Machine learning tools autonomously classify 1000 supernovae

Many current and exciting scientific questions that astronomers are trying to answer require them to collect large samples of different cosmic events. As a result, modern astronomical observatories have become relentless data-generating machines that throw thousands of alerts and images at astronomers every night. Using a machine learning algorithm, astronomers from the Zwicky Transient Facility collaboration at Caltech successfully classified 1000 supernovae autonomously. The algorithm was applied to data captured by the Zwicky Transient Facility, or ZTF, a sky survey instrument based at Caltech's Palomar Observatory. Every night, ZTF analyses the night sky for alterations known as transient events.