Machine learning tools classify 1,000 supernovae independently

Machine learning tools classify 1,000 supernovae automatically

The algorithm helps astronomers sift through detections from the Zwicky transit facility. Credit: California Institute of Technology

Caltech astronomers used a machine learning algorithm to classify 1,000 supernovae completely independently. The algorithm was applied to data captured by the Zwicky Transient Facility, or ZTF, a sky survey instrument based at Caltech’s Palomar Observatory.

says Christopher Frimling, employee astronomer at Caltech and the brains behind new algorithmdubbed SNIascore. “SNIascore ranked the first supernova in April 2021, and after a year and a half, we reach the milestone of 1,000 supernovae.”

The ZTF scans the night sky each night to look for changes called transient events. This includes everything from moving asteroids to star-eating black holes to exploding stars known as supernovae. ZTF sends hundreds of thousands of alerts every night to Astronomy scientists around the world and notify them of these fleeting events. Then astronomers use other telescopes to follow and examine the nature of the changing objects. To date, ZTF data has led to the discovery of thousands of supernovae.

But with relentless amounts of data pouring in every night, ZTF team members can’t sort through all the data themselves.

“The traditional idea of ​​an astronomer sitting in an observatory and sifting through telescope images carries a lot of romance but strays from reality,” says Matthew Graham, project scientist at ZTF and professor of astronomy at Caltech.

A machine learning algorithm has classified 1,000 supernovae completely independently using data captured by ZTF, which is based at Caltech’s Palomar Observatory near San Diego. The empty region in the video at the bottom right represents regions of the southern sky that cannot be seen from Mount Palomar.

Instead, the team developed machine learning algorithms to aid in the searches. They developed a SNIascore for the task of classifying candidate supernovae. Supernovas come in two broad categories: type I and type II. Type I supernovae are devoid of hydrogen, while type II supernovae are rich in hydrogen. The most common type 1 supernova occurs when a massive star steals matter from a neighboring star, triggering a thermonuclear explosion. A type II supernova occurs when a massive star collapses under its own gravity.

Currently, SNIascore can rank what are known as Type Ia supernovae, or “standard candles” in the sky. These are dying stars that explode in a thermonuclear explosion of constant force. Type Ia supernovae allow astronomers to measure the expansion rate of the universe. Fremling and colleagues are currently working to expand the capabilities of this tool algorithm To classify other types of supernovae in the near future.

Each night, after the ZTF picks up flashes in the sky that could be supernovae, it sends the data to a spectrometer at Palomar located in a dome a few hundred meters away, called a SEDM (Spectral Energy Distribution Machine). SNIascore works with SEDM to classify supernovae as likely Type Ia. The upshot is that the ZTF team is quickly building a more reliable data set of supernovae for astronomers to investigate further and eventually learn about the physics of powerful stellar explosions.

SNIascore is remarkably accurate. After 1000 supernovaeWe’ve seen how the algorithm works in the real world, Fremling says. “We have not found clearly misclassified events since their launch in April 2021, and we now plan to implement the same algorithm with other monitoring facilities.”

“This work illustrates well how applications of machine learning in astronomy are evolving in near real time,” adds Ashish Mahapal, who leads ZTF’s machine learning activities and serves as the principal computational and data scientist at Caltech’s Data Driven Discovery Center.

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