Adaptive binning of X-ray galaxy cluster images

We present a simple method for adaptively binning the pixels in an image. The algorithm groups pixels into bins of size such that the fractional error on the photon count in a bin is less than or equal to a threshold value, and the size of the bin is as small as possible. The process is particularly useful for generating surface brightness and colour maps, with clearly defined error maps, from images with a large dynamic range of counts, for example X-ray images of galaxy clusters. We demonstrate the method in application to data from Chandra ACIS-S and ACIS-I observations of the Perseus cluster of galaxies. We use the algorithm to create intensity maps, and colour images which show the relative X-ray intensities in different bands. The colour maps can later be converted, through spectral models, into maps of physical parameters, such as temperature, column density, etc. The adaptive binning algorithm is applicable to a wide range of data, from observations or numerical simulations, and is not limited to two-dimensional data.Comment: 8 pages, 12 figures, accepted by MNRAS (includes changes suggested by referee), high resolution version at http://www-xray.ast.cam.ac.uk/~jss/adbin

Strategy for reliable strain measurement in InAs/GaAs materials from high-resolution Z-contrast STEM images

Geometric phase analysis (GPA), a fast and simple Fourier space method for strain analysis, can give useful information on accumulated strain and defect propagation in multiple layers of semiconductors, including quantum dot materials. In this work, GPA has been applied to high resolution Z-contrast scanning transmission electron microscopy (STEM) images. Strain maps determined from different g vectors of these images are compared to each other, in order to analyze and assess the GPA technique in terms of accuracy. The SmartAlign tool has been used to improve the STEM image quality getting more reliable results. Strain maps from template matching as a real space approach are compared with strain maps from GPA, and it is discussed that a real space analysis is a better approach than GPA for aberration corrected STEM images

Ultraviolet Imaging Polarimetry of the Large Magellanic Cloud. II. Models

Motivated by new sounding-rocket wide-field polarimetric images of the Large Magellanic Cloud, we have used a three-dimensional Monte Carlo radiation transfer code to investigate the escape of near-ultraviolet photons from young stellar associations embedded within a disk of dusty material (i.e. a galaxy). As photons propagate through the disk, they may be scattered or absorbed by dust. Scattered photons are polarized and tracked until they escape to be observed; absorbed photons heat the dust, which radiates isotropically in the far-infrared, where the galaxy is optically thin. The code produces four output images: near- UV and far-IR flux, and near-UV images in the linear Stokes parameters Q and U. From these images we construct simulated UV polarization maps of the LMC. We use these maps to place constraints on the star + dust geometry of the LMC and the optical properties of its dust grains. By tuning the model input parameters to produce maps that match the observed polarization maps, we derive information about the inclination of the LMC disk to the plane of the sky, and about the scattering phase function g. We compute a grid of models with i = 28 deg., 36 deg., and 45 deg., and g = 0.64, 0.70, 0.77, 0.83, and 0.90. The model which best reproduces the observed polarization maps has i = 36 +2/-5 degrees and g ~0.7. Because of the low signal-to-noise in the data, we cannot place firm constraints on the value of g. The highly inclined models do not match the observed centro-symmetric polarization patterns around bright OB associations, or the distribution of polarization values. Our models approximately reproduce the observed ultraviolet photopolarimetry of the western side of the LMC; however, the output images depend on many input parameters and are nonunique.Comment: Accepted to AJ. 20 pages, 7 figure

Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps

Studies of land cover dynamics would benefit greatly from the generation of land cover maps at both fine spatial and temporal resolutions. Fine spatial resolution images are usually acquired relatively infrequently, whereas coarse spatial resolution images may be acquired with a high repetition rate but may not capture the spatial detail of the land cover mosaic of the region of interest. Traditional image spatial–temporal fusion methods focus on the blending of pixel spectra reflectance values and do not directly provide land cover maps or information on land cover dynamics. In this research, a novel Spatial–Temporal remotely sensed Images and land cover Maps Fusion Model (STIMFM) is proposed to produce land cover maps at both fine spatial and temporal resolutions using a series of coarse spatial resolution images together with a few fine spatial resolution land cover maps that pre- and post-date the series of coarse spatial resolution images. STIMFM integrates both the spatial and temporal dependences of fine spatial resolution pixels and outputs a series of fine spatial–temporal resolution land cover maps instead of reflectance images, which can be used directly for studies of land cover dynamics. Here, three experiments based on simulated and real remotely sensed images were undertaken to evaluate the STIMFM for studies of land cover change. These experiments included comparative assessment of methods based on single-date image such as the super-resolution approaches (e.g., pixel swapping-based super-resolution mapping) and the state-of-the-art spatial–temporal fusion approach that used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Flexible Spatiotemporal DAta Fusion model (FSDAF) to predict the fine-resolution images, in which the maximum likelihood classifier and the automated land cover updating approach based on integrated change detection and classification method were then applied to generate the fine-resolution land cover maps. Results show that the methods based on single-date image failed to predict the pixels of changed and unchanged land cover with high accuracy. The land cover maps that were obtained by classification of the reflectance images outputted from ESTARFM and FSDAF contained substantial misclassification, and the classification accuracy was lower for pixels of changed land cover than for pixels of unchanged land cover. In addition, STIMFM predicted fine spatial–temporal resolution land cover maps from a series of Landsat images and a few Google Earth images, to which ESTARFM and FSDAF that require correlation in reflectance bands in coarse and fine images cannot be applied. Notably, STIMFM generated higher accuracy for pixels of both changed and unchanged land cover in comparison with other methods