ODNet: A Convolutional Neural Network for Asteroid Occultation Detection
This work addresses the need for fast and reliable occultation detection to handle increasing data from the Unistellar network, benefiting astronomers and citizen scientists, though it is incremental as it applies a known CNN method to a specific domain problem.
The authors tackled the problem of detecting asteroid occultations from large-scale citizen telescope data by developing ODNet, a convolutional neural network that processes star image snippets with a reference star to distinguish true occultations from atmospheric artifacts, achieving 91% precision and 87% recall while analyzing three sequences per second.
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, 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 twenty different types of photometric signals. Inputs to the network consists 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 artefacts introduced by poor atmospheric condition. Our Occultation Detection Neural Network (ODNet), can analyze three sequence of stars per second with 91\% of precision and 87\% of recall. The algorithm is sufficiently fast and robust so we can envision incorporating onboard 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.