William D. Pence

Definition of the Flexible Image Transport System (FITS), version 3.0

The Flexible Image Transport System (FITS) has been used by astronomers for over 30 years as a data interchange and archiving format; FITS files are now handled by a wide range of astronomical software packages. Since the FITS format definition document (the “standard”) was last printed in this journal in 2001, several new features have been developed and standardized, notably support for 64-bit integers in images and tables, variable-length arrays in tables, and new world coordinate system conventions which provide a mapping from an element in a data array to a physical coordinate on the sky or within a spectrum. The FITS Working Group of the International Astronomical Union has therefore produced this new version 3.0 of the FITS standard, which is provided here in its entirety. In addition to describing the new features in FITS, numerous editorial changes were made to the previous version to clarify and reorganize many of the sections. Also included are some appendices which are not formally part of the standard. The FITS standard is likely to undergo further evolution, in which case the latest version may be found on the FITS Support Office Web site at http://fits.gsfc.nasa.gov/, which also provides many links to FITS-related resources.

Lossless Astronomical Image Compression and the Effects of Noise

ABSTRACT.We compare a variety of lossless image compression methods on a large sample of astronomical images and show how the compression ratios and speeds of the algorithms are affected by the amount of noise (that is, entropy) in the images. In the ideal case where the image pixel values have a random Gaussian distribution, the equivalent number of uncompressible noise bits per pixel is given by Nbits=log2(σ12) and the lossless compression ratio is given by R=BITPIX/(Nbits+K) where BITPIX is the bit length of the pixel values (typically 16 or 32), and KK is a measure of the efficiency of the compression algorithm. We show that real astronomical CCD images also closely follow these same relations, by using a robust algorithm for measuring the equivalent number of noise bits from the dispersion of the pixel values in background regions of the image. We perform image compression tests on a large sample of 16-bit integer astronomical CCD images using the GZIP compression program and using a newer FITS tiled...

Optimal Compression of Floating-Point Astronomical Images Without Significant Loss of Information

We describe a compression method for floating-point astronomical images that gives compression ratios of 6-10 while still preserving the scientifically important information in the image. The pixel values are first preprocessed by quantizing them into scaled integer intensity levels, which removes some of the uncompressible noise in the image. The integers are then losslessly compressed using the fast and efficient Rice algorithm and stored in a portable FITS format file. Quantizing an image more coarsely gives greater image compression, but it also increases the noise and degrades the precision of the photometric and astrometric measurements in the quantized image. Dithering the pixel values during the quantization process can greatly improve the precision of measurements in the images. This is especially important if the analysis algorithm relies on the mode or the median, which would be similarly quantized if the pixel values are not dithered. We perform a series of experiments on both synthetic and real astronomical CCD images to quantitatively demonstrate that the magnitudes and positions of stars in the quantized images can be measured with the predicted amount of precision. In order to encourage wider use of these image compression methods, we have made available a pair of general-purpose image compression programs, called fpack and funpack, which can be used to compress any FITS format image.

New image compression capabilities in CFITSIO

The previously proposed tiled-image compression scheme is now fully supported when reading and writing FITS images using the CFITSIO subroutine library. This scheme generally produces image compression factors that are superior to the standard gzip or unix compress algorithms, especially when compressing floating point data type images. In addition to reducing the required amount of disk space to store the image, this compression technique often makes applications programs run faster because of the reduced amount of magnetic disk I/O that is required to read or write the image.

DIVISION XII / COMMISSION 5 / WORKING GROUP FITS DATA FORMAT

The Working Group FITS (WG-FITS) is the international control authority for the Flexible Image Transport System (FITS) data format. The WG-FITS was formed in 1988 by a formal resolution of the IAU XX General Assembly in Baltimore (MD, USA), 1988, to maintain the existing FITS standards and to approve future extensions to FITS.

DIVISION XII / COMMISSON 5 / WORKING GROUP: FITS

The business meeting began with a brief review of the current rules and procedures of the WG, which are documented on the WG web page. Four regional FITS committees have been established by the WG, covering North American, Europe, Japan, and Australian/New Zealand, to provide advice to the WG on pending proposals. While it is recognized that this committee structure might need to be revised to provide representation to other regions, the current system is working well, and there were no motions to make any changes at this time.

COMMISSION 5: DOCUMENTATION AND ASTRONOMICAL DATA

Commission 5 and its working groups have continued to operate at a high level of activity over the last three years. In an era when the volume of astronomical data generated by next-generation instruments continues to increase dramatically, and data centres and data tools become increasingly central to front-line astronomical research, the activities of Commission 5 are becoming even more significant. However, most of the activities of Commission 5 take place through its working groups. That was reflected in the meetings at the IAU GA, where there was only one short Business Meeting of the Commission as a whole, but several vigorous meetings of the working groups.

Hera: the HEASARC web-based data analysis environment

Hera is a new experiment at the HEASARC (High Energy Astrophysics Science Archive Research Center) at the NASA Goddard Space Flight Center to provide a complete data analysis environment over the Internet for archival researchers. This new facility complements the existing Browse database search facility that is available on the Web. With Hera, users can search the HEASARC data archives with a Web browser and save any selected data set to their Hera disk space area. This only takes a few seconds compared to the many minutes or hours that it could take to down load large data sets to the users local machine. The user can then immediately log into one of the available Hera server machines and begin analyzing the data without having to install any local software except for a very small Hera client application program that runs on the users local machine. Hera is currently most useful for expert users who are already familiar with analyzing high energy data sets with the HEASARC software. In the future we intend to make Hera more useful for the novice scientific user by providing more on-line help features to guide the user through the data analysis process.

Classifying the high energy universe with ClassX

Building an automated classifier for high-energy sources provides an opportunity to prototype approaches to building the Virtual Observatory with a substantial immediate scientific return. The ClassX collaboration is combining existing data resources with trainable classifiers to build a tool that classifies lists of objects presented to it. In our first year the collaboration has concentrated on developing pipeline software that finds and combines information of interest and in exploring the issues that will be needed for successful classification. ClassX must deal with many key VO issues: automating access to remote data resources, combining heterogeneous data and dealing with large data volumes. While the VO must attempt to deal with these problems in a generic way, the clear science goals of ClassX allow us to act as a pathfinder exploring particular approaches to addressing these issues.