arXiv subjects

Tingting Liu

Publications and source records attributed to Tingting Liu.

At least 37 records · Page 2Linked to original sources

arXiv2026

Wavefront Control and Intensity Modulation of Third Harmonic Generation in Nonlocal Metasurfaces

Metasurfaces have emerged as a promising platform for integrated nonlinear optics. Nonlocal metasurfaces enable high nonlinear conversion efficiency, while the local ones can offer versatile wavefront control, yet achieving both within a single metasurface remains challenging. Here, using a nonlocal phase gradient metasurface, we firstly demonstrate efficient third harmonic generation (THG) with polarization-dependent wavefront control. Leveraging nonlocal nonlinear geometric phase existing at resonance, the third harmonic light with distinct polarizations is deflected into $\pm$ 2nd and $\pm$ 4th diffraction orders, simultaneously achieving a conversion efficiency up to $1.45\times 10^{-4}$ under a pump intensity of $1 GW/cm^{2}$. Then, by introducing a secondary fundamental beam, whose generated third harmonic light overlaps with that of the first beam, the intensity modulation of THG is obtained. The THG efficiency can be tuned from $3.9 \times 10^{-9}$ to $5.5 \times 10^{-3}$ by varying the relative phase, polarization and intensity of two fundamental beams. Through utilizing the advantages of both local and nonlocal metasurfaces, our results effectively pave the way to on-chip nonlinear photonic devices and signal processing.

physics.optics↗

arXiv2026

The NANOGrav 15 yr Data Set: Piecewise Power-Law Reconstruction of the Gravitational-Wave Background

The NANOGrav 15-year (NG15) data set provides evidence for a gravitational-wave background (GWB) signal at nanohertz frequencies, which is expected to originate either from a cosmic population of inspiraling supermassive black-hole binaries or new particle physics in the early Universe. A firm identification of the source of the NG15 signal requires an accurate reconstruction of its frequency spectrum. In this paper, we provide such a spectral characterization of the NG15 signal based on a piecewise power-law (PPL) ansatz that strikes a balance between existing alternatives in the literature. Our PPL reconstruction is more flexible than the standard constant-power-law model, which describes the GWB spectrum in terms of only two parameters: an amplitude A and a spectral index gamma. Concurrently, it better approximates physically realistic GWB spectra -- especially those of cosmological origin -- than the free spectral model, since the latter allows for arbitrary variations in the GWB amplitude from one frequency bin to the next. Our PPL reconstruction of the NG15 signal relies on individual PPL models with a fixed number of internal nodes (i.e., constant power law, broken power law, doubly broken power law, etc.) that are ultimately combined in a Bayesian model average. The data products resulting from our analysis provide the basis for fast refits of spectral GWB models.

astro-ph.HE↗

arXiv2026

The NANOGrav 15 yr Data Set: Targeted Searches for Supermassive Black Hole Binaries

We present the first targeted searches for continuous gravitational waves (CWs) from 114 active galactic nuclei (AGN) that may host supermassive black hole binaries, using the NANOGrav 15 yr data set. By incorporating electromagnetic priors on sky location, distance, redshift, and CW frequency, our strain and chirp mass upper limits are typically improved by a factor of $\sim 2$ (median 2.2) relative to all-sky limits at the same frequency. Bayesian comparisons against a model including only a Hellings-Downs correlated background disfavors a CW signal for all targets, with a mean Bayes factor of $0.73 \pm 0.32$. Two targets have Bayes factors slightly above unity, but coherence tests, random targeting experiments, and a conservative accounting of the 114-target trials factor all indicate that they are consistent with noise. We use these two candidates as worked examples to illustrate an end-to-end targeted CW search analysis and a suite of follow up tests that future promising candidates would need to pass. We find that the electromagnetic interpretations of both candidates are ambiguous, and we update the constraints on a putative binary in 3C 66B, ruling out part of its previously allowed parameter space. Ultimately, our results demonstrate the current sensitivity of targeted pulsar timing array searches for CWs and define a roadmap for future multimessenger CW detections.

astro-ph.HE↗

arXiv2025

Inferring Mbh-Mbulge Evolution from the Gravitational Wave Background

We test the impact of an evolving supermassive black hole (SMBH) mass scaling relation (Mbh-Mbulge) on the predictions for the gravitational wave background (GWB). The observed GWB amplitude is 2-3 times higher than predicted by astrophysically informed models which suggests the need to revise the assumptions in those models. We compare a semi-analytic model's ability to reproduce the observed GWB spectrum with a static versus evolving-amplitude Mbh-Mbulge relation. We additionally consider the influence of the choice of galaxy stellar mass function on the modeled GWB spectra. Our models are able to reproduce the GWB amplitude with either a large number density of massive galaxies or a positively evolving Mbh-Mbulge amplitude (i.e., the Mbh / Mbulge ratio was higher in the past). If we assume that the Mbh-Mbulge amplitude does not evolve, our models require a galaxy stellar mass function that implies an undetected population of massive galaxies (Mstellar > 10^11 Msun at z > 1). When the Mbh-Mbulge amplitude is allowed to evolve, we can model the GWB spectrum with all fiducial values and an Mbh-Mbulge amplitude that evolves as alpha(z) = alpha_0 (1 + z)^(1.04 +/- 0.5).

astro-ph.HE↗

arXiv2025

Metasurface-based all-optical diffractive convolutional neural networks

The escalating energy demands and parallel-processing bottlenecks of electronic neural networks underscore the need for alternative computing paradigms. Optical neural networks, capitalizing on the inherent parallelism and speed of light propagation, present a compelling solution. Nevertheless, physically realizing convolutional neural network (CNN) components all-optically remains a significant challenge. To this end, we propose a metasurface-based all-optical diffractive convolutional neural network (MAODCNN) for computer vision tasks. This architecture synergistically integrates metasurface-based optical convolutional layers, which perform parallel convolution on the optical field, with cascaded diffractive neural networks acting as all-optical decoders. This co-design facilitates layer-wise feature extraction and optimization directly within the optical domain. Numerical simulations confirm that the fusion of convolutional and diffractive layers markedly enhances classification accuracy, a performance that scales with the number of diffractive layers. The MAODCNN framework establishes a viable foundation for practical all-optical CNNs, paving the way for high-efficiency, low-power optical computing in advanced pattern recognition.

physics.optics↗

arXiv2025

Joint Optimization of Pilot Length, Pilot Assignment, and Power Allocation for Cell-free MIMO Systems with Graph Neural Networks

In user-centric cell-free multi-antenna systems, pilot contamination degrades spectral efficiency (SE) severely. To mitigate pilot contamination, existing works jointly optimize pilot assignment and power allocation by assuming fixed pilot length, which fail to balance pilot overhead against the contamination. To maximize net-SE, we jointly optimize pilot length, pilot assignment, and power allocation with deep learning. Since the pilot length is a variable, the size of pilot assignment matrix is unknown during the optimization. To cope with the challenge, we design size-generalizable graph neural networks (GNNs). We prove that pilot assignment policy is a one-to-many mapping, and improperly designed GNNs cannot learn the optimal policy. We tackle this issue by introducing feature enhancement. To improve learning performance, we design a contamination-aware attention mechanism for the GNNs. Given that pilot assignment and power allocation respectively depend on large- and small-scale channels, we develop a dual-timescale GNN framework to explore the potential. To reduce inference time, a single-timescale GNN is also designed. Simulation results show that the designed GNNs outperform existing methods in terms of net-SE, training complexity, and inference time, and can be well generalized across problem scales and channels.

eess.SP↗

arXiv2025

Four-channel Imaging Based on Reconfigurable Metasurfaces: Hyperchaotic Encryption under Physical Protection

Metasurfaces facilitate high-capacity optical information integration by simultaneously supporting near-field nanoprinting and far-field holography on a single platform. However, conventional multi-channel designs face critical security vulnerabilities for sensitive information due to insufficient encryption mechanisms. In this work, we propose a four-channel phase-change metasurface featuring algorithm-physical co-security-a dual-protection framework combining intrinsic metasurface physical security with chaotic encryption. Our polarization-multiplexed metasurface generates four optical imaging channels through meta-atom design, including two far-field holograms and two near-field patterns. To enhance system security, we apply Chen hyperchaotic encryption combined with the Logistic map and DNA encoding to convert near-field information into secure QR codes; far-field holograms are retained to demonstrate the metasurface's information capacity and for attack detection. Phase-change metasurface further provides physical-layer security by dynamically switching imaging channels via crystalline-to-amorphous state transitions, enhancing anti-counterfeiting and reliability. The proposed metasurface achieves high-fidelity imaging, robust anti-attack performance, and independent channel control. This integrated approach pioneers a secure paradigm for high-density optical information processing.

physics.optics↗

arXiv2025

Continuum Reverberation Mapping of Accretion Disks Surrounding Supermassive Black Hole Binaries: Observational Signatures

It has remained challenging to reliably identify sub-parsec supermassive black hole binaries (SMBHBs), despite them being expected to be ubiquitous. We propose a new method using multi-band continuum reverberation mapping to identify low-mass-ratio SMBHBs in active galactic nuclei. The basic principle is that, due to the presence of a low-density cavity between the mini-disks and the circumbinary disk, the continuum emissions show a deficit at certain wavelengths, leading to a distinguishing feature in the relation between the inter-band time lag and wavelengths $τ(λ)$. Specifically, the relation appears flat at short wavelengths because of the truncated sizes of the mini-disks and transits to a power law $λ^{4/3}$ at long wavelength stemming from the circumbinary disk. This transition feature is distinct from the uniform relation $λ^{4/3}$ of the standard accretion disk around a single black hole. Using the lamp-post scenario and assuming that only the secondary black hole is active in a low-mass-ratio SMBHB, we design a simple continuum reverberation model to calculate the transfer function of the accretion disks and the resulting $τ(λ)$ relations for various SMBHB orbital parameters. The transition wavelength typically can lie at UV/optical bands, mainly depending on the total mass and orbital separation of the SMBHB. We apply our SMBHB model to the intensive multiwavelength monitoring data of the SMBHB candidate PG1302-102 and find that the SMBHB model can reproduce the inter-band time lags. Remarkably, the inferred total mass and orbital period from the SMBHB fitting are consistent with values derived from other independent methods.

astro-ph.GA↗

arXiv2025

High-efficiency infrared upconversion imaging with nonlinear silicon metasurfaces empowered by quasi-bound states in the continuum

Infrared imaging is indispensable for its ability to penetrate obscurants and visualize thermal signatures, yet its practical use is hindered by the intrinsic limitations of conventional detectors. Nonlinear upconversion, which converts infrared light into the visible band, offers a promising pathway to address these challenges. Here, we demonstrate high-efficiency infrared upconversion imaging using nonlinear silicon metasurfaces. By strategically breaking in-plane symmetry, the metasurface supports a high-$Q$ quasi-bound states in the continuum resonance, leading to strongly enhanced third-harmonic generation (THG) with a conversion efficiency of $3\times10^{-5}$ at a pump intensity of 10 GW/cm$^{2}$. Through this THG process, the metasurface enables high-fidelity upconversion of arbitrary infrared images into the visible range, achieving a spatial resolution of $\sim 6$ $\upmu$m as verified using a resolution target and various customized patterns. This work establishes a robust platform for efficient nonlinear conversion and imaging, highlighting the potential of CMOS-compatible silicon metasurfaces for high-performance infrared sensing applications with reduced system complexity.

physics.optics↗

arXiv2025

Tunable edge and depth sensing via phase-change nonlocal metasurfaces

Performing simultaneous depth-of-field (DoF) extension and edge enhancement within a single optical element remains a fundamental challenge in advanced imaging. Here, we propose a wavelength-tunable nonlocal Huygens' metasurface capable of simultaneously extracting depth and edge features of images in a single-shot exposure. Using the selective polarization response of the Huygens' metasurfaces, the circularly polarized converted component undergoes geometric phase modulation for wavefront shaping to extend the DoF, while the non-converted component acts as a spatial frequency filter to enhance edge contrast. The integration of a phase-change material, Sb$_{2}$S$_{3}$, enables continuous tuning of the resonance wavelength across a range of 100 nm by modulating its refractive index, granting the system excellent broadband spectral adaptability. This work offers a novel and compact solution for real-time depth sensing and feature extraction in applications such as autonomous navigation and biomedical imaging.

physics.optics↗

arXiv2025

Tunable spin-wave nonreciprocity in ferrimagnetic domain-wall channels

The nonreciprocal propagation of spin waves (SWs) offers opportunities for developing novel functional magnonic logic devices, where controllability is crucial for magnetic signal processing. Domain walls act as natural waveguides due to their magnetic configuration, offering a platform for the in-depth investigation of nonreciprocal SW propagation and its manipulation. In this work, we theoretically and numerically investigate the tunable spin-wave nonreciprocity in ferrimagnetic domain-wall channels under the influence of an external field. It is revealed that the Dzyaloshinskii-Moriya interaction exerts dual control over both nonreciprocal spin-wave propagation and spin-splitting phenomena. Moreover, SW nonreciprocity is magnetically tunable, with its sign reversibly switched by inverting the applied field direction, while preserving the host spin configuration. The orientation of the magnetic field can selectively stabilize or destabilize the domain wall structure, offering precise control over spin-wave nonreciprocity. Ultimately, we demonstrate a controllable SW transmission scheme via external magnetic field modulation, providing critical insights for the design of future magnonic devices.

cond-mat.mtrl-sci↗

arXiv2025

Integrated Spectropolarimeter by Metasurface-Based Diffractive Optical Networks

Conventional spectrometer and polarimeter systems rely on bulky optics, fundamentally limiting compact integration and hindering multi-dimensional optical sensing capabilities. Here, we propose a spectropolarimeter enabled by metasurface-based diffractive optical networks that simultaneously performs spectrometric and polarimetric measurements in a compact device. By leveraging the wavelength- and polarization-dependent phase modulation of metasurfaces, our system encodes the spectral and polarization information of incident light into spatially resolved intensity distributions, which are subsequently decoded by a trained deep neural network, enabling simultaneous high-accuracy reconstruction of both spectral compositions and Stokes parameters through a single-shot measurement. Experiments validate the proposed network's accurate reconstruction of the spectral and polarization information across a broad wavelength range, and further confirm its imaging capability. Notably, we demonstrate a chip-integrated sensor prototype combing both measurement functionalities into a commercial CMOS image sensor. This integrated platform provides a compact solution for on-chip multi-dimensional optical sensing, holding significant potential for versatile sensing, biomedical diagnosis, and industrial metrology.

physics.optics↗

arXiv2025

Quasi-Bound States in the Continuum-Induced Second Harmonic Generation Enhancement in High-$Q$ Triple Dielectric Nanoresonators

High-$Q$ optical nanocavities are fundamental to modern optics and photonics, enabling enhanced light-matter interactions. Previous studies have demonstrated that high-$Q$ supercavity modes can be constructed within a single dielectric resonator by leveraging quasi-bound states in the continuum. However, their $Q$-factors are limited to few tens or hundreds when such a resonator is subwavelength scale. Here, we propose a general recipe for achieving high-$Q$ resonances with $Q>10,000$ in triple subwavelength dielectric resonators. This is realized through destructive interference between two resonant modes, optimized by structural tuning. Multipole analysis confirms that destructive interference across radiation channels suppresses loss, forming the ultrahigh-$Q$ states. These resonances can be efficiently excited by azimuthally polarized light due to improved mode overlap. As a key application, we demonstrate efficient second harmonic generation under this excitation, achieving a conversion efficiency of $6.6\%$ at an incident intensity of 0.78 GW/cm$^2$. Our results may find exciting applications in developing ultracompact photonic devices with superior performance.

physics.optics↗

arXiv2025

MTSIC: Multi-stage Transformer-based GAN for Spectral Infrared Image Colorization

Thermal infrared (TIR) images, acquired through thermal radiation imaging, are unaffected by variations in lighting conditions and atmospheric haze. However, TIR images inherently lack color and texture information, limiting downstream tasks and potentially causing visual fatigue. Existing colorization methods primarily rely on single-band images with limited spectral information and insufficient feature extraction capabilities, which often result in image distortion and semantic ambiguity. In contrast, multiband infrared imagery provides richer spectral data, facilitating the preservation of finer details and enhancing semantic accuracy. In this paper, we propose a generative adversarial network (GAN)-based framework designed to integrate spectral information to enhance the colorization of infrared images. The framework employs a multi-stage spectral self-attention Transformer network (MTSIC) as the generator. Each spectral feature is treated as a token for self-attention computation, and a multi-head self-attention mechanism forms a spatial-spectral attention residual block (SARB), achieving multi-band feature mapping and reducing semantic confusion. Multiple SARB units are integrated into a Transformer-based single-stage network (STformer), which uses a U-shaped architecture to extract contextual information, combined with multi-scale wavelet blocks (MSWB) to align semantic information in the spatial-frequency dual domain. Multiple STformer modules are cascaded to form MTSIC, progressively optimizing the reconstruction quality. Experimental results demonstrate that the proposed method significantly outperforms traditional techniques and effectively enhances the visual quality of infrared images.

eess.IV↗

arXiv2025

The NANOGrav 15 Yr Data Set: Removing Pulsars One by One from the Pulsar Timing Array

Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15-year pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese PTAs. This signal has been interpreted as indicative of the presence of a nanohertz stochastic gravitational wave background. To explore the internal consistency of this result we investigate how the recovered signal strength changes as we remove the pulsars one by one from the data set. We calculate the signal strength using the (noise-marginalized) optimal statistic, a frequentist metric designed to measure correlated excess power in the residuals of the arrival times of the radio pulses. We identify several features emerging from this analysis that were initially unexpected. The significance of these features, however, can only be assessed by comparing the real data to synthetic data sets. After conducting identical analyses on simulated data sets, we do not find anything inconsistent with the presence of a stochastic gravitational wave background in the NANOGrav 15-year data. The methodologies developed here can offer additional tools for application to future, more sensitive data sets. While this analysis provides an internal consistency check of the NANOGrav results, it does not eliminate the necessity for additional investigations that could identify potential systematics or uncover unmodeled physical phenomena in the data.

astro-ph.HE↗

arXiv2025

MAGI-1: Autoregressive Video Generation at Scale

We present MAGI-1, a world model that generates videos by autoregressively predicting a sequence of video chunks, defined as fixed-length segments of consecutive frames. Trained to denoise per-chunk noise that increases monotonically over time, MAGI-1 enables causal temporal modeling and naturally supports streaming generation. It achieves strong performance on image-to-video (I2V) tasks conditioned on text instructions, providing high temporal consistency and scalability, which are made possible by several algorithmic innovations and a dedicated infrastructure stack. MAGI-1 facilitates controllable generation via chunk-wise prompting and supports real-time, memory-efficient deployment by maintaining constant peak inference cost, regardless of video length. The largest variant of MAGI-1 comprises 24 billion parameters and supports context lengths of up to 4 million tokens, demonstrating the scalability and robustness of our approach. The code and models are available at https://github.com/SandAI-org/MAGI-1 and https://github.com/SandAI-org/MagiAttention. The product can be accessed at https://sand.ai.

cs.CV↗

arXiv2025

The NANOGrav 15-year Gravitational-Wave Background Methods

Pulsar timing arrays (PTAs) use an array of millisecond pulsars to search for gravitational waves in the nanohertz regime in pulse time of arrival data. This paper presents rigorous tests of PTA methods, examining their consistency across the relevant parameter space. We discuss updates to the 15-year isotropic gravitational-wave background analyses and their corresponding code representations. Descriptions of the internal structure of the flagship algorithms Enterprise and PTMCMCSampler are given to facilitate understanding of the PTA likelihood structure, how models are built, and what methods are currently used in sampling the high-dimensional PTA parameter space. We introduce a novel version of the PTA likelihood that uses a two-step marginalization procedure that performs much faster in gravitational wave searches, reducing the required resources facilitating the computation of Bayes factors via thermodynamic integration and sampling a large number of realizations for computing Bayesian false-alarm probabilities. We perform stringent tests of consistency and correctness of the Bayesian and frequentist analysis methods. For the Bayesian analysis, we test prior recovery, simulation recovery, and Bayes factors. For the frequentist analysis, we test that the optimal statistic, when modified to account for a non-negligible gravitational-wave background, accurately recovers the amplitude of the background. We also summarize recent advances and tests performed on the optimal statistic in the literature from both GWB detection and parameter estimation perspectives. The tests presented here validate current analyses of PTA data.

astro-ph.HE↗

arXiv2025

Electro-optically tunable second-harmonic generation in lithium niobate metasurfaces boosted by Brillouin zone folding induced bound states in the continuum

Lithium niobate (LN) metasurfaces exhibit remarkable Pockels effect-driven electro-optic tunability, enabling dynamic control of optical responses through external electric fields. When combined with their high second-order nonlinear susceptibility ($χ^{2}$), this tunability is projected into the nonlinear landscape, realizing second-harmonic generation (SHG)-dominated functionalities in integrated photonics. However, achieving deep SHG modulation in LN metasurfaces remains challenging due to LN's limited refractive index tunability under practical driving voltage. To address this, we design an air-hole-structured LN metasurface by strategically adjusting air hole positions to induce Brillouin zone folding-enabled bound states in the continuum with ultrahigh quality factors ($Q>10^{4}$). Numerical simulations demonstrate a $2.59\%$ SHG conversion efficiency at 0.1 MW/cm$^{2}$ excitation and a modulation depth exceeding 0.99 under 15 V peak-to-peak voltage ($ΔV_{\text{pp}}$). This work establishes a compact framework for electrically tunable nonlinear optics, advancing applications in integrated quantum light sources and programmable photonic chips.

physics.optics↗