A sparse approach to astronomical point source detection

In this work we introduce a method for the detection of point sources in images based on al l1-norm sparse approximation. The method is inspired on astronomical image analysis but is directly applicable to any kind of images. We introduce a "top-to-bottom" detection algorithm that can greatly reduce the computational burden of detection if the images are sufficiently well-behaved, in the sense that sources are truly sparse and the chances of source overlapping are small. We test our ideas with simulated faint sources embedded in white noise, comparing the results with the matched filter detector for a number of detection thresholds. We show that the sparse detection approach leads to better results in the ROC curve than the matched filter detector. Moreover, with the sparse approach it is possible to provide an objective stopping criterion for the detection algorithm.The authors acknowledge partial financial support from the Spanish Ministry of Education (MEC) under project ESP2004-07067-C03-01 and from the joint CNR-CSIC research project 2008IT0059. MLC acknowledges an EGEE-III postdoctoral contract at IFCA

ZAP -- Enhanced PCA Sky Subtraction for Integral Field Spectroscopy

We introduce Zurich Atmosphere Purge (ZAP), an approach to sky subtraction based on principal component analysis (PCA) that we have developed for the Multi Unit Spectrographic Explorer (MUSE) integral field spectrograph. ZAP employs filtering and data segmentation to enhance the inherent capabilities of PCA for sky subtraction. Extensive testing shows that ZAP reduces sky emission residuals while robustly preserving the flux and line shapes of astronomical sources. The method works in a variety of observational situations from sparse fields with a low density of sources to filled fields in which the target source fills the field of view. With the inclusion of both of these situations the method is generally applicable to many different science cases and should also be useful for other instrumentation. ZAP is available for download at http://muse-vlt.eu/science/tools.Comment: 12 pages, 7 figures, 1 table. Accepted to MNRA

The Characterised Noise Hi source finder: Detecting Hi galaxies using a novel implementation of matched filtering

The spectral line datacubes obtained from the Square Kilometre Array (SKA) and its precursors, such as the Australian SKA Pathfinder (ASKAP), will be sufficiently large to necessitate automated detection and parametrisation of sources. Matched filtering is widely acknowledged as the best possible method for the automated detection of sources. This paper presents the Characterised Noise Hi (CNHI) source finder, which employs a novel implementation of matched filtering. This implementation is optimised for the 3-D nature of the planned Wide-field ASKAP Legacy L-band All- sky Blind surveY's (WALLABY) Hi spectral line observations. The CNHI source finder also employs a novel sparse representation of 3-D objects, with a high compression rate, to implement Lutz one-pass algorithm on datacubes that are too large to process in a single pass. WALLABY will use ASKAP's phenomenal 30 square degree field of view to image \sim 70% of the sky. It is expected that WALLABY will find 500 000 Hi galaxies out to z \sim 0.2.Comment: Part of the 2012 PASA Source Finding Special Issue, 10 figure

LOFAR Sparse Image Reconstruction

Context. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital phased array interferometer with multiple antennas distributed in Europe. It provides discrete sets of Fourier components of the sky brightness. Recovering the original brightness distribution with aperture synthesis forms an inverse problem that can be solved by various deconvolution and minimization methods Aims. Recent papers have established a clear link between the discrete nature of radio interferometry measurement and the "compressed sensing" (CS) theory, which supports sparse reconstruction methods to form an image from the measured visibilities. Empowered by proximal theory, CS offers a sound framework for efficient global minimization and sparse data representation using fast algorithms. Combined with instrumental direction-dependent effects (DDE) in the scope of a real instrument, we developed and validated a new method based on this framework Methods. We implemented a sparse reconstruction method in the standard LOFAR imaging tool and compared the photometric and resolution performance of this new imager with that of CLEAN-based methods (CLEAN and MS-CLEAN) with simulated and real LOFAR data Results. We show that i) sparse reconstruction performs as well as CLEAN in recovering the flux of point sources; ii) performs much better on extended objects (the root mean square error is reduced by a factor of up to 10); and iii) provides a solution with an effective angular resolution 2-3 times better than the CLEAN images. Conclusions. Sparse recovery gives a correct photometry on high dynamic and wide-field images and improved realistic structures of extended sources (of simulated and real LOFAR datasets). This sparse reconstruction method is compatible with modern interferometric imagers that handle DDE corrections (A- and W-projections) required for current and future instruments such as LOFAR and SKAComment: Published in A&A, 19 pages, 9 figure

Compact source detection in multi-channel microwave surveys: from SZ clusters to polarized sources

In this paper we describe the state-of-the art status of multi-frequency detection techniques for compact sources in microwave astronomy. From the simplest cases where the spectral behaviour is well-known (i.e. thermal SZ clusters) to the more complex cases where there is little a priori information (i.e. polarized radio sources) we will review the main advances and the most recent results in the detection problem.Comment: 13 pages, 4 figures. Accepted for publication in the Special Issue "Astrophysical Foregrounds in Microwave Surveys" of the journal Advances in Astronom

Asteroid Models from Multiple Data Sources

In the past decade, hundreds of asteroid shape models have been derived using the lightcurve inversion method. At the same time, a new framework of 3-D shape modeling based on the combined analysis of widely different data sources such as optical lightcurves, disk-resolved images, stellar occultation timings, mid-infrared thermal radiometry, optical interferometry, and radar delay-Doppler data, has been developed. This multi-data approach allows the determination of most of the physical and surface properties of asteroids in a single, coherent inversion, with spectacular results. We review the main results of asteroid lightcurve inversion and also recent advances in multi-data modeling. We show that models based on remote sensing data were confirmed by spacecraft encounters with asteroids, and we discuss how the multiplication of highly detailed 3-D models will help to refine our general knowledge of the asteroid population. The physical and surface properties of asteroids, i.e., their spin, 3-D shape, density, thermal inertia, surface roughness, are among the least known of all asteroid properties. Apart for the albedo and diameter, we have access to the whole picture for only a few hundreds of asteroids. These quantities are nevertheless very important to understand as they affect the non-gravitational Yarkovsky effect responsible for meteorite delivery to Earth, or the bulk composition and internal structure of asteroids.Comment: chapter that will appear in a Space Science Series book Asteroids I

X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM

The study on point sources in astronomical images is of special importance, since most energetic celestial objects in the Universe exhibit a point-like appearance. An approach to recognize the point sources (PS) in the X-ray astronomical images using our newly designed granular binary-tree support vector machine (GBT-SVM) classifier is proposed. First, all potential point sources are located by peak detection on the image. The image and spectral features of these potential point sources are then extracted. Finally, a classifier to recognize the true point sources is build through the extracted features. Experiments and applications of our approach on real X-ray astronomical images are demonstrated. comparisons between our approach and other SVM-based classifiers are also carried out by evaluating the precision and recall rates, which prove that our approach is better and achieves a higher accuracy of around 89%.Comment: Accepted by ICSP201