Exploring the Sub-Second Transient Sky with Continuous-Readout Images and Neural Networks

With the novel application of deep learning models to continuously-exposed astronomical data, we are creating tools to discover and reveal the nature of rapidly-evolving optical astrophysical phenomena. Phenomena that vary on this timescale include cataclysmic variables, blazars, occultations by solar system objects, and potentially the optical counterpart of Fast Radio Bursts. Yet, the evolution of optical astronomical phenomena at sub-second timescales is under-explored due to technical limitations in traditional observing modes which require seconds-to-minutes exposure and readout cycles. However, the nontraditional "continuous-readout" mode enables resolution at sub-second timescales by integrating the images of astrophysical objects along one spatial dimension. Analyses of these data require custom pipelines. We are developing neural networks for the analysis of a 450GB continuous-readout astronomical dataset sampled at 300 Hz from the Zwicky Transient Facility (ZTF). This poster will show the performance of CNN and transformer models on the ZTF data and outline our potential for detecting and analyzing rapid transients at scale.