Astrometry.net

Bibliography

Published work by our group

In reverse chronological order:

• Lang, D., Hogg, D. W., Jester, S., & Rix, H.-W., 2008 Measuring the undetectable: Proper motions and parallaxes of very faint sources, The Astronomical Journal, submitted.
  By simultaneous modeling of the individual-epoch images in a multi-epoch (time series) imaging data set, we show that we can measure proper motions of stars well below the magnitude level (brightness) at which they can be detected in each individual image. By this method we discover a set of new, very cool brown dwarfs (objects too low in mass to start hydrogen burning and become stars).
• Barron, J. T., Hogg, D. W., Lang, D., & Roweis, S., 2008, Blind Date: Using proper motions to determine the ages of historical images, The Astronomical Journal, 136 1490–1501.
  Using only raw pixel data and known catalog proper motions, it is possible to accurately estimate the date of origin of historical imagery. This allows us to retrieve lost meta-data, improve astrometric calibration, and re-estimate proper motions.
• Hogg, D. W., Blanton, M., Lang, D., Mierle, K., & Roweis, S., 2008, Automated Astrometry, Astronomical Data Analysis Software and Systems XVII, R. W. Argyle, P. S. Bunclark, and J. R. Lewis, eds., ASP Conference Series 394, 27–34.
  A summary of the project as of 2007 September, aimed at astronomers with an interest in software.
• Barron, J. T., Stumm, C., Hogg, D. W., Lang, D., & Roweis, S., 2008, Cleaning the USNO-B Catalog through automatic detection of optical artifacts, The Astronomical Journal 135 414–422.
  The USNO-B Catalog of astrometric standards contains about 2 percent spurious entries that are caused by diffraction spikes and circular reflection halos around bright stars in the original imaging data. We use computer vision techniques to identify and remove them. Our code and data are available here.
• Harvey, C., 2004, New algorithms for automated astrometry [PDF], MSc Thesis, University of Toronto.
  Harvey shows that solution to the blind astrometry problem (ie, no first guess at image pointing, rotation, or scale) is possible for at least some kinds of data. Two methods are implemented.

Published work by other groups

In alphabetical order:

• Groth, E. J., 1986, A pattern-matching algorithm for two-dimensional coordinate lists, The Astronomical Journal 91 1244–1248.
  This is the first published algorithm for matching image stars to catalog stars that does not depend on image scale. The method makes use of triangles, with many triangles contributing to each solution. The scaling (with the number of stars n) is bad, but the project is similar in spirit to ours.
• Liebe, C. C., 1993, Pattern recognition of star constellations for spacecraft applications, IEEE Aerospace and Electronic Systems Magazine 8 (no 1) 33–39.
  Liebe shows that the blind astrometry problem is easily solved when (a) you have a camera with a large field of view (tens of deg), (b) you know exactly the image scale (ie, your images are calibrated in deg), and (c) you are working with the brightest 1000 or so stars on the sky. The method involves matching triangles of stars.
• Pál, A. & Bakos, G. A., 2006 Astrometry in wide-field surveys, The Publications of the Astronomical Society of the Pacific 118 1474–1483.
  This is similar in spirit to Groth (1986), but includes a very impressive (and successful) test on real-world data.
• Storkey, A. J., Hambly, N. C., Williams, C. K. I., & Mann, R. G., 2004, Cleaning sky survey data bases using Hough transform and renewal string approaches, Monthly Notices of the Royal Astronomical Society 347 36–51
  They present clever techniques for the automatic detection of common defects in plate-based astronomical catalogs.
• Valdes, F. G., Campusano, L. E., Velasquez, J. D., & Stetson, P. B., 1995, FOCAS Automatic Catalog Matching Algorithms, Publications of the Astronomical Society of the Pacific 107 1119–1128
  An implementation of a method similar to that of Groth (1986).

Background material

In alphabetical order:

• Calabretta, M. R. & Greisen, E. W., 2002, Representations of celestial coordinates in FITS, Astronomy & Astrophysics 395 1077–1122.
  This paper, along with its companion (Greisen & Calabretta 2002, below), sets down the basic FITS WCS standard for astronomical images. The basic WCS standard allows for various simple spherical-to-planar image projections for the purposes of making sky maps.
• Górski, K. M., Hivon, E., Banday, A. J., Wandelt, B. D., Hansen, F. K., Reinecke, M., & Bartelmann, M., 2005, HEALPIX: A framework for high resolution discretization and fast analysis of data distributed on the sphere, The Astrophysical Journal 622 759–771.
• Greisen, E. W. & Calabretta, M. R. 2002, Representations of world coordinates in FITS, Astronomy & Astrophysics 395 1061–1075.
• Monet, D. G. et al, 2003, The USNO-B Catalog, The Astronomical Journal 125 984–993.
  The explanatory paper for the USNO-B1.0 astrometric catalog, on which all of our work is based, either directly or indirectly.
• Shupe, D. L., Moshir, M., Li, J., Makovoz, D., Narron, R., & Hook, R. N., 2005, The SIP Convention for Representing Distortion in FITS Image Headers, in ASP Conf. Ser. 347: Astronomical Data Analysis Software and Systems XIV, Shopbell, P., Britton M., & Ebert R., eds., 491–498.
  This paper clearly walks the user through the very sensible SIP extension to the TAN option in the WCS standard.
• Wells, D. C., Greisen, E. W., & Harten, R. H., 1981, FITS: A flexible image transport system, Astronomy & Astrophysics Supplement Series 44 363–370.