A Two-Stage Deep Learning Detection Classifier for the ATLAS Asteroid Survey

• URL: http://arxiv.org/abs/2101.08912v1
• Date: Fri, 22 Jan 2021 01:35:08 GMT
• Title: A Two-Stage Deep Learning Detection Classifier for the ATLAS Asteroid Survey
• Authors: Amandin Chyba Rabeendran and Larry Denneau
• Abstract summary: We present a two-step neural network model to separate detections of solar system objects from optical and electronic artifacts. We show that the model reaches 99.6% accuracy on real asteroids in ATLAS data with a 0.4% false negative rate.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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