Periodic Astrometric Signal Recovery through Convolutional Autoencoders

Astrometric detection involves a precise measurement of stellar positions, and is widely regarded as the leading concept presently ready to find earth-mass planets in temperate orbits around nearby sun-like stars. The TOLIMAN space telescope is a low-cost, agile mission concept dedicated to narrow-angle astrometric monitoring of bright binary stars.In this paper we demonstrate that a Deep Convolutional Auto-Encoder is able to detected signals from simplified simulations of the TOLIMAN data and we present the full experimental pipeline to recreate out experiments from the simulations to the signal analysis.