Uncovering 1,000 Supernovae: Machine Learning Algorithm Revolutionizes Astronomy

Machine learning tools automatically classify 1,000 supernovae
Caltech astronomers have classified 1,000 supernovae using a machine-learning algorithm. The algorithm was used to classify data collected by the Zwicky Transient Facility (ZTF), a sky survey tool located at Caltech’s Palomar observatory.

Christoffer Fremling is a Caltech staff astronomer and the creator of the SNIascore algorithm. \”SNIascore classified its first supernova on April 20, 2021. A year and a quarter later, we have reached a milestone of 1,000 supernovae.\”

ZTF scans night skies to detect transient events. It includes everything from black holes that just ate stars to supernovae, exploding star explosions. ZTF notifies astronomers all over the world of these transient events by sending out hundreds of thousands alerts per night. The astronomers use other telescopes in order to investigate and follow-up on the objects that are changing. ZTF data has led to thousands of supernovae being discovered.