  In this paper we present a two-step neural network model to separate
detections of solar system objects from optical and electronic artifacts in
data obtained with the "Asteroid Terrestrial-impact Last Alert System" (ATLAS),
a near-Earth asteroid sky survey system [arXiv:1802.00879]. A convolutional
neural network [arXiv:1807.10912] is used to classify small "postage-stamp"
images of candidate detections of astronomical sources into eight classes,
followed by a multi-layered perceptron that provides a probability that a
temporal sequence of four candidate detections represents a real astronomical
source. The goal of this work is to reduce the time delay between Near-Earth
Object (NEO) detections and submission to the Minor Planet Center. Due to the
rare and hazardous nature of NEOs [Harris and D'Abramo, 2015], a low false
negative rate is a priority for the model. We show that the model reaches
99.6\% accuracy on real asteroids in ATLAS data with a 0.4\% false negative
rate. Deployment of this model on ATLAS has reduced the amount of NEO
candidates that astronomers must screen by 90%, thereby bringing ATLAS one step
closer to full autonomy.
