Finding faint HI structure in and around galaxies: scraping the barrel

Soon to be operational HI survey instruments such as APERTIF and ASKAP will produce large datasets. These surveys will provide information about the HI in and around hundreds of galaxies with a typical signal-to-noise ratio of $\sim$ 10 in the inner regions and $\sim$ 1 in the outer regions. In addition, such surveys will make it possible to probe faint HI structures, typically located in the vicinity of galaxies, such as extra-planar-gas, tails and filaments. These structures are crucial for understanding galaxy evolution, particularly when they are studied in relation to the local environment. Our aim is to find optimized kernels for the discovery of faint and morphologically complex HI structures. Therefore, using HI data from a variety of galaxies, we explore state-of-the-art filtering algorithms. We show that the intensity-driven gradient filter, due to its adaptive characteristics, is the optimal choice. In fact, this filter requires only minimal tuning of the input parameters to enhance the signal-to-noise ratio of faint components. In addition, it does not degrade the resolution of the high signal-to-noise component of a source. The filtering process must be fast and be embedded in an interactive visualization tool in order to support fast inspection of a large number of sources. To achieve such interactive exploration, we implemented a multi-core CPU (OpenMP) and a GPU (OpenGL) version of this filter in a 3D visualization environment ($\tt{SlicerAstro}$).Comment: 17 pages, 9 figures, 4 tables. Astronomy and Computing, accepte

A difference boosting neural network for automated star-galaxy classification

In this paper we describe the use of a new artificial neural network, called the difference boosting neural network (DBNN), for automated classification problems in astronomical data analysis. We illustrate the capabilities of the network by applying it to star galaxy classification using recently released, deep imaging data. We have compared our results with classification made by the widely used Source Extractor (SExtractor) package. We show that while the performance of the DBNN in star-galaxy classification is comparable to that of SExtractor, it has the advantage of significantly higher speed and flexibility during training as well as classification.Comment: 9 pages, 1figure, 7 tables, accepted for publication in Astronomy and Astrophysic

The VISTA Science Archive

We describe the VISTA Science Archive (VSA) and its first public release of data from five of the six VISTA Public Surveys. The VSA exists to support the VISTA Surveys through their lifecycle: the VISTA Public Survey consortia can use it during their quality control assessment of survey data products before submission to the ESO Science Archive Facility (ESO SAF); it supports their exploitation of survey data prior to its publication through the ESO SAF; and, subsequently, it provides the wider community with survey science exploitation tools that complement the data product repository functionality of the ESO SAF. This paper has been written in conjunction with the first public release of public survey data through the VSA and is designed to help its users understand the data products available and how the functionality of the VSA supports their varied science goals. We describe the design of the database and outline the database-driven curation processes that take data from nightly pipeline-processed and calibrated FITS files to create science-ready survey datasets. Much of this design, and the codebase implementing it, derives from our earlier WFCAM Science Archive (WSA), so this paper concentrates on the VISTA-specific aspects and on improvements made to the system in the light of experience gained in operating the WSA.Comment: 22 pages, 16 figures. Minor edits to fonts and typos after sub-editting. Published in A&

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complete database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful, and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications, and has produced real, published results

SPIDERS: Selection of spectroscopic targets using AGN candidates detected in all-sky X-ray surveys

SPIDERS (SPectroscopic IDentification of eROSITA Sources) is an SDSS-IV survey running in parallel to the eBOSS cosmology project. SPIDERS will obtain optical spectroscopy for large numbers of X-ray-selected AGN and galaxy cluster members detected in wide area eROSITA, XMM-Newton and ROSAT surveys. We describe the methods used to choose spectroscopic targets for two sub-programmes of SPIDERS: X-ray selected AGN candidates detected in the ROSAT All Sky and the XMM-Newton Slew surveys. We have exploited a Bayesian cross-matching algorithm, guided by priors based on mid-IR colour-magnitude information from the WISE survey, to select the most probable optical counterpart to each X-ray detection. We empirically demonstrate the high fidelity of our counterpart selection method using a reference sample of bright well-localised X-ray sources collated from XMM-Newton, Chandra and Swift-XRT serendipitous catalogues, and also by examining blank-sky locations. We describe the down-selection steps which resulted in the final set of SPIDERS-AGN targets put forward for spectroscopy within the eBOSS/TDSS/SPIDERS survey, and present catalogues of these targets. We also present catalogues of ~12000 ROSAT and ~1500 XMM-Newton Slew survey sources which have existing optical spectroscopy from SDSS-DR12, including the results of our visual inspections. On completion of the SPIDERS program, we expect to have collected homogeneous spectroscopic redshift information over a footprint of ~7500 deg$^2$ for >85 percent of the ROSAT and XMM-Newton Slew survey sources having optical counterparts in the magnitude range 17<r<22.5, producing a large and highly complete sample of bright X-ray-selected AGN suitable for statistical studies of AGN evolution and clustering.Comment: MNRAS, accepte

Multivariate Statistical Analysis Software Technologies for Astrophysical Research Involving Large Data Bases

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complex database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects of the SKICAT system, and of some of the scientific results achieved to date. We also developed a user-friendly package for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications and has produced real, published results

Searching for Stars in Compact High-Velocity Clouds. II

We address the hypothesis that High Velocity Clouds correspond to the "missing" dwarf galaxies of the Local Group predicted by cosmological simulations. To this end, we present optical and near-infrared photometry of five additional High Velocity Clouds, one of which produces Lyman series absorption on the sight line towards the Quasar Ton S210, with sufficient resolution and sensitivity to enable the detection of an associated stellar content. We do not detect significant stellar populations intrinsic to any of the five clouds. In combination with the results from our paper I, which had yielded non detections of stellar content in another five cases, we find that there is a 50% chance of getting a null result in ten trials if fewer than 7% of all High Velocity Clouds contain stars. We conclude that the population of High Velocity Clouds is an unlikely repository for the "missing" dwarfs of the Local Group.Comment: 6 pages, 3 figures. submitted to MNRA