ODNet: A Convolutional Neural Network for Asteroid Occultation Detection

Abstract

We propose to design and build an algorithm that will use a convolutional neural network (CNN) and observations from the Unistellar Network to reliably detect asteroid occultations. The Unistellar Network is made of more than 10,000 digital telescopes owned by citizen scientists, and is regularly used to record asteroid occultations. In order to process the increasing amount of observational produced by this network, we need a quick and reliable way to analyze occultations. In an effort to solve this problem, we trained a CNN with artificial images of stars with 20 different types of photometric signals. Inputs to the network consist of two stacks of snippet images of stars, one around the star that is supposed to be occulted and a reference star used for comparison. We need the reference star to distinguish between a true occultation and artifacts introduced by a poor atmospheric condition. Our Occultation Detection Neural Network can analyze three sequences of stars per second with 91% precision and 87% recall. The algorithm is sufficiently fast and robust so we can envision incorporating it on board the eVscopes to deliver real-time results. We conclude that citizen science represents an important opportunity for the future studies and discoveries in the occultations, and that application of artificial intelligence will permit us to to take better advantage of the ever-growing quantity of data to categorize asteroids.

Keywords

• Asteroid occultation
• Convolutional neural networks
• Automated telescopes
• Optical astronomy