  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.
