Abstract

Time domain astronomy, or the study of the dynamic universe on human timescales, stands at the forefront of a revolution fueled by the advent of large surveys. In the past decades we have experienced an unprecedented influx of observations that led to the discovery of exotic transients such as superluminous supernovae and tidal disruption events, as well as electromagnetic counterparts to gravitational wave sources and pair-instability supernova candidates. The upcoming deployment of next-generation survey telescopes, such as the Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope, stands to increase our transient detection capabilities by two orders of magnitude. Development of machine learning techniques will prove to be not only useful, but necessary, to deal with the deluge of data we will obtain from these observatories, promising deeper insights into known cosmic phenomena and the exciting prospect of discovering entirely new classes of transients.