Nicholas Weir

Discovery of an infrared nucleus in Cygnus A - An obscured quasar revealed

This paper reports on the discovery of a compact, unresolved infrared nucleus, coincident with the radio core, in the prototypical powerful radio galaxy Cygnus A (3C 405). The infrared colors and magnitudes of the nucleus can be explained as a highly reddened extension of the radio continuum. The implied restframe extinction is A(V) equal to about 50 + or {minus} 30 magnitudes. The extinction-corrected luminosity of the object is in the quasar range. This discovery gives some support to the unification models for quasars and powerful radio galaxies. 35 refs.

The SKICAT System for Processing and Analyzing Digital Imaging Sky Surveys

We describe the design and implementation of a software system for producing, managing, and analyzing catalogs from the digital scans of the Second Palomar Observatory Sky Surveys. The system (SKICAT) integrates new and existing packages for performing the full sequence of tasks from raw pixel processing, to object classification, to the matching of multiple, overlapping Schmidt plates and CCD calibration frames. We describe the relevant details of constructing SKITCAT plate, CCD, matched, and object catalogs. Plate and CCD catalogs are generated from images, while the latter are derived from existing catalogs. A pair of programs complete the majroity of plate and CCD processing in an automated, pipeline fashion, with the user required to execute a minimal number of pre- and post-processing procedures. We apply a modified version of FOCAS for the detection and photometry, and new software for matching catalogs on an object by object basis. SKICAT employs modern machine learning techniques, such as decision trees, to perform automatic star-galaxy-artifact classification with a > 90% accuracy down to ~1m above the plate detection limit. The system also provides a variety of tools for interactively querying and analyzing the resulting object catalogs.

Automated cataloging and analysis of sky survey image databases: the SKICAT system

We describe the application of machine learning and state-of-the-art database management technology to the development of an automated tool for the reduction and analysis of a large astronomical data set. The 3 terabytes worth of images are expected to contain on the order of 5 x 10^7 galaxies and 5 x 10^8 stars. For the primary scientific analysis of these data, it is necessary to detect, measure, and classify every sky object. The size of the complete data set precludes manual reduction, requiring an automated approach. SKICAT integrates techniques for image processing, data classification, and database management. Once sky objects are detected, a set of basic features for each object are computed. The learning algorithms are trained to classify the detected objects and can classify objects too faint for visual classification with an accuracy level of about 941Z0. This increases the number of classified objects in the final catalog three-fold relative to the best results from digitized photographic sky surveys to date. The tasks of managing and matching the resulting hundreds of plate catalogs is accomplished using custom software and the Sybase relational DBMS. A full array of scientific analysis tools are provided for filtering, manipulating, plotting, and listing the data in the sky object database. We are currently experimenting with the use of machine discovery tools, such as the AUTOCLASS unsupervised classification program, on the data. SKICAT represents a system in which machine learning played a powerful and enabling role, and solved a difficult, scientifically significant problem. The primary benefits of our overall approach are increased data reduction throughput consistency of classification; and the ability to easily access, analyze, and create new information from an otherwise unfathomable data set.

Automated analysis and exploration of image databases: Results, progress, and challenges

In areas as diverse as earth remote sensing, astronomy, and medical imaging, image acquisition technology has undergone tremendous improvements in recent years. The vast amounts of scientific data are potential treasure-troves for scientific investigation and analysis. Unfortunately, advances in our ability to deal with this volume of data in an effective manner have not paralleled the hardware gains. While special-purpose tools for particular applications exist, there is a dearth of useful general-purpose software tools and algorithms which can assist a scientist in exploring large scientific image databases. This paper presents our recent progress in developing interactive semi-automated image database exploration tools based on pattern recognition and machine learning technology. We first present a completed and successful application that illustrates the basic approach: the SKICAT system used for the reduction and analysis of a 3 terabyte astronomical data set. SKICAT integrates techniques from image processing, data classification, and database management. It represents a system in which machine learning played a powerful and enabling role, and solved a difficult, scientifically significant problem. We then proceed to discuss the general problem of automated image database exploration, the particular aspects of image databases which distinguish them from other databases, and how this impacts the application of off-the-shelf learning algorithms to problems of this nature. A second large image database is used to ground this discussion: Magellans images of the surface of the planet Venus. The paper concludes with a discussion of current and future challenges.

From Digitized Images to Online Catalogs Data Mining a Sky Survey

The value of scientific digital-image libraries seldom lies in the pixels of images. For large collections of images, such as those resulting from astronomy sky surveys, the typical useful product is an online database cataloging entries of interest. We focus on the automation of the cataloging effort of a major sky survey and the availability of digital libraries in general. The SKICAT system automates the reduction and analysis of the three terabytes worth of images, expected to contain on the order of 2 billion sky objects. For the primary scientific analysis of these data, it is necessary to detect, measure, and classify every sky object. SKICAT integrates techniques for image processing, classification learning, database management, and visualization. The learning algorithms are trained to classify the detected objects and can classify objects too faint for visual classification with an accuracy level exceeding 90 percent. This accuracy level increases the number of classified objects in the final catalog threefold relative to the best results from digitized photographic sky surveys to date. Hence, learning algorithms played a powerful and enabling role and solved a difficult, scientifically significant problem, enabling the consistent, accurate classification and the ease of access and analysis of an otherwise unfathomable data set.

The first detection of a collapsed core globular cluster in M31

We report on the observations of a globular cluster (designated G105 = Bo 343) in the nearby spiral galaxy M31, using the Hubble Space Telescope (HST). Image deconvolutions using three different methods indicate that this cluster has a density profile with the morphology characteristic of stellar systems which have undergone core collapse, i.e., a power-law density cusp near the center. This is the first such detection in a galaxy as distant as M31. This discovery may lead to extensive future statistical studies of the globular cluster system of M31, and thus a better understanding of its evolution.

Cataloging the Northern Sky Using a new Generation of Software Technology

We have developed a system, called SKICAT, for producing, managing and analyzing catalogs from the digitized POSS-II survey. The system classifies and matches catalogs from multiple, overlapping plate scans as well as CCD calibration sequences; and it can be used for the scientific analysis of the resulting catalogs. It incorporates a number of novel machine-learning and AI tools, including the star/galaxy classification using decision tree algorithms. This results in star/galaxy separation accurate to 90% or better down to Bj, ∼ 21m, i.e. ∼ 1m above the plate limit The final catalog is expected to contain at least 5 x 107 galaxies and > 2 x 109 stars. We present preliminary results on galaxy counts from a test region near the NGP. We find a mild excess over the no-evolution models, smaller than previously found by the APM group. A search for z > 4 quasars and the two-point correlation analysis of this data set are in progress.

High-resolution imaging of the double QSO 2345+007

Raw and maximum entropy restored images of the quasar pair (gravitational lens candidate) 2345 + 007 A and B are presented. Restorations are performed using an implementation of the Gull-Skilling MEMSYS-3 package of maximum entropy method subroutines designed to achieve subpixel resolution in certain data regimes. Extensive simulations of the data imply that it is possible to detect structure in the restored images down to the 0.4 inch level. Using this method, it is qualitatively confirmed that component B is resolved and, at least at visual and red wavelengths, elongated in a direction almost perpendicular to the line joining A and B. Evidence is found for a color difference and variation in the magnitude difference between the two components. These data, in conjunction with recent spectroscopic results, more likely favor the multiple quasar rather than gravitational lens interpretation of the objects. 18 refs.

The current ability of HST to reveal morphological structure in medium-redshift galaxies

The capabilities of the Faint Object Camera (FOC) and the Wide Field Camera (WFC) are assessed on the basis of a brief program of single-orbit images of medium-faint galaxies. The FOC yielded a good resolved image of a compact galaxy at a blue magnitude J of 20.5 in a single-orbit exposure. WFC images have a survey capability that can include many galaxies per field, with sufficient resolving power to distinguish clearly between galaxies and stars down to the level of 0.2 arcsec, depending on the signal-to-noise ratio, and a reasonable capacity for morphology. Although some morphological detail can be discerned in even the aberrated images, deconvolutions are found to greatly enhance the ability to see structural detail. Even at the low S/N that is provided by single-orbit exposures, the more sophisticated restoration methods offer some advantage over simple Fourier or Lucy techniques.

Globular Clusters in M31 with the Hubble Space Telescope

The study of the globular clusters in the Galaxy has provided a wealth of essential information on stellar evolution and dynamics, as well as on the formation and early evolutionary stages of the Galaxy itself.