Data analysis of gravitational-wave signals from spinning neutron stars. V. A narrow-band all-sky search

We present theory and algorithms to perform an all-sky coherent search for periodic signals of gravitational waves in narrow-band data of a detector. Our search is based on a statistic, commonly called the $\mathcal{F}$-statistic, derived from the maximum-likelihood principle in Paper I of this series. We briefly review the response of a ground-based detector to the gravitational-wave signal from a rotating neuron star and the derivation of the $\mathcal{F}$-statistic. We present several algorithms to calculate efficiently this statistic. In particular our algorithms are such that one can take advantage of the speed of fast Fourier transform (FFT) in calculation of the $\mathcal{F}$-statistic. We construct a grid in the parameter space such that the nodes of the grid coincide with the Fourier frequencies. We present interpolation methods that approximately convert the two integrals in the $\mathcal{F}$-statistic into Fourier transforms so that the FFT algorithm can be applied in their evaluation. We have implemented our methods and algorithms into computer codes and we present results of the Monte Carlo simulations performed to test these codes.Comment: REVTeX, 20 pages, 8 figure

Probing Convolutional Neural Networks for Event Reconstruction in {\gamma}-Ray Astronomy with Cherenkov Telescopes

A dramatic progress in the field of computer vision has been made in recent years by applying deep learning techniques. State-of-the-art performance in image recognition is thereby reached with Convolutional Neural Networks (CNNs). CNNs are a powerful class of artificial neural networks, characterized by requiring fewer connections and free parameters than traditional neural networks and exploiting spatial symmetries in the input data. Moreover, CNNs have the ability to automatically extract general characteristic features from data sets and create abstract data representations which can perform very robust predictions. This suggests that experiments using Cherenkov telescopes could harness these powerful machine learning algorithms to improve the analysis of particle-induced air-showers, where the properties of primary shower particles are reconstructed from shower images recorded by the telescopes. In this work, we present initial results of a CNN-based analysis for background rejection and shower reconstruction, utilizing simulation data from the H.E.S.S. experiment. We concentrate on supervised training methods and outline the influence of image sampling on the performance of the CNN-model predictions.Comment: 8 pages, 4 figures, Proceedings of the 35th International Cosmic Ray Conference (ICRC 2017), Busan, Kore

Scaling Dynamics of Domain Walls in the Cubic Anisotropy Model

We have investigated the dynamics of domain walls in the cubic anisotropy model. In this model a global O(N) symmetry is broken to a set of discrete vacua either on the faces, or vertices of a (hyper)cube. We compute the scaling exponents for $2\le N\le 7$ in two dimensions on grids of $2048^2$ points and compare them to the fiducial model of $Z_2$ symmetry breaking. Since the model allows for wall junctions lattice structures are locally stable and modifications to the standard scaling law are possible. However, we find that since there is no scale which sets the distance between walls, the walls appear to evolve toward a self-similar regime with $L\sim t$.Comment: 16 pages, 12 figure

Bi-cubic interpolation for image conversion from virtual hexagonal to square structure

Hexagonal image structure represents an image as a collection of hexagonal pixels rather than square pixels in the traditional image structure. However, all the existing hardware for capturing image and for displaying image are produced based on square pixel image structure. Therefore, it becomes important to find a proper software approach to mimic the hexagonal structure so that images represented on the traditional square structure can be smoothly converted from or to the images on the hexagonal structure. For accurate image processing, it is critical to best maintain the image resolution during the image conversion. In this paper, we present an algorithm for bi-cubic interpolation of pixel values on a hexagonal structure when convert from the hexagonal structure to the square structure. We will compare with the results obtained through bi-linear interpolation for the conversion. Our experimental results show that the bi-cubic interpolation outperforms the bi-linear interpolation for most of testing images at the cost of slower and more complex computation

Bilinear interpolation on a virtual hexagonal structure

Spiral Architecture (SA) is a relatively new and powerful approach to machine vision system. The geometrical arrangement of pixels on SA can be described as a collection of hexagonal pixels. However, all the existing hardware for capturing image and for displaying image are produced based on rectangular architecture. Therefore, it becomes important to find a proper software approach to mimic SA so that images represented on the traditional square structure can be smoothly converted from or to the images on SA. For accurate image processing, it is critical to best maintain the image resolution during the image conversion. In this paper, we present an algorithm for bilinear interpolation of pixel values on a simulated SA. Our experimental results show that the bilinear interpolation improves the image representation accuracy while keeping the computation simple

Self-Assembled Chiral Photonic Crystals From Colloidal Helices Racemate

Chiral crystals consisting of micro-helices have many optical properties while presently available fabrication processes limit their large-scale applications in photonic devices. Here, by using a simplified simulation method, we investigate a bottom-up self-assembly route to build up helical crystals from the smectic monolayer of colloidal helices racemate. With increasing the density, the system undergoes an entropy-driven co-crystallization by forming crystals of various symmetries with different helical shapes. In particular, we identify two crystals of helices arranged in the binary honeycomb and square lattices, which are essentially composed by two sets of opposite-handed chiral crystal. Photonic calculations show that these chiral structures can have large complete photonic bandgaps. In addition, in the self-assembled chiral square crystal, we also find dual polarization bandgaps that selectively forbid the propagation of circularly polarized lights of a specific handedness along the helical axis direction. The self-assembly process in our proposed system is robust, suggesting possibilities of using chiral colloids to assemble photonic metamaterials.Comment: Accepted in ACS Nan