arXiv subjects

Kaihao Wang

Publications and source records attributed to Kaihao Wang.

8 recordsLinked to original sources

arXiv2026

Anisotropic Maxwell neural operator for rapid parametric full-wave modelling of ion cyclotron resonance heating

Full-wave calculations of ion cyclotron resonance heating (ICRH) under different plasma dielectric conditions require repeated assembly and solution of large-scale discretised systems, limiting parameter sweeps and multi-case response analysis. We therefore propose an anisotropic Maxwell neural operator (AMNO) for rapid parametric modelling of ICRH full-wave responses for the Experimental Advanced Superconducting Tokamak (EAST), which learns, within the one-parameter dielectric-field family generated by varying the hydrogen minority fraction X_H over 0.01-0.05 under otherwise fixed settings, a shared solution operator from the spatially varying complex anisotropic dielectric-tensor field to the three-component complex electric field under frequency-domain Maxwell constraints. It represents global spatial coupling through spectral operator layers and local fine-scale responses, and combines sparse reference-field supervision with the frequency-domain Maxwell-equation residual. Comparisons with COMSOL reference solutions for the same EAST frequency-domain Maxwell-dielectric model show that AMNO reconstructs the principal spatial and spectral features and maintains stable accuracy for unseen interpolation test cases. With reference-field points reduced to 7.5% of the dense full-wave set, AMNO reduces the relative L_2 error by 66.1%-89.9% compared with a sparsely supervised Fourier neural operator (FNO-Sparse) under the same supervision and requires about 0.25 s for single-case inference. AMNO thus reduces dependence on dense reference-field supervision while enabling subsecond parametric complex-field inference, providing a physics-constrained and data-efficient surrogate for rapid in-range X_H sweeps and cross-case response analysis within the modelled EAST configuration.

physics.plasm-ph↗

arXiv2026

Detection of a dark matter subhalo in the strongly lensed system PJ011646

We present a strong lensing analysis of the system PJ011646 using high-resolution ($\sim$0.1 arcsec) Atacama Large Millimeter/submillimeter Array (ALMA) dust-continuum observations to test for the presence of dark matter substructures. The lens mass distribution is modelled with an elliptical power law and third- and fourth-order multipoles (PL+MP; $m=3,4$), plus external shear. The multipoles have amplitudes of $\simeq$1.5 per cent of the convergence, consistent with nearby early-type galaxies, and improve the fit by $Δ\ln Z = 52.1$ relative to a pure PL model. Using this best-fitting macromodel, we perform a grid-based subhalo search in the image plane, parametrising the perturber as a spherical NFW. A subhalo in two locations in the image plane improves the fit by $Δ\ln Z>10$. Both correspond to the same location in the source plane, so they are partially degenerate; follow-up analysis suggests that only one is physically real. This is a subhalo of mass $M_{200} = {2.78}_{-0.66}^{+0.43} \times 10^{10} \, M_\odot$ and concentration $c_{200} = 30_{-7}^{+5}$, detected at $\sim$5.8$σ$ significance (relative to the PL+MP). This concentration is consistent with that expected for a typical tidally stripped Navarro-Frenk-White subhalo. The enclosed projected mass is most tightly constrained within a radius of 2 kpc, where we infer $M_{\rm sub} = {3.57}_{-0.14}^{+0.16}\times 10^9 \, M_\odot$. From grid cells consistent with no detection ($Δ\ln Z < 10$), we derive limits on the minimum subhalo mass that could have been detected at $3σ$ significance, finding $M_{200} \approx 8 \times 10^{8} \, M_\odot$ in the most sensitive regions of the lensed arcs. This demonstrates that ALMA continuum imaging at sub-arcsecond resolution can probe dark matter substructure in a mass regime where cold and warm dark matter models predict different abundances of subhalos.

astro-ph.GA↗

arXiv2026

Measuring the Stellar-to-Halo Mass Relation at $\sim10^{10}$ Solar masses, using forthcoming space-based imaging of galaxy-galaxy strong lenses

The stellar-to-halo mass relation (SHMR) is central to understanding the co-evolution of galaxies and their host dark matter haloes, yet it remains weakly constrained for dwarf galaxies owing to their faintness, especially beyond the Local Group. Strong gravitational lensing offers a unique probe of the SHMR at sub-galactic scales and cosmological distances, as the masses of subhalos within the main lens can be inferred from the perturbations they imprint on lensed images. Anticipating the discovery of $\sim10^5$ galaxy--galaxy strong lenses by forthcoming facilities such as \textit{Euclid}, we perform an end-to-end simulation to forecast \textit{Euclid}'s constraints on the SHMR at the halo mass scale of $\sim10^{10}\,\mathrm{M}_\odot$. We generate mock \textit{Euclid} VIS images of lens systems hosting a fiducial $3\times10^{10}\,\mathrm{M}_\odot$ subhalo and vary its properties to assess the robustness of mass inference. We find that \textit{Euclid}'s angular resolution cannot break the intrinsic mass--concentration degeneracy of subhaloes, nor deblend the light of satellite galaxies (when present) associated with them, leading to biased inferred halo masses. These limitations are overcome with high-resolution follow-up imaging from facilities such as the \textit{Hubble Space Telescope}, enabling accurate halo-mass measurements. We forecast that a statistical sample of $\sim100$ such systems, combining lensing-derived halo masses with stellar masses from photometric SED fitting, can constrain the SHMR at dwarf-galaxy scales with a precision of $\sim0.05$~dex in halo mass and $\sim0.03$~dex in stellar mass, enabling powerful tests of galaxy formation theories.

astro-ph.GA↗

arXiv2026

Probing Dark Matter Substructures with Free-Form Modelling: A Case Study of the `Jackpot' Strong Lens

Characterising the population and internal structure of sub-galactic halos is critical for constraining the nature of dark matter. These halos can be detected near galaxies that act as strong gravitational lenses with extended arcs, as they perturb the shapes of the arcs. However, this method is subject to false-positive detections and systematic uncertainties, particularly degeneracies between an individual halo and larger-scale asymmetries in the distribution of lens mass. We present a new free-form lens modelling code, developed within the framework of the open-source software \texttt{PyAutoLens}, to address these challenges. Our method models mass perturbations that cannot be captured by parametric models as pixelized potential corrections and suppresses unphysical solutions via a Matérn regularisation scheme that is inspired by Gaussian process regression. This approach enables the recovery of diverse mass perturbations, including subhalos, line-of-sight halos, external shear, and multipole components that represent the complex angular mass distribution of the lens galaxy, such as boxiness/diskiness. Additionally, our fully Bayesian framework objectively infers hyperparameters associated with the regularisation of pixelized sources and potential corrections, eliminating the need for manual fine-tuning. By applying our code to the well-known `Jackpot' lens system, SLACS0946+1006, we robustly detect a highly concentrated subhalo that challenges the standard cold dark matter model. This study represents the first attempt to independently reveal the mass distribution of a subhalo using a fully free-form approach.

astro-ph.CO↗

arXiv2025

The "Little Dark Dot": Evidence for Self-interacting Dark Matter in the Strong Lens SDSS J0946+1006?

Previous studies, based on precise modeling of a gravitationally lensing image, have identified what may be an extremely compact, dark perturber in the well-known lensing system SDSS J0946+1006 (the "Jackpot"). Its remarkable compactness challenges the standard cold dark matter (CDM) paradigm. In this paper, we explore whether such a compact perturber could be explained as a core-collapse halo described by the self-interacting dark matter (SIDM) model. Using the isothermal Jeans method, we compute the density profiles of core-collapse halos across a range of masses. Our comparison with observations indicates that a core-collapse halo has an inner density profile and mass enclosed within 1 kpc that fit the data well, but only if the halo has a total mass $\sim10^{11}~{\rm M_{\odot}}$. While a halo of this mass should host a detectable galaxy, the current observational upper limit on the perturber's luminosity remains uncertain. Resolving whether or not the data support the presence of a core-collapse SIDM halo therefore requires future deep observations to measure its luminosity.

astro-ph.GA↗

arXiv2025

Not so dark, not so dense: an alternative explanation for the lensing subhalo in SDSSJ0946+1006

Previous studies of the strong lens system SDSSJ0946+1006 have reported a dark matter subhalo with an unusually high central density, potentially challenging the standard cold dark matter (CDM) paradigm. However, these analyses assumed the subhalo to be completely dark, neglecting the possibility that it may host a faint galaxy. In this work, we revisit the lensing analysis of SDSSJ0946+1006, explicitly modelling the subhalo as a luminous satellite. Incorporating light from the perturber broadens the range of allowed subhalo properties, revealing solutions with significantly lower central densities that are consistent with CDM expectations. The inferred luminosity of the satellite also aligns with predictions from hydrodynamical simulations. While high-concentration subhaloes remain allowed, they are no longer statistically preferred. The luminous subhalo model yields a better fit to the data, while also offering a more plausible explanation that is in line with theoretical expectations. We validate our methodology using mock data, demonstrating that neglecting subhalo light can lead to inferred mass distributions that are artificially compact.

astro-ph.CO↗

arXiv2025

Galaxy Mass Modelling from Multi-Wavelength JWST Strong Lens Analysis: Dark Matter Substructure, Angular Mass Complexity, or Both?

We analyze two galaxy-scale strong gravitational lenses, SPT0418-47 and SPT2147-50, using JWST NIRCam imaging across multiple filters. To account for angular complexity in the lens mass distribution, we introduce multipole perturbations with orders $m=1, 3, 4$. Our results show strong evidence for angular mass complexity in SPT2147, with multipole strengths of 0.3-1.7 $\%$ for $m=3, 4$ and 2.4-9.5 $\%$ for $m=1$, while SPT0418 shows no such preference. We also test lens models that include a dark matter substructure, finding a strong preference for a substructure in SPT2147-50 with a Bayes factor (log-evidence change) of $\sim 60$ when multipoles are not included. Including multipoles reduces the Bayes factor to $\sim 11$, still corresponding to a $5σ$ detection of a subhalo with an NFW mass of $\log_{10}(M_{200}/M_{\odot}) = 10.87\substack{+0.53\\ -0.71}$. While SPT2147-50 may represent the fourth detection of a dark matter substructure in a strong lens, further analysis is needed to confirm that the signal is not due to systematics associated with the lens mass model.

astro-ph.CO↗

arXiv2025

TAET: Two-Stage Adversarial Equalization Training on Long-Tailed Distributions

Adversarial robustness is a critical challenge in deploying deep neural networks for real-world applications. While adversarial training is a widely recognized defense strategy, most existing studies focus on balanced datasets, overlooking the prevalence of long-tailed distributions in real-world data, which significantly complicates robustness. This paper provides a comprehensive analysis of adversarial training under long-tailed distributions and identifies limitations in the current state-of-the-art method, AT-BSL, in achieving robust performance under such conditions. To address these challenges, we propose a novel training framework, TAET, which integrates an initial stabilization phase followed by a stratified equalization adversarial training phase. Additionally, prior work on long-tailed robustness has largely ignored the crucial evaluation metric of balanced accuracy. To bridge this gap, we introduce the concept of balanced robustness, a comprehensive metric tailored for assessing robustness under long-tailed distributions. Extensive experiments demonstrate that our method surpasses existing advanced defenses, achieving significant improvements in both memory and computational efficiency. This work represents a substantial advancement in addressing robustness challenges in real-world applications. Our code is available at: https://github.com/BuhuiOK/TAET-Two-Stage-Adversarial-Equalization-Training-on-Long-Tailed-Distributions.

cs.LG↗