Periodic Astrometric Signal Recovery through Convolutional Autoencoders

Abstract

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[39] is a low-cost, agile mission concept dedicated to narrow-angle astrometric monitoring of bright binary stars. In particular the mission will be optimised to search for habitable-zone planets around Alpha Centauri AB. If the separation between these two stars can be monitored with sufficient precision, tiny perturbations due to the gravitational tug from an unseen planet can be witnessed and, given the configuration of the optical system, the scale of the shifts in the image plane are about one millionth of a pixel. Image registration at this level of precision has never been demonstrated (to our knowledge) in any setting within science. In this paper we demonstrate that a Deep Convolutional Auto-Encoder is able to retrieve such a signal 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. In future works, all the more realistic sources of noise and systematic effects present in the real-world system will be injected into the simulations.

arXiv commentPreprint version of the manuscript to appear in the Volume "Intelligent Astrophysics" of the series "Emergence, Complexity and Computation", Book eds. I. Zelinka, D. Baron, M. Brescia, Springer Nature Switzerland, ISSN: 2194-7287

References in corpus (13)

• Gaia Data Release 2. Summary of the contents and survey properties
• The Gaia mission
• Understanding the Lomb-Scargle Periodogram
• First Light for GRAVITY: Phase Referencing Optical Interferometry for the Very Large Telescope Interferometer
• A Deep Learning Approach to Structured Signal Recovery
• Taking the Measure of the Universe: Precision Astrometry with SIM PlanetQuest
• Double-blind test program for astrometric planet detection with Gaia
• Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder
• Astrometry and exoplanets in the Gaia era: a Bayesian approach to detection and parameter recovery
• Separating the EoR Signal with a Convolutional Denoising Autoencoder: A Deep-learning-based Method
• High precision astrometry with a diffractive pupil telescope
• Point Spread Function Modelling for Wide Field Small Aperture Telescopes with a Denoising Autoencoder
• Unsupervised feature-learning for galaxy SEDs with denoising autoencoders