January 7, 2003
Berkeley Lab Research News
Nearby Supernova Factory announces 34 supernovae in one year
Best rookie year ever for a supernova search
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BERKELEY, CA   The Nearby Supernova Factory (SNfactory), an international collaboration based at Lawrence Berkeley National Laboratory, today announced that it had discovered 34 supernovae during the first year of the prototype system's operation -- all but two of them in the last four months alone. The announcement was made at the 201st meeting of the American Astronomical Society in Seattle.

Image of wide-field camera
The Nearby Supernova Factory uses images from wide-field cameras built and operated by the Jet Propulsion Laboratory's Near Earth Asteroid Tracking program (NEAT). One camera is mounted on the Oschin Telescope at Mount Palomar Observatory.

"This is the best performance ever for a 'rookie' supernova search," said Greg Aldering of Berkeley Lab's Physics Division, principal investigator of the SNfactory. "We have shown we can discover supernovae at the rate of nine a month, a rate other searches have reached only after years of trying."

The goal of the SNfactory is to discover and carefully study 300 to 600 nearby Type Ia supernovae, many more than have been studied so far. Knowledge of nearby supernovae will allow better use of observations of very distant supernovae to measure the expansion history of the universe; distant supernovae are the key to understanding the mysterious dark energy that comprises some two-thirds of the density of the universe.

A torrent of data

Aldering credits the SNfactory's rapidly increasing discovery rate to "data pipeline" software developed by Michael Wood-Vasey of Berkeley Lab's Physics Division. The pipeline is filled with up to 50 gigabytes of data a night (a gigabyte is a billion bytes) from wide-field cameras, built and operated by the Near Earth Asteroid Tracking program (NEAT), on remote telescopes at Mount Palomar in Southern California and at Haleakala on Maui, Hawaii.

NEAT images
[Click to view larger image]
NEAT images are sent to the Nearby Supernova Factory through a special link in the High Performance Wireless Research and Education Network (HPWREN), helping connect Mount Palomar Observatory to the National Energy Research Scientific Computing Center (NERSC) at Berkeley Lab.

By means of a custom high-speed link with Mount Palomar, part of the High Performance Wireless Research and Education Network (HPWREN) spearheaded by Hans-Werner Braun of the San Diego Supercomputer Center, and an existing link to Maui, the data moves to the National Energy Research Scientific Computing Center (NERSC) at Berkeley Lab.

"NEAT sends us images of about 500 square degrees of the sky each night," says Wood-Vasey. "The software we've developed automatically archives these at NERSC and notifies NEAT, which is one of the services we provide in exchange for the use of their images."

Aldering adds that "the SNfactory owes much of its success to NERSC's ability to store and process the vast amounts of data that flow in virtually every night." NERSC's Parallel Distributed Systems Facility, devoted to high-energy physics, astrophysics, and nuclear science, gives the SNfactory team instant access to up to 2 terabytes of imaging data (a terabyte is a trillion bytes), with another 8 terabytes accessible in longer-term storage.

Standard candles to light the universe

The Nearby Supernova Factory grew out of the international Supernova Cosmology Project, also headquartered at Berkeley Lab. In 1998, the Supernova Cosmology Project and the competing High-Z Supernova Search team announced that the universe is expanding at an accelerating rate -- a surprising discovery that resulted from comparing the relative brightness (magnitude) of several dozen Type Ia supernovae at high and low redshift. Type Ia supernovae are used as astronomical standard candles because of their exceeding brightness and remarkably similar optical characteristics.

The universe's accelerating expansion points to the existence of some kind of "dark energy," but while there are several theories about its nature, there is no way to choose among them. A new generation of experiments, using distant supernovae, is required to eliminate those ideas that don't work and learn more about those that might.

First, researchers need to know much more about Type Ia supernovae themselves. "Our motive is to establish a much better sample of nearby Type Ia supernovae, against which the brightness of distant supernovae can be compared to obtain relative distances," says Aldering. At present, astronomers can determine the distance to a well-studied Type Ia supernova with an accuracy of five percent; the SNfactory expects to improve on this accuracy.

Aldering adds "This has to be a large, homogeneous, and well-calibrated sample, containing supernovae which can be studied in great detail." Measurement goals include refining the relationships among a Type Ia's redshift, the width of its light curve -- the time it takes to reach maximum brightness, then slowly fade -- and the features of its spectrum over a wide range of wavelengths.

"We also want to measure the intrinsic colors of a Type Ia at every stage, so we'll know the effects of intervening dust," says Aldering, "and we want to know what difference the 'metallicity' of the home galaxy makes -- that is, the presence of elements heavier than helium. We'll be able to do that by finding and studying supernovae in dim, metal-poor galaxies that are overlooked by other nearby supernova searches."

Catch an exploding star

A decade ago the Supernova Cosmology Project developed the technique of finding very distant supernovae "on demand," by systematically searching the same patches of sky at intervals, then subtracting the two images from each other. Any bright spots left over, once spurious signals had been eliminated, were candidate supernovae.

The Nearby Supernova Factory employs a similar method, using images generated by NEAT. NEAT's primary mission is to discover and track asteroids and comets, especially those that could pose a threat to Earth. While its automated telescopes search the solar system, they incidentally image many thousands of galaxies. The telescopes revisit the same regions roughly every six days during a typical 18-day observing period; when a supernova appears in one of those galaxies, the SNfactory can find it.

Image illustrating subtraction technique
This supernova, found in archived data, illustrates the subtraction technique used by the Nearby Supernova Factory to find supernovae in NEAT data. NEAT cameras image an area of the sky for reference (left). They image the same area again several days later (center). The first image is automatically subtracted from the second, and any remaining bright spots may be supernova candidates.

Aldering says the NEAT search strategy has distinct advantages for finding the kind of nearby supernovae of most interest. "It's a blind search," he says. "It doesn't target specific galaxies but looks at whatever galaxies happen to be in an image -- about 40 per image."

One result is that the nearby supernovae found by the SNfactory tend to be a little farther away than those found by searches that target previously catalogued galaxies. In the astronomical jargon, they are more likely to be "in the Hubble flow," Aldering explains, "so it's less likely their redshifts are disturbed by the gravitational pull of neighboring galaxies," meaning their redshifts are good indicators of their distance.

Wood-Vasey explains that subtracting the supernovae from the hundreds of images generated each night first requires software that eliminates spurious signals. "We have to flag defects such as cosmic ray hits, electronic noise, occasionally even ice crystals that can form on the detector system."

At present, while the SNfactory is still in its prototype phase, candidates must be confirmed by human eye. A half dozen undergraduates at the University of California at Berkeley have been trained to inspect the images and identify real supernovae.

"We're working on improving the software's subtraction process," says Wood-Vasey. "The software must sift through billions of objects. It does well, but as always the challenge is getting it to work all the time. The better it performs, the more time our dedicated undergraduates will have for more interesting supernova studies."

The next step: SNIFfing out supernovae

The SNfactory has found so many supernovae so quickly that follow-up observations -- to confirm supernova types, determine redshifts, study full spectra, and identify home-galaxy conditions -- have not been possible for all of them. This will change when the French members of the collaboration complete construction of a revolutionary new spectrograph, to be installed next September on the University of Hawaii's 2.2-meter telescope on Mauna Kea.

Image of a SNIFS spectrograph
[Click to view larger image]
The SNIFS spectrograph, built by the French members of the SNfactory team, will be permanently installed on the University of Hawaii's 2.2-meter telescope on Mauna Kea.

Dubbed SNIFS, for Supernova Integral Field Spectrograph, the fully automated instrument will "go far beyond what we can do now," says Aldering. SNIFS will simultaneously obtain 225 spectra covering the target supernova, its galaxy, and the surrounding sky, through two channels equipped with separate CCDs optimally sensitive to blue light and red light. At the same time, SNIFS corrects telescope tracking and measure atmospheric light absorption by monitoring neighboring stars. Permanently mounted on the telescope, SNIFS will be available to the SNfactory 20 percent of the time.

"We've already proved that the SNfactory can handle a huge amount of data and identify nearby supernovae in the Hubble flow at high, predictable rate," says Aldering. "We're well on our way to providing an essential tool in the effort to identify the nature of the dark energy."

Members of the Nearby Supernova Factory are, from Berkeley Lab, Greg Aldering, Brian C. Lee, Stewart Loken, Peter Nugent, Saul Perlmutter, Robert Quimby, James Siegrist, Lifan Wang, and Michael Wood-Vasey; from the Laboratoire de Physique Nucleaire et de Haute Energies de Paris, Pierre Antilogus, Pierre Astier, Delphine Hardin, Jean-Michael Levy, and Reynald Pain; from the Institute de Physique Nucleaire de Lyon, Yves Copin and Gerard Smadja; and from the Centre de Recherche Astronomique de Lyon, Gilles Adam, Roland Bacon, Jean-Pierre Lemmonier, and Arlette Pecontal.

The Berkeley Lab is a U.S. Department of Energy national laboratory located in Berkeley, California. It conducts unclassified scientific research and is managed by the University of California.

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