According to NASA, a supernova is the biggest explosion humans have ever seen.

Data is captured every night by the Zwicky Transient Facility or ZTF, a sky survey instrument based at Caltech’s Palomar Observatory, which scans the skies looking for changes (transient events) that could be of interest to astronomers. These alerts have led to the discovery of thousands of supernovae, but the sheer volume of data from ZTF makes its classification and analysis very difficult for humans. (Caltech)

The most common (Type I) supernova occurs when matter flows from a companion on to a white dwarf star, which triggers a thermonuclear explosion. A Type II supernova occurs when a massive star collapses under its own gravity.

Supernovae: AI is the solution

Astronomers at Caltech have deployed an AI algorithm, SNlascore, that could classify 1000 supernovae autonomously.

SNlascore can currently classify Type la supernovae (termed “standard candles”) which occur due to the dying of a star. In addition, these explosions allows astronomers to measure the expansion of the universe. The algo works in tandem with a spectrograph at Palomar that is housed in a dome just few hundred meters away, called the SEDM (Spectral Energy Distribution Machine). The two systems identify Type la supernovae for further analysis by astronomers.

“SNIascore is remarkably accurate. After 1,000 supernovae, we have seen how the algorithm performs in the real world,” says Christoffer Fremling, a staff astronomer at Caltech and the mastermind behind the new algorithm. “We have found no clearly misclassified events since launching back in April 2021, and we are now planning to implement the same algorithm with other observing facilities.”

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