minimizing the Wasserstein distance between probability paths and maximizing sample likelihood. RMFlow achieves near state-of-the-art results on text-to-image, context-to-molecule, and time-series generation using only 1-NFE, at a computational cost comparable to the baseline MeanFlows.

cs.LG↗

arXiv2026

Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs

Graph neural networks (GNNs) have emerged as powerful tools for learning protein structures by capturing spatial relationships at the residue level. However, existing GNN-based methods often face challenges in learning multiscale representations and modeling long-range dependencies efficiently. In this work, we propose an efficient multiscale graph-based learning framework tailored to proteins. Our proposed framework contains two crucial components: (1) It constructs a hierarchical graph representation comprising a collection of fine-grained subgraphs, each corresponding to a secondary structure motif (e.g., $α$-helices, $β$-strands, loops), and a single coarse-grained graph that connects these motifs based on their spatial arrangement and relative orientation. (2) It employs two GNNs for feature learning: the first operates within individual secondary motifs to capture local interactions, and the second models higher-level structural relationships across motifs. Our modular framework allows a flexible choice of GNN in each stage. Theoretically, we show that our hierarchical framework preserves the desired maximal expressiveness, ensuring no loss of critical structural information. Empirically, we demonstrate that integrating baseline GNNs into our multiscale framework remarkably improves prediction accuracy and reduces computational cost across various benchmarks.

cs.LG↗

arXiv2026

Improving Flow Matching by Aligning Flow Divergence

Conditional flow matching (CFM) stands out as an efficient, simulation-free approach for training flow-based generative models, achieving remarkable performance for data generation. However, CFM is insufficient to ensure accuracy in learning probability paths. In this paper, we introduce a new partial differential equation characterization for the error between the learned and exact probability paths, along with its solution. We show that the total variation gap between the two probability paths is bounded above by a combination of the CFM loss and an associated divergence loss. This theoretical insight leads to the design of a new objective function that simultaneously matches the flow and its divergence. Our new approach improves the performance of the flow-based generative model by a noticeable margin without sacrificing generation efficiency. We showcase the advantages of this enhanced training approach over CFM on several important benchmark tasks, including generative modeling for dynamical systems, DNA sequences, and videos. Code is available at \href{https://github.com/Utah-Math-Data-Science/Flow_Div_Matching}{Utah-Math-Data-Science}.

cs.LG↗

arXiv2026

Investigating the Anisotropy of Dispersion Measure Contribution from the Galactic Halo by Using Fast Radio Bursts

We propose a data-driven approach to reconstruct the all-sky distribution of the dispersion measure contribution from the Galactic halo ($\mathrm{DM_{halo}}$) through a spherical harmonic expansion, enabling an investigation of its possible anisotropies. Based on the NE2001 model and using 92 localized and 574 unlocalized non-repeating fast radio bursts (FRBs) at Galactic latitudes $|b|>15^\circ$, we find a significant dipole anisotropy in $\mathrm{DM_{halo}}$, pointing toward $(l=130^\circ,\, b=+5^\circ)$ with a $1σ$ uncertainty of approximately $28^\circ$. The $\mathrm{DM_{halo}}$ value in this direction is $63\pm9~\mathrm{pc~cm^{-3}}$, exceeding the all-sky mean by about $2.6σ$. This result is not significantly affected by the choice of Galactic ISM models. Furthermore, even when using a refined sample of 62 localized FRBs (excluding CHIME detections, repeaters, and unlocalized events), the dipole anisotropic structure persists, with a direction of $(l=141^\circ,\, b=+51^\circ)$ and a larger 1$σ$ uncertainty of $\sim 44^\circ$. Model comparisons using the Akaike Information Criterion and Bayesian evidence yield consistent preferences, and together they suggest that current FRB data slightly favor the existence of a dipole structure in $\mathrm{DM_{halo}}$. If this feature is not a statistical fluctuation or systematic error, its physical origin requires further investigation. Future FRB samples with larger sizes and more complete sky coverage will be essential to confirm or refute this possible anisotropic structure.

astro-ph.GA↗

arXiv2025

Plug-and-Play Image Restoration with Flow Matching: A Continuous Viewpoint

Flow matching-based generative models have been integrated into the plug-and-play image restoration framework, and the resulting plug-and-play flow matching (PnP-Flow) model has achieved some remarkable empirical success for image restoration. However, the theoretical understanding of PnP-Flow lags its empirical success. In this paper, we derive a continuous limit for PnP-Flow, resulting in a stochastic differential equation (SDE) surrogate model of PnP-Flow. The SDE model provides two particular insights to improve PnP-Flow for image restoration: (1) It enables us to quantify the error for image restoration, informing us to improve step scheduling and regularize the Lipschitz constant of the neural network-parameterized vector field for error reduction. (2) It informs us to accelerate off-the-shelf PnP-Flow models via extrapolation, resulting in a rescaled version of the proposed SDE model. We validate the efficacy of the SDE-informed improved PnP-Flow using several benchmark tasks, including image denoising, deblurring, super-resolution, and inpainting. Numerical results show that our method significantly outperforms the baseline PnP-Flow and other state-of-the-art approaches, achieving superior performance across evaluation metrics.

cs.CV↗

arXiv2025

A possible periodic RM evolution in the repeating FRB 20220529

Fast radio bursts (FRBs) are mysterious millisecond-duration radio transients of extragalactic origin. Some of them repeat, while others apparently do not. Investigations of periodic activity in repeating FRB have been conducted to probe their origins. While periodicity in the burst rate has been reported, studies of periodicities in other properties, such as dispersion measure (DM) and rotation measure (RM), are sparse. FRB~20220529 was monitored by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) for nearly three years, providing an opportunity to investigate periodicity in its observed properties. Here we report a possible period of $\sim 200$ days in the RM evolution, with a significance of {4.1 $σ$} estimated via the Lomb-Scargle algorithm and {3.1 $σ$} with the phase-folding method. Periodicity in the burst rate was also investigated. It may indicate that the FRB progenitor is in a binary system, which is consistent with the significant RM increase and prompt recovery of this FRB on a week-timescale. Other scenarios, such as a system with an intermediate-mass black hole, are also explored.

astro-ph.HE↗