ENGINEERING

LIVERMORE, CA -- A project supported by the United States Department of Energy (DOE) and the National Science Foundation (NSF) to determine the nature of dark matter in the halo of the Milky Way has yielded a treasure trove of data on 73 million stars, many of them variable. This database, created by an international team in Australia and the United States, has been made available to astronomy researchers worldwide via the Internet. The Massive Compact Halo Objects (MACHO) team scrutinized the Large and Small Magellanic Clouds, two galaxies that orbit the Milky Way, and the bulge of the Milky Way in an eight-year search for massive objects, such as planets or brown dwarfs, believed to make up much of the dark matter there. These objects can be detected through gravitational lensing, in which the light reaching Earth from the extragalactic stars is magnified due to the gravitational force generated by the massive objects. As a byproduct, the search yielded images and light curves of 73 million stars. The brightness of many of these stars varies in a regular pattern, and their light curves chart the pattern. "A particularly useful feature of this data release is that we provide period, amplitude and tentative classification information in a catalog for periodically varying stars in the Large Magellanic Cloud," said Kem Cook, of Lawrence Livermore National Laboratory, who has led the variable star work for the project. "The light curve is a window into the heart of a star, providing us with information that is not available in any other way," said Morris Aizenman, a senior science adviser at NSF. "As these data are analyzed by the world's scientific community, they are certain to reveal some surprises." The Cepheid variables, one type of variable star, are useful as "meter sticks" for measuring distances in the universe. Other potential uses of the data include studying the interiors of stars and their evolution, and estimating the age of the universe. The light amplifications sought by the MACHO scientists are so rare that, in order to generate useful data, they examined millions of stars in more than 200 separate regions, using the 1.3-meter Great Melbourne Telescope at Mt. Stromlo Observatory, Australia. They found almost 20 potential candidates for massive objects in the halo of the Milky Way in a partial analysis of their data. The lightcurves, along with images and a catalog of the variable stars, are available for viewing or downloading from the MACHO project websites at www.macho.mcmaster.ca and wwwmacho.anu.edu.au. Sophisticated search engines and image analysis tools assist researchers accessing the data. "The combination of large databases and computational tools are speeding scientific discovery in all fields, and we wanted to expand this capability for astronomers," said U.S. team leader Charles Alcock of the University of Pennsylvania. Alcock started the MACHO project at Lawrence Livermore in 1990 along with Cook and Tim Axelrod, formerly of Lawrence Livermore and now of the Australian National University. Another team member, Robyn Allsman of the Australian National University, took the lead in making the data available. "The MACHO web delivery system grew from my determination that the data should outlive the MACHO Project itself," Allsman said. "Use of emerging standards, such as GLU and XML, enabled the data to merge into the evolving network of linked astronomical data catalogs, and position it for inclusion in future virtual observatories." The MACHO project received support from the NSF-supported Center for Particle Astrophysics at the Universities of California at Berkeley, Santa Barbara and San Diego; DOE's Lawrence Livermore National Laboratory; and the Australian National University. Founded in 1952, Lawrence Livermore National Laboratory is a national nuclear security laboratory, with a mission to ensure national security and apply science and technology to the important issues of our time. The National Nuclear Security Administration’s Lawrence Livermore National Laboratory is managed by the University of California.