arXiv subjects

Liang Gao

Publications and source records attributed to Liang Gao.

At least 19 recordsLinked to original sources

arXiv2026

Enhanced structure-dependent alpha decay tunneling in deformed odd-odd nuclei via intense two-color laser pulses

In the present work, we investigate the effect of ultra-intense laser fields on the $α$ decay tunneling of deformed odd-odd nuclei. Calculations performed within the deformed density-dependent cluster model show that, for deformed odd-odd $α$ emitters, the laser-induced variation in the penetration probability separates into distinct branches associated with different valence particle-hole configurations relative to the $Z=82$ and $N=126$ shell closures. In contrast to the even-even and odd-$A$ cases, the odd-odd parent nucleus most sensitive to the laser field is not the one with the lowest $α$ decay energy. This indicates that the laser response is governed not only by $Q_α$ but also by structural correlations associated with the simultaneously unpaired proton and neutron. We further examine the pulse-averaged response under a two-color laser field. The results show that an optimized two-color field enhances the pulse-averaged laser response for most of the studied nuclei, while highly sensitive nuclei exhibit an effective gain equivalent to increasing the laser intensity by more than one order of magnitude. These findings identify structural determinants of laser-assisted $α$ decay in odd-odd nuclei and provide guidance for selecting candidate nuclei and optimizing laser parameters in future experiments.

nucl-th↗

arXiv2026

AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving

Practical autonomous driving requires models that generalize by reasoning through spatial-temporal possibilities to exclude unsafe outcomes. While state-of-the-art (SOTA) methods use parallel planning architectures, they fail to explicitly couple speed decisions with agent behavior along the driving path, leading to suboptimal coordination. To address this, we propose a cascaded framework that transforms longitudinal planning from an independent prediction task into a path-conditioned reasoning process. On the model side, we introduce an anchor-based regression design that conditions longitudinal prediction on the lateral drive path, and reformulate longitudinal planning as 1D displacement prediction along the path. This reduces geometric uncertainty and sharpens the model's focus on interaction-driven dynamics. On the data side, we introduce a planning-oriented data augmentation strategy that simulates rare safety-critical events by programmatically inserting agents and relabeling longitudinal targets to enforce collision avoidance. Evaluated on the challenging Bench2Drive benchmark, our method achieves SOTA performance with a driving score of 89.07 and a success rate of 73.18%, demonstrating significantly improved coordination and safety. Further evaluation on Fail2Drive confirms strong generalization to rare edge cases where parallel formulations typically fail. Project page:https://yanhaowu.github.io/AlignDrive/.

cs.RO↗

arXiv2026

Mapping Dark Matter in Merging Clusters with Weak Lensing for CSST: Morphology and Filament Recovery

Weak gravitational lensing offers a direct probe of the projected dark matter distribution in cluster-scale structures and the surrounding cosmic web. We investigate the capability of the China Space Station Telescope (CSST) to recover non-spherical dark matter morphologies using mock reduced-shear maps generated from cosmological N-body simulations. We apply the deep-learning-based MIU2Net mass-reconstruction framework to CSST-like wide- and deep-field source densities, focusing on two representative systems: filament-connected halo pairs and elongated halos. MIU2Net accurately recovers halo centers, elongated mass distributions, and the filamentary structures connecting halo pairs in both survey configurations. Halo centers are localized within about 0.1 h^(-1) Mpc, and elongated-halo shapes and orientations are reliably recovered. Filament paths are stable against shape noise, with mean normalized instability parameters I_F, defined as the mean deviation from the true filament locus in units of R_200, decreasing from 0.041 to 0.037 in the low-redshift bin and from 0.051 to 0.045 in the high-redshift bin from the wide to deep field. The recovered filament shapes remain consistent with the true maps at the percent level. Compared with conventional Kaiser-Squires reconstruction, MIU2Net better preserves low-contrast anisotropic structures while suppressing small-scale noise. These results demonstrate that CSST weak-lensing data, combined with machine-learning-based mass reconstruction, will enable statistical studies of cluster assembly, anisotropic matter accretion, and the connection between massive halos and the filamentary cosmic web.

astro-ph.CO↗

arXiv2026

Methods, trade-offs and opportunities in high-speed optical microscopy for neural voltage imaging

High-speed optical imaging of dynamic neuronal activity is essential yet challenging in neuroscience. While calcium imaging has been firmly established as a workhorse technique for monitoring neuronal activity, its limited temporal resolution and indirect measurement restrict its ability to capture rapid inhibitory and excitatory events and subthreshold voltage oscillations. In contrast, voltage imaging directly measures membrane potential fluctuations, providing a comprehensive and precise representation of neuronal circuit dynamics. Recent advancements in voltage-sensitive dyes and, particularly, genetically encoded voltage indicators have significantly enhanced the feasibility of voltage imaging, prompting the development of advanced fluorescence microscopy methods optimized for high-speed acquisition. However, achieving millisecond-scale temporal resolution remains challenging due to inherent trade-offs among imaging speed, spatial resolution, and signal-to-noise ratio. Conventional raster-scanning approaches, including confocal microscopy, are fundamentally limited by their slow frame rates, precluding the capture of rapid neuronal events from multiple neurons simultaneously. Alternative techniques such as random-access scanning, spatiotemporal multiplexing, and computational optical imaging have successfully addressed these constraints, enabling kilohertz-level imaging of neuronal activity in both two-dimensional and three-dimensional contexts. This review summarizes recent progress in high-speed optical microscopy for voltage imaging and discusses its transformative potential for neuroscience research.

physics.optics↗

arXiv2026

BioEVAL: A global, multi-institutional benchmark of large language and multimodal models for bioengineering

Large Language Models (LLMs) have demonstrated historic breakthroughs in general reasoning with early successes in biomedical science. However, existing LLM benchmarking emphasizes factual recall, offering limited insight into model performance on frontier and multimodal tasks. We assembled BioEVAL (BioEngineering Validation of AI and LLMs), a global, multi-institutional initiative designed to assess experimental reasoning capability across bioengineering (BE) subfields. BioEVAL spans 11 major BE subfields plus a set of uncategorized items, bringing together 22 research groups to create a PhD-level benchmark comprising 608 evaluation items: 1) 380 multiple-choice questions (MCQs, 359 retained after audit), 2) 218 literature synthesis tasks, and 3) 10 multimodal problems with experimental image interpretation. Benchmark items underwent authoring-group expert review and centralized quality control before evaluation. Following evaluation, a blinded cross-group consensus audit of the highest- and lowest-accuracy MCQ items flagged 21 questions for revision or removal; these were withheld, and all reported MCQ results are computed on the 359 retained items. We evaluated diverse cloud-scale foundation/multimodal models (e.g., ChatGPT, Gemini, and Grok) and locally deployable models suitable for inference on consumer-grade GPUs. Models achieved the highest accuracy of up to 90% on MCQs, similarity score of 0.72 on literature synthesis, and accuracy of 80% on a small sample of multimodal reasoning questions, with substantial performance variation across subfields. Leaderboard rankings characterize current capabilities, limitations, and development priorities across the evaluated BE task categories. BioEVAL is maintained as an extensible benchmark with standardized protocols for continuing expert item contribution and model evaluation.

cs.AI↗

arXiv2026

Reducing False Positives in Strong-Lens Searches with Generalized-Mean Consensus of Machine-Learning Ensembles in the Kilo-Degree Survey

Context. In wide-field surveys, the main challenge is not just classifier sensitivity, but the overwhelming number of false positives. Searching for strong lenses among millions to bilions of galaxies produces many contaminants, making the bottleneck for follow-up inspection and building statistically useful lens samples. Aims. We aim to improve the purity of strong-lens candidate selection in KiDS DR4 by combining several classifiers. The objective is to retain high completeness for known candidates while substantially reducing the fraction of non-lenses. Methods. We trained convolutional, Transformer-based, and hybrid classifiers, including Li ResNet+, Swin Transformer variants, Swin-MLP, and DemiLensNet. Their probabilistic outputs were combined at score level using averaging and a generalized mean consensus. The models were tested on simulated KiDS-like lens images and then evaluated on real KiDS DR4 lens candidates embedded in a non-lens sample. Results. On the simulated test set, ensembles show no advantage over the best single models. On the mixed real KiDS test set, the arithmetic mean reduces the false-positive rate at 90% completeness from 0.016-0.020 (the range spanned by the two best individual models) to 0.011 for the seven-model ensemble. The generalized mean reduces it further, to 0.007. Applied to the full LRG and BG samples at the same 90% completeness level, the generalized mean reduces returned candidates by roughly 50% for LRGs and 70% for BGs, relative to the best single model. After visual inspection, we obtain 170 new high-quality candidates (24 Class A and 146 Class B), together with 1706 Class C candidates. Conclusions. Our results demonstrate that the generalized mean consensus of an ML ensemble strategy provides a practical route to reducing the visual inspection workload while preserving a high recovery rate of promising strong-lens candidates.

astro-ph.GA↗

arXiv2026

Identification of gravitational lenses obscured by foreground light in the KiDS dataset using U-Nets and ResNets

*Context.* Many lensing images are often obscured by foreground light from the central galaxies, making them challenging to detect. *Aims.* To address the limitations of previous lens search efforts, particularly for samples with smaller $R_E$ or faint lensed images, we developed a composite convolutional neural network framework that utilizes both U-Net and ResNet architectures for feature extraction and classification. *Methods.* We propose a hybrid search method that combines U-Net and ResNet architectures to enhance the detection of foreground galaxy-obscured lenses. Our approach consists of two main stages: first, the U-Net model separates the foreground galaxy light from potential lensing signals, creating residual images that highlight the lensing features. Next, the ResNet module performs binary classification on these residual images to detect lensing signals. *Results.* We evaluated the hybrid search method with real observational data to demonstrate its effectiveness, achieving a recall of 71.5% and a 4.5% false positive rate at a confidence threshold of 0.6. Applying this method to over 638,398 galaxy samples from the Kilo-Degree Survey Data Release 4 and conducting thorough inspections, we identify 88 Class A, 322 Class B, and 1,758 Class C candidates. *Conclusions.* This hybrid approach significantly enhances the completeness of existing strong gravitational lensing searches and shows great potential for improving future astronomical surveys.

astro-ph.IM↗

arXiv2026

LenNet: Direct Detection and Localization of Strong Gravitational Lenses in Wide-Field Sky Survey Images

Strong gravitational lenses are invaluable tools for addressing fundamental questions in astrophysics, from the nature of dark matter to the expansion of the universe. While current sky surveys have successfully identified thousands of lens candidates, the search methods employed face a critical challenge. The conventional approach relies on a "crop-and-classify" strategy, where small images are first cut out around billions of potential host galaxies before being individually classified. This process creates a significant computational and storage bottleneck that is unsustainable for future large-scale surveys. To overcome this limitation, we propose LenNet, an object detection model that identifies lenses directly within large, original survey images. Our method completely bypasses the inefficient cropping step by framing the problem as a direct detection and localization task. We initially train LenNet on simulated data to learn the complex features of gravitational lenses and then use transfer learning to fine-tune the model on a limited set of real, labeled examples from the Kilo-Degree Survey (KiDS). Our experiments show that LenNet performs remarkably well on real survey data, validating its potential as a highly efficient and scalable solution for lens discovery in massive astronomical surveys.

astro-ph.GA↗

arXiv2026

A Wavelet-Integrated Search Pipeline for Narrowband Technosignatures in FAST Observations of 33 Exoplanet Systems

Building on prior FAST targeted and blind SETI campaigns toward 33 exoplanet systems, we introduce a wavelet-integrated search pipeline for narrowband technosignature candidates in radio dynamic spectra. At its core, the pipeline uses a Multi-Scale Wavelet Net (MSWNet) to produce an interpretable multi-resolution representation, followed by a lightweight parameter estimator for endpoint localization. Rather than relying solely on hard-threshold drift searches, the pipeline reframes narrowband detection as wavelet-guided feature extraction followed by endpoint regression, morphology-aware filtering, raw-data S/N validation, and multi-beam anticoincidence veto. Applied to real FAST data, the pipeline recovers representative events from prior analyses and produces a compact set of veto-ready candidates for downstream inspection. The resulting workflow preserves interpretability, low regression complexity, and auditable threshold control, making it readily transferable to other radio surveys and large-scale technosignature searches.

astro-ph.IM↗

arXiv2026

Morphology classification for galaxies in the Kilo Degree Survey using a label-efficient self-supervised learning framework

Galaxy morphology classification is fundamental to understanding galaxy formation and evolution. The advent of large-scale sky surveys has produced an unprecedented volume of galaxy images, making traditional manual classification impractical. Although supervised deep learning can achieve high accuracy, it requires large labeled datasets that are time-consuming to construct. In contrast, unsupervised methods often show limited classification performance. To address this limitation, we propose a label-efficient self-supervised learning framework for galaxy morphology classification. Our method first learns robust morphological representations from 305,583 unlabeled KiDS galaxy images through contrastive learning, and then trains a classifier using only 5,000 human-labeled images. The classifier separates galaxies into five categories: elliptical, spiral, lenticular-disk, irregular, and "other." Using a ResNet-50 model with a crop size of 64x64 pixels, our approach achieves an overall test accuracy of up to 91.0% (90.5% +/- 0.2% on average) on the human-classified catalog. The corresponding F1 scores for elliptical, spiral, irregular, lenticular-disk, and "other" galaxies are 0.96, 0.86, 0.86, 0.95, and 0.92, respectively. We apply this pipeline to the Kilo-Degree Survey Data Release 5 and produce a publicly available morphology catalog of 310,583 galaxies. This is the first morphology catalog for KiDS galaxies and provides a valuable resource for future studies of galaxy evolution. Our results show that self-supervised learning can substantially reduce the need for manual labels while maintaining high classification accuracy, making it a promising and scalable approach for automated galaxy morphology classification in the era of large-scale surveys.

astro-ph.GA↗

arXiv2026

As Cities Grow, They Spread Out Rather Than Rise

Cities and settlements are a global phenomenon, and their continued expansion is fundamentally transforming patterns of resource demand. This transformation is reflected in growing pressures on land, material use, and infrastructure. Thus, understanding how cities grow in space is essential for planning future urban development and determining all its consequences. Here, we characterize four geometrically related dimensions of urban growth: two horizontal dimensions describing the urban area, one vertical dimension represented by average building height, which together with the urban area defines building volume, and the population dimension. Analyzing a global dataset of growing cities, for the period 1975-2025 we identify three main patterns. First, urban growth is fundamentally anisotropic -- as cities gain population, they expand much more rapidly in the horizontal than in the vertical direction. Second, average building height decreases in the vast majority of cities as they grow, indicating that vertical development is not only slower than horizontal expansion but on average, can act in an effectively negative manner by giving greater weight to the horizontal dimensions. Third, despite their diverse growth trajectories, cities converge toward characteristic population densities, both with respect to urban area and building volume. We further explore alternative scenarios for future urban growth. These projections show that different assumptions about horizontal and vertical scaling can lead to substantially different demands for urban land and building volume. These findings reveal constraints on the long-term evolution of urban form and provide a quantitative assessment for understanding the spatial dynamics and development of cities.

physics.soc-ph↗

arXiv2026

ELUCID-DESI II. Revealing dark matter mass, tidal, and velocity (MTV) fields using galaxy group phase information

We introduce a novel method for reconstructing the cosmic mass, tidal, and velocity (MTV) fields over the redshift range $0 < z < 0.6$ using the phase information of galaxy groups. This approach replaces the explicit theoretical bias correction typically needed to relate galaxy groups to the underlying dark matter density field with a simulation-calibrated statistical mapping, reducing a major source of systematic uncertainty and making the method directly applicable to spectroscopic redshift surveys such as the DESI Bright Galaxy Survey (BGS). We evaluate the performance of our MTV reconstruction pipeline with mock redshift surveys that include a comprehensive set of observational selection effects. The galaxy groups used as tracers are identified with an extended halo-based group finder applied to the DESI mock galaxy catalogue with an apparent magnitude limit of $m_z < 19.65$, yielding a galaxy number comparable to that of the DESI BGS faint sample ($m_r < 20.175$). Our tests show that the reconstructed velocities are accurate and unbiased, with a residual dispersion of $\sim 120\ \mathrm{km\,s^{-1}}$ across the redshift bins. The recovered velocity field allows us to shift galaxy groups to their real-space positions, thereby correcting for the Kaiser effect. By iteratively applying this Kaiser correction to the galaxy groups, we further reconstruct the tidal field and the mass-density distribution. The reconstruction is stable with respect to the grid resolution. Overall, our results demonstrate that this group-based phase-space reconstruction provides a robust pathway to recovering the dark matter MTV fields, with strong prospects for application to DESI BGS data.

astro-ph.CO↗

arXiv2026

Fisher-information limits of detector-bandwidth-efficient 3D light-field microscopy

Light-field microscopy enables snapshot volumetric imaging, but its information rate is constrained by both optical encoding and detector readout architecture. Here we develop a task-dependent Fisher-information framework that evaluates optical encoders relative to the detector resource limiting acquisition throughput. We compare full Fourier light-field microscopy (FLFM), squeezed light-field microscopy (SLIM), and frame-rate-matched FLFM under a common optical geometry, photon budget, and row-limited camera model. Sparse scenes are analyzed using a 3D point-emitter Fisher matrix, and dense scenes using Fourier-mode information on tilted spectral slices. At s=0.25, SLIM provides 2.40x higher axial Fisher information per camera bandwidth and 1.89x higher 3D D-optimal position information than frame-rate-matched FLFM. For dense scenes, it provides 1.85x higher integrated Fourier-mode Fisher information per bandwidth, 4x greater axial-frequency extent, and approximately 11x larger projected lateral hard-support area. Sweeps over compression factor and view tilt show that these advantages reflect a general detector-allocation principle rather than a specific operating point. More broadly, the framework can be adapted to other camera architectures by incorporating architecture-specific measurement models and detector-throughput costs, providing a general basis for co-designing optical encoding, scene statistics, and camera readout.

physics.optics↗

arXiv2026

Testing the Reptation Picture: Topological Constraint from Monomer Dynamics

The reptation model postulates that entangled polymers slide within a fractal tube. Here we employ a model-independent relation between the zero-displacement probability and the mean-square displacement that applies to time-dependent fractal structures, enabling direct measurement of the fractal dimension $d_\mathrm{f}$ of the geometry experienced by monomer motion. For two-dimensional obstacle arrays and in the slip-link model, $d_\mathrm{f}$ agrees with the reptation prediction $d_\mathrm{f}=1/ν$ (where $ν$ is the Flory exponent). In polymer melts, however, we find $d_\mathrm{f} \approx 2.6$ --- a value close to the fractal dimension of percolation clusters, not the reptation value $d_\mathrm{f}=2$. This contrasts sharply with the reptation picture, in which a Rouse chain slides in a fractal structure with $d_\mathrm{f}=2$, spectral dimension $d_\mathrm{s}=1$, and walk dimension $d_\mathrm{w}=4$; our results point instead to a percolation-like scenario, characterized by $d_\mathrm{f}\approx 2.6$, $d_\mathrm{s}\approx 1.3$, and $d_\mathrm{w}\approx 4$ --- revealing a dynamically emergent, finite-size fractal geometry distinct from the static tube.

cond-mat.soft↗

arXiv2026

How mergers shape galaxy morphology in the IllustrisTNG simulation

How galaxy mergers drive morphological evolution remains an open question. Traditional views hold that major mergers produce elliptical galaxies, while minor mergers form dispersion-dominated components such as galactic bulges. However, more recent work has challenged this simple picture, suggesting a more complex evolutionary scenario. In this study, we use the IllustrisTNG cosmological simulation to investigate how mergers shape galaxy morphology, across a broad range of galaxy masses and merger mass ratios. Our results show that the post-merger galaxy morphology is primarily determined by three factors: collision angle $\overlineθ$, cold gas fraction $f_\mathrm{cold\,gas}$, and pre-merger galaxy morphology $\mathrm{(B/T)_{*,pre}}$. Specifically, spiral-in mergers with large $\overlineθ$ increase the rotational support of the system, allowing the gas to settle into an extended disk. When the system is rich in cold gas, star formation within the newly formed gas disk can further strengthen the disk-dominated structure of the remnant. On the other hand, head-on mergers with small $\overlineθ$ typically disrupt ordered galactic motion, producing more dispersion-supported remnants. Overall, we interpret our results within a unified picture of merger-driven morphological transformation in galaxies.

astro-ph.GA↗

arXiv2026

Distinguishing the nature of dark matter by mapping cosmic filaments from Lyman-alpha emission

The standard $Λ$CDM cosmological model predicts that cosmic filaments are highly clumpy, whereas warm dark matter -- invoked to address small-scale challenges in $Λ$CDM -- produces filaments that are noticeably smoother and less structured. In this work, we investigate the potential of Lyman $α$ (Ly$α$) emission to trace cosmic filaments at redshifts $z=2.5$ and $z=4$, and assess their potential for constraining the nature of dark matter. Our analysis shows that Ly$α$ filaments provide a promising observational probe of dark matter: at $z=4$, differences in filament smoothness and surface brightness serve as distinctive signatures between models. Looking ahead, the upcoming generation of 30-meter class telescopes will be critical for enabling these measurements, offering a compelling opportunity to distinguish the nature of dark matter by mapping the structure of cosmic filaments.

astro-ph.CO↗

arXiv2026

The capture of halo material by orbiting subhaloes

When a dark matter halo falls into a more massive object and becomes a subhalo, it typically loses much of its mass through tidal stripping. The reverse process is also possible in principle. The subhalo may gravitationally capture material from its host. If sufficiently efficient, this process could make an initially starless subhalo visible. We use high-resolution N-body simulations to estimate the efficiency of capture. We find that after an extended period orbiting within its host, at most $\sim 10^{-4}$ of a subhalo's remaining mass has been acquired since infall. This captured material is less concentrated to subhalo centre than material retained from before infall. It is also very much less abundant than host material that is instantaneously passing through the subhalo on almost unperturbed orbits. Captured stars are not sufficiently spatially concentrated to be distinguished from the dominant background of "field" stars, and their concentration in velocity space is no greater than that of typical stellar streams in the halo. Unfortunately, stellar capture is not efficient enough to allow initially starless low-mass subhaloes to be detected.

astro-ph.GA↗

arXiv2026

Freeform super-oscillatory optics for CMOS-integrated THz super-resolution imaging

The diffraction limit fundamentally constrains the spatial resolution of far-field imaging systems. While near-field techniques can circumvent this limit, their inherently short working distances (WD) severely restrict practical applications. Super-oscillatory lenses (SOLs) offer a far-field alternative; however, conventional SOLs are plagued by discrete operating wavelengths, low efficiencies (below 5%), and formidable trade-offs among numerical aperture, chromatic aberration, and depth of focus (DOF). Here, we introduce a nonlocal, nonlinear-curvature mechanism to design a freeform SOL that achieves ultrabroadband (0.3 to 1 THz), achromatic super-resolution focusing with an unprecedented efficiency of 44%. Operating at a 9 mm WD, the lens maintains a consistent sub-diffraction full-width at half-maximum (FWHM) of around 0.45 wavelength alongside an extended DOF of around 10 wavelengths. By integrating a compact 65-nm CMOS oscillator-radiator array, we establish an advanced imaging platform capable of resolving complex 2D and 3D sub-millimeter features (down to 0.15 mm). Readily scalable to the optical regime via two-photon lithography, this freeform SOL paradigm paves the way for next-generation, high-performance integrated photonics.

physics.optics↗