arXiv subjects

Dun Wang

Publications and source records attributed to Dun Wang.

8 recordsLinked to original sources

arXiv2026

Geometric Engineering of Flat Bands in a Single-layer Photonic Graphene

Photonic flat bands offer significant potential for strong light-matter interactions, nonlinear optics, and sensing thanks to their localization of light and high density of states. However, realizing these flat bands typically requires intricate fabrication, perfect alignment and/or specialized geometries, and a general design strategy is missing. In this work, we demonstrate a simple yet versatile strategy to engineer radiative flat bands above the light line, using only a single-layer honeycomb photonic crystal slab. By applying a density wave like geometric perturbation-a spatially periodic displacement of the lattice air holes-we couple intrinsic flat band states from below the light cone into the radiative continuum. This structural modulation creates a highly anisotropic band structure that exhibits linear, Dirac-like dispersion in one direction and nearly flat dispersion in the orthogonal direction, forming an extended van Hove singularity at band extrema. Furthermore, by tuning the Fourier components of the modulation, we can manipulate the Dirac mass term to realize band inversion and switch between two topologically distinct phases. As an application, we demonstrate a Jackiw-Rebbi interface state positioned at the junction of two domains with opposite Dirac mass, that also shows flat band dispersion along the interface. This density-wave perturbation approach provides a conceptually clear and fabrication friendly platform for programming complex photonic band dispersions, opening new avenues for both topological photonics and practical flat-band optoelectronic devices.

physics.optics↗

arXiv2025

General Framework for Twisted Bilayer Photonic Crystal with Interlayer Coupling and Far-Field Response

We develop a general theory for twisted bilayer photonic crystals that takes into account both far-field response and near-field coupling. The theory is based on the framework of a generalized Rayleigh-Schr\"odinger perturbation theory for non-Hermitian Hamiltonians. A universal form for interlayer coupling is derived, which relates the hopping strength to the Fourier transforms of the Wannier functions in the single layer photonic crystal. For low energy states at the K point in hexagonal lattices, the interlayer coupling reduces to that in the Bistritzer-MacDonald model for graphene. As an example, we study a twisted bilayer photonic crystal slab with air holes arranged in a honeycomb lattice in each layer. The first order solution of our model predicts a four-fold band splitting in the far-field spectrum compared to the single-layer case, which is confirmed by numerical simulations. Moreover, our theory reveals that for low energy states at K points, scattering towards the {\Gamma} point via the moir\'e potential is suppressed. Based on our theory, we propose a wide-angle, high-Q tunable flat band cavity by combining the bilayer at a large twist angle with a Brillouinzone-folding perturbation within each layer. The cavity behaves like a collection of quasi-bound states in the continuum with a divergent density of states, with potential applications in nonlinear optics, lasing and quantum optics.

physics.optics↗

arXiv2022

Computer-aided Recognition and Assessment of a Porous Bioelastomer on Ultrasound Images for Regenerative Medicine Applications

Biodegradable elastic scaffolds have attracted more and more attention in the field of soft tissue repair and tissue engineering. These scaffolds made of porous bioelastomers support tissue ingrowth along with their own degradation. It is necessary to develop a computer-aided analyzing method based on ultrasound images to identify the degradation performance of the scaffold, not only to obviate the need to do destructive testing, but also to monitor the scaffold's degradation and tissue ingrowth over time. It is difficult using a single traditional image processing algorithm to extract continuous and accurate contour of a porous bioelastomer. This paper proposes a joint algorithm for the bioelastomer's contour detection and a texture feature extraction method for monitoring the degradation behavior of the bioelastomer. Mean-shift clustering method is used to obtain the bioelastomer's and native tissue's clustering feature information. Then the OTSU image binarization method automatically selects the optimal threshold value to convert the grayscale ultrasound image into a binary image. The Canny edge detector is used to extract the complete bioelastomer's contour. The first-order and second-order statistical features of texture are extracted. The proposed joint algorithm not only achieves the ideal extraction of the bioelastomer's contours in ultrasound images, but also gives valuable feedback of the degradation behavior of the bioelastomer at the implant site based on the changes of texture characteristics and contour area. The preliminary results of this study suggest that the proposed computer-aided image processing techniques have values and potentials in the non-invasive analysis of tissue scaffolds in vivo based on ultrasound images and may help tissue engineers evaluate the tissue scaffold's degradation and cellular ingrowth progress and improve the scaffold designs.

eess.IV↗

arXiv2018

An Ultraviolet-Optical Color-Metallicity relation for Red Clump Stars using GALEX and Gaia

Although core helium-burning red clump (RC) stars are faint at ultraviolet wavelengths, their ultraviolet-optical color is a unique and accessible probe of their physical properties. Using data from the GALEX All Sky Imaging Survey, Gaia Data Release 2 and the SDSS APOGEE DR14 survey, we find that spectroscopic metallicity is strongly correlated with the location of an RC star in the UV-optical color magnitude diagram. The RC has a wide spread in (NUV - G)$_0$ color, over 4 magnitudes, compared to a 0.7-magnitude range in (G$_{BP}$ - G$_{RP}$)$_0$. We propose a photometric, dust-corrected, ultraviolet-optical (NUV - G)$_0$ color-metallicity [Fe/H] relation using a sample of 5,175 RC stars from APOGEE. We show that this relation has a scatter of 0.28 dex and is easier to obtain for large, wide-field samples than spectroscopic metallicities. Importantly, the effect may be comparable to the spread in RC color attributed to extinction in other studies.

astro-ph.SR↗

arXiv2017

A pixel-level model for event discovery in time-domain imaging

Difference imaging or image subtraction is a method that measures differential photometry by matching the pointing and point-spread function (PSF) between image frames. It is used for the detection of time-variable phenomena. Here we present a new category of method---CPM Difference Imaging, in which differences are not measured between matched images but instead between image frames and a data-driven predictive model that has been designed only to predict the pointing, PSF, and detector effects but not astronomical variability. In CPM Difference Imaging each pixel is modelled by the Causal Pixel Model (CPM) originally built for modeling Kepler data, in which pixel values are predicted by a linear combination of other pixels at the same epoch but far enough away such that these pixels are causally disconnected, astrophysically. It does not require that the user have any explicit model or description of the pointing or point-spread function of any of the images. Its principal drawback is that---in its current form---it requires an imaging campaign with many epochs and fairly stable telescope pointing. The method is applied to simulated data and also the K2 Campaign 9 microlensing data. We show that CPM Difference Imaging can detect variable objects and produce precise differentiate photometry in a crowded field. CPM Difference Imaging is capable of producing image differences at nearly photon-noise precision.

astro-ph.IM↗

arXiv2015

A Causal, Data-Driven Approach to Modeling the Kepler Data

Astronomical observations are affected by several kinds of noise, each with its own causal source; there is photon noise, stochastic source variability, and residuals coming from imperfect calibration of the detector or telescope. The precision of NASA Kepler photometry for exoplanet science---the most precise photometric measurements of stars ever made---appears to be limited by unknown or untracked variations in spacecraft pointing and temperature, and unmodeled stellar variability. Here we present the Causal Pixel Model (CPM) for Kepler data, a data-driven model intended to capture variability but preserve transit signals. The CPM works at the pixel level so that it can capture very fine-grained information about the variation of the spacecraft. The CPM predicts each target pixel value from a large number of pixels of other stars sharing the instrument variabilities while not containing any information on possible transits in the target star. In addition, we use the target star's future and past (auto-regression). By appropriately separating, for each data point, the data into training and test sets, we ensure that information about any transit will be perfectly isolated from the model. The method has four hyper-parameters (the number of predictor stars, the auto-regressive window size, and two L2-regularization amplitudes for model components), which we set by cross-validation. We determine a generic set of hyper-parameters that works well for most of the stars and apply the method to a corresponding set of target stars. We find that we can consistently outperform (for the purposes of exoplanet detection) the Kepler Pre-search Data Conditioning (PDC) method for exoplanet discovery.

astro-ph.EP↗

arXiv2015

Removing systematic errors for exoplanet search via latent causes

We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest. The method, referred to as half-sibling regression, is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification and illustrate the potential of the method in a challenging astronomy application.

stat.ML↗

arXiv2015

A systematic search for transiting planets in the K2 data

Photometry of stars from the K2 extension of NASA's Kepler mission is afflicted by systematic effects caused by small (few-pixel) drifts in the telescope pointing and other spacecraft issues. We present a method for searching K2 light curves for evidence of exoplanets by simultaneously fitting for these systematics and the transit signals of interest. This method is more computationally expensive than standard search algorithms but we demonstrate that it can be efficiently implemented and used to discover transit signals. We apply this method to the full Campaign 1 dataset and report a list of 36 planet candidates transiting 31 stars, along with an analysis of the pipeline performance and detection efficiency based on artificial signal injections and recoveries. For all planet candidates, we present posterior distributions on the properties of each system based strictly on the transit observables.

astro-ph.EP↗