Artificial brains called neural nets mimicking the way that our own gray matter works are increasingly finding use in astronomy, helping shed light on everything from supernova debris to binary stars.

Artificial intelligence researchers often seek to develop computers that can learn over time, just like humans. A common strategy these scientists pursue often involves building neural networks, which mimic the pathways of neurons inside the human brain.

In artificial neural networks, software or hardware “neurons” first receive a set of data and then cooperate to solve a problem, such as recognizing an image. The networks then alter the pattern of connections between their neurons to change the way they interact and attempt to solve the problem once more. Over time, after training on many different sets of data, the network learns which patterns of connections are best at computing solutions.

A Harvard team led by Ashley Villar, a graduate student in astronomy at Harvard University, used neural nets to study Type Ia supernovae. This kind of explosion occurs after a white dwarf star obliterates itself after siphoning off too much mass from a companion star.

All type 1a supernovae have relatively similar brightnesses, so astronomers use them as "standard candles" to measure cosmic distances — the dimmer a type Ia supernova appears to be, the farther away it is from Earth. Scientists rely on this data to investigate enigmas such as the mysterious accelerating expansion of the universe.

"Neural nets have had a lot of success in complex datasets involving images — for example, Facebook can recognize your face thanks to neural nets," Villar says.

Still, there are sources of variation in the light from type Ia supernovae that can lead to systematic errors in studies of them. One source of such variation is the "metallicities" of the supernovae — that is, the abundances of elements heavier than hydrogen and helium.