Machine Learning Algorithm Accurately Classifies 1,000 Type Ia Supernovae
A machine learning algorithm named SNIascore was employed by astronomers at the California Institute of Technology (Caltech) to classify 1,000 supernovas caused by dying stars. The algorithm was developed to assist in the monumental task of sifting through the vast amount of data generated by the Zwicky Transient Facility (ZTF), a sky survey instrument that scans the night sky for short-lived or transient events such as supernovas, black holes, and asteroids. SNIascore works with the Spectral Energy Distribution Machine (SEDM) to classify supernovas that fit into the Type Ia category, which can be used to measure cosmic distances and gauge the expansion rate of the universe.
