Astrometry.net: Blind astrometric calibration of arbitrary astronomical images

We have built a reliable and robust system that takes as input an astronomical image, and returns as output the pointing, scale, and orientation of that image (the astrometric calibration or WCS information). The system requires no first guess, and works with the information in the image pixels alone; that is, the problem is a generalization of the "lost in space" problem in which nothing--not even the image scale--is known. After robust source detection is performed in the input image, asterisms (sets of four or five stars) are geometrically hashed and compared to pre-indexed hashes to generate hypotheses about the astrometric calibration. A hypothesis is only accepted as true if it passes a Bayesian decision theory test against a background hypothesis. With indices built from the USNO-B Catalog and designed for uniformity of coverage and redundancy, the success rate is 99.9% for contemporary near-ultraviolet and visual imaging survey data, with no false positives. The failure rate is consistent with the incompleteness of the USNO-B Catalog; augmentation with indices built from the 2MASS Catalog brings the completeness to 100% with no false positives. We are using this system to generate consistent and standards-compliant meta-data for digital and digitized imaging from plate repositories, automated observatories, individual scientific investigators, and hobbyists. This is the first step in a program of making it possible to trust calibration meta-data for astronomical data of arbitrary provenance.Comment: submitted to A

Infrared Astronomical Satellite (IRAS) Scientific Data Analysis System

The Jet Propulsion Laboratory's Scientific Data Analysis System will process Infrared Astronomical Satellite data and produce a catalog of perhaps a million infrared sources in the sky, as well as other vital information for astronomical research

Safeguarding Old and New Journal Tables for the VO: Status for Extragalactic and Radio Data

Independent of established data centers, and partly for my own research, since 1989 I have been collecting the tabular data from over 2600 articles concerned with radio sources and extragalactic objects in general. Optical character recognition (OCR) was used to recover tables from 740 papers. Tables from only 41 percent of the 2600 articles are available in the CDS or CATS catalog collections, and only slightly better coverage is estimated for the NED database. This fraction is not better for articles published electronically since 2001. Both object databases (NED, SIMBAD, LEDA) as well as catalog browsers (VizieR, CATS) need to be consulted to obtain the most complete information on astronomical objects. More human resources at the data centers and better collaboration between authors, referees, editors, publishers, and data centers are required to improve data coverage and accessibility. The current efforts within the Virtual Observatory (VO) project, to provide retrieval and analysis tools for different types of published and archival data stored at various sites, should be balanced by an equal effort to recover and include large amounts of published data not currently available in this way.Comment: 11 pages, 4 figures; accepted for publication in Data Science Journal, vol. 8 (2009), http://dsj.codataweb.org; presented at Special Session "Astronomical Data and the Virtual Observatory" on the conference "CODATA 21", Kiev, Ukraine, October 5-8, 200

AMADA-Analysis of Multidimensional Astronomical Datasets

We present AMADA, an interactive web application to analyse multidimensional datasets. The user uploads a simple ASCII file and AMADA performs a number of exploratory analysis together with contemporary visualizations diagnostics. The package performs a hierarchical clustering in the parameter space, and the user can choose among linear, monotonic or non-linear correlation analysis. AMADA provides a number of clustering visualization diagnostics such as heatmaps, dendrograms, chord diagrams, and graphs. In addition, AMADA has the option to run a standard or robust principal components analysis, displaying the results as polar bar plots. The code is written in R and the web interface was created using the Shiny framework. AMADA source-code is freely available at https://goo.gl/KeSPue, and the shiny-app at http://goo.gl/UTnU7I.Comment: Accepted for publication in Astronomy & Computin