Instead, the researchers wanted to see if neural nets could distinguish the two kinds of supernovae just based on how the intensity of their light varies over time. After training their neural net on simulated data comprised of 1,652 type Ia supernovae and 1,560 core-collapse supernovae, Kovacs and her colleagues found when their system was tested with 1,652 type Ia supernovae and 195 core-collapse supernovae, it could distinguish the two kinds of explosions with 98.7 percent accuracy.
Beyond big explosions
In addition to supernova research, neural nets can help scientists dig up insights from the mountains of information that astronomers are now collecting from satellites and observatories. For instance, graduate researcher Connor Hause and astrophysicist Andrej Prsa at Villanova University and their colleagues found that neural nets can help analyze the vast amounts of data regarding eclipsing binaries — that is, pairs of stars in binary systems that occasionally eclipse one another.
Eclipsing binaries can yield a great deal of details on the orbits, comparative temperatures, and other fundamental properties of the stars in these systems. "Their study also enables the testing of many stellar evolution theories, as well as helping determine distances to objects both within and outside our galaxy," Hause says.
Hause, Prsa and their colleagues found that neural nets trained to analyze the light from nearly 21,000 simulated eclipsing binaries could automatically compute the physical parameters of 2,875 eclipsing binaries spotted by NASA's Kepler space observatory in only 36 hours. Hause adds that neural networks can also perform such automated analysis can other kinds of star systems besides eclipsing binaries.
"Although the training process normally takes tens of hours to complete, hundreds of thousands of observations can be parameterized by a trained artificial neural network in a matter of seconds," Hause says. "Successful implementations of artificial neural network systems will ensure that scientific yield keeps pace with future data collection rates."
As powerful as neural nets are for astronomy research, a weakness of theirs is that while a neural net might tell you what the answer to a problem is, "it will not tell you why," Villar says. This makes it difficult to determine "what physics it is actually telling us about," she says. "It's up to the scientist to figure out what the neural net learned."
All three teams of researchers detailed their findings in January at the 229th American Astronomical Society meeting in Grapevine, Texas.