Speaker
Alexandra Junell Brown
Description
In time-domain astronomy, rapid classification of astronomical transients is critical for determining candidates for follow-up observations. With the advent of the Vera Rubin Observatory’s Legacy Survey of Space and Time, the backlog of astronomical data will increase by terabytes a night. Machine learning models capable of processing and analyzing large quantities of data can advance the discovery process. Building upon astronet, a python package for classifying transients, this research moves towards a model capable of early-time classification of transients using a time-series transformer.
