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.
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 user's 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 user's 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.
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.