Background–source separation in astronomical images with Bayesian probability theory – I. The method

A probabilistic technique for the joint estimation of background and sources with the aim of detecting faint and extended celestial objects is described. Bayesian probability theory is applied to gain insight into the co-existence of background and sources through a probabilistic two-component mixture model, which provides consistent uncertainties of background and sources. A multiresolution analysis is used for revealing faint and extended objects in the frame of the Bayesian mixture model. All the revealed sources are parametrized automatically providing source position, net counts, morphological parameters and their errors.
We demonstrate the capability of our method by applying it to three simulated data sets characterized by different background and source intensities. The results of employing two different prior knowledge on the source signal distribution are shown. The probabilistic method allows for the detection of bright and faint sources independently of their morphology and the kind of background. The results from our analysis of the three simulated data sets are compared with other source detection methods. Additionally, the technique is applied to ROSAT All-Sky Survey data.     

asmooth: a simple and efficient algorithm for adaptive kernel smoothing of two-dimensional imaging data

An efficient algorithm for adaptive kernel smoothing (AKS) of two-dimensional imaging data has been developed and implemented using the Interactive Data Language ( idl ). The functional form of the kernel can be varied (top-hat, Gaussian, etc.) to allow different weighting of the event counts registered within the smoothing region. For each individual pixel, the algorithm increases the smoothing scale until the signal-to-noise ratio (S/N) within the kernel reaches a pre-set value. Thus, noise is suppressed very efficiently, while at the same time real structure, that is, signal that is locally significant at the selected S/N level, is preserved on all scales. In particular, extended features in noise-dominated regions are visually enhanced. The asmooth algorithm differs from other AKS routines in that it allows a quantitative assessment of the goodness of the local signal estimation by producing adaptively smoothed images in which all pixel values share the same S/N above the background .
We apply asmooth to both real observational data (an X-ray image of clusters of galaxies obtained with the Chandra X-ray Observatory) and to a simulated data set. We find the asmooth ed images to be fair representations of the input data in the sense that the residuals are consistent with pure noise, that is, they possess Poissonian variance and a near-Gaussian distribution around a mean of zero, and are spatially uncorrelated.     

The VOISE algorithm: a versatile tool for automatic segmentation of astronomical images

The auroras on Jupiter and Saturn can be studied with a high sensitivity and resolution by the Hubble Space Telescope ( HST ) ultraviolet (UV) and far-ultraviolet Space Telescope Imaging Spectrograph (STIS) and Advanced Camera for Surveys (ACS) instruments. We present results of automatic detection and segmentation of Jupiter's auroral emissions as observed by the HST ACS instrument with the VOronoi Image SEgmentation (VOISE). VOISE is a dynamic algorithm for partitioning the underlying pixel grid of an image into regions according to a prescribed homogeneity criterion. The algorithm consists of an iterative procedure that dynamically constructs a tessellation of the image plane based on a Voronoi diagram, until the intensity of the underlying image within each region is classified as homogeneous. The computed tessellations allow the extraction of quantitative information about the auroral features, such as mean intensity, latitudinal and longitudinal extents and length-scales. These outputs thus represent a more automated and objective method of characterizing auroral emissions than manual inspection.     

Object classification at the Nearby Supernova Factory

We present the results of applying new object classification techniques to the supernova search of the Nearby Supernova Factory. In comparison to simple threshold cuts, more sophisticated methods such as boosted decision trees, random forests, and support vector machines provide dramatically better object discrimination: we reduced the number of nonsupernova candidates by a factor of 10 while increasing our supernova identification efficiency. Methods such as these will be crucial for maintaining a reasonable false positive rate in the automated transient alert pipelines of upcoming large optical surveys. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

天文选址中的地理信息处理

地理信息是地面望远镜天文选址研究中的重要内容.尤其是对现有地理信息系统(Geographic Information System,GIS)的有效利用,已成为现代选址中远程分析阶段最重要的手段之一.天文址点的考虑在地理条件上应遵循以下原则:相对落差较大、交通可行、尽量远离人口集中的区域.从未来建站维护和人员长期驻站的角度考虑,天文址点以海拔处于3000～5000 m的范围、距离城镇大本营1 h左右车程为最佳.以云南省大姚县百草岭地区为选址研究对象,展示了GIS数据处理能够为全面了解一个候选区域3维地形地貌、江川河流、道路村落、电力分布以及预测该地区未来人口和城市化发展趋势等方面内容提供准确、直观、定量的参考数据.通过基于GIS数据的科学分析,发现海拔3 600多米的大百草岭山顶平坦而又干旱、周围人口稀少、道路便利、无工厂污染源,是地方政府重要自然保护区;它与直线距离10 km外的乡镇之间的相对落差超过了1 500 m,与50km外的县城的相对落差达到1 800 m,是滇中彝州坝区一座名副其实的孤峰.因此,GIS数据分析对于远程选址十分有益,GIS技术是现代天文选址不可或缺的重要手段.     

The ORAC‐DR data reduction pipeline

The ORAC‐DR data reduction pipeline has been used by the Joint Astronomy Centre since 1998. Originally developed for an infrared spectrometer and a submillimetre bolometer array, it has since expanded to support twenty instruments from nine different telescopes. By using shared code and a common infrastructure, rapid development of an automated data reduction pipeline for nearly any astronomical data is possible. This paper discusses the infrastructure available to developers and estimates the development timescales expected to reduce data for new instruments using ORAC‐DR. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Software design for panoramic astronomical pipeline processing

We describe the software requirement and design specifications for all-sky panoramic astronomical pipelines. The described software aims to meet the specific needs of superwide-angle optics, and includes cosmic-ray hit rejection, image compression, star recognition, sky opacity analysis, transient detection and a web server allowing access to real-time and archived data. The presented software is being regularly used for the pipeline processing of 11 all-sky cameras located in some of the world's premier observatories. We encourage all-sky camera operators to use our software and/or our hosting services and become part of the global Night Sky Live network.     

A fuzzy logic‐based algorithm for cosmic‐ray hit rejection from single images

An algorithm for cosmic‐ray rejection from single images is presented. The algorithm is based on modeling human perception using fuzzy logic. The proposed algorithm is specifically designed to reject multiple‐pixel cosmic ray hits that are larger than some of the point spread functions of the true astronomical sources. Experiments show that the algorithm can accurately reject ∼97.5% of the cosmic rays hits, while mistakenly rejecting 0.02% of the true astronomical sources. The major advantage of the presented algorithm is its computational efficiency. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

An optimal extraction algorithm for imaging photometry

This paper is primarily an investigation of whether the 'optimal extraction' techniques used in CCD spectroscopy can be applied to imaging photometry. It is found that using such techniques provides a gain of around 10 per cent in signal-to-noise ratio over normal aperture photometry. Formally, it is shown to be equivalent to profile fitting, but offers advantages of robust error estimation, freedom from bias introduced by mis-estimating the point spread function, and convenience. In addition some other techniques are presented, which can be applied to profile fitting, aperture photometry and the 'optimal' photometry. Code implementing these algorithms is available at http://www.astro.keele.ac.uk/~timn/.     

The observability of topological defects with ground-based interferometers

Topological defect theories lead to non-Gaussian features on maps of fluctuations of the cosmic microwave background radiation (CMBR), which enable us to distinguish them from maps predicted by standard inflationary models. We have recently presented a maximum entropy method (MEM) which simultaneously deconvolves interferometer maps of CMBR fluctuations, and separates out foreground contaminants. By applying this method to simulated observations using a realistic ground-based interferometer, we demonstrate that it is possible to recover the prominent hotspots in the CMBR maps which delineate individual defects, even in the presence of a significant Galactic foreground.     

An adaptive loop gain selection for CLEAN deconvolution algorithm

Radio interferometry significantly improves the resolution of observed images, and the final result also relies heavily on data recovery. The Cotton-Schwab CLEAN(CS-Clean) deconvolution approach is a widely used reconstruction algorithm in the field of radio synthesis imaging. However, parameter tuning for this algorithm has always been a difficult task. Here, its performance is improved by considering some internal characteristics of the data. From a mathematical point of view, a peak signal-to-noise-based(PSNRbased) method was introduced to optimize the step length of the steepest descent method in the recovery process. We also found that the loop gain curve in the new algorithm is a good indicator of parameter tuning.Tests show that the new algorithm can effectively solve the problem of oscillation for a large fixed loop gain and provides a more robust recovery.     

E3D,the Euro3D visualization tool II: Mosaics,VIMOS data and large IFUs of the future

In this paper, we describe the capabilities of E3D, the Euro3D visualization tool, to handle and display data created by large Integral Field Units (IFUs) and by mosaics consisting of multiple pointings. The reliability of the software has been tested with real data, originating from the PMAS instrument in mosaic mode and from the VIMOS instrument, which features the largest IFU currently available. The capabilities and limitations of the current software are examined in view of future large IFUs, which will produce extremely large datasets. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Difference imaging photometry of blended gravitational microlensing events with a numerical kernel

The numerical kernel approach to difference imaging has been implemented and applied to gravitational microlensing events observed by the PLANET collaboration. The effect of an error in the source-star coordinates is explored and a new algorithm is presented for determining the precise coordinates of the microlens in blended events, essential for accurate photometry of difference images. It is shown how the photometric reference flux need not be measured directly from the reference image but can be obtained from measurements of the difference images combined with the knowledge of the statistical flux uncertainties. The improved performance of the new algorithm, relative to isis2 , is demonstrated.     

Weak gravitational shear and flexion with polar shapelets

We derive expressions, in terms of 'polar shapelets', for the image distortion operations associated with weak gravitational lensing. Shear causes galaxy shapes to become elongated, and is sensitive to the second derivative of the projected gravitational potential along their line of sight; flexion bends galaxy shapes into arcs, and is sensitive to the third derivative. Polar shapelets provide a natural representation, in which both shear and flexion transformations are compact. Through this tool, we understand progress in several weak lensing methods. We then exploit various symmetries of shapelets to construct a range of shear estimators with useful properties. Through an analogous investigation, we also explore several flexion estimators. In particular, some of the estimators can be measured simultaneously and independently for every galaxy, and will provide unique checks for systematics in future weak lensing analyses. Using simulated images from the Shear TEsting Programme, we show that we can recover input shears with no significant bias. A complete software package to parametrize astronomical images in terms of polar shapelets, and to perform a full weak lensing analysis, is available on the Internet.     

Automatic morphological classification of galaxy images

We describe an image analysis supervised learning algorithm that can automatically classify galaxy images. The algorithm is first trained using manually classified images of elliptical, spiral and edge-on galaxies. A large set of image features is extracted from each image, and the most informative features are selected using Fisher scores. Test images can then be classified using a simple Weighted Nearest Neighbour rule such that the Fisher scores are used as the feature weights. Experimental results show that galaxy images from Galaxy Zoo can be classified automatically to spiral, elliptical and edge-on galaxies with an accuracy of ∼90 per cent compared to classifications carried out by the author. Full compilable source code of the algorithm is available for free download, and its general-purpose nature makes it suitable for other uses that involve automatic image analysis of celestial objects.