The sensitivity limits of space telescopes are imposed by uncalibrated errors in the point spread function, photon-noise, background light, and detector sensitivity. These are typically calibrated with specialized wavefront sensor hardware and with flat fields obtained on the ground or with calibration sources, but these leave vulnerabilities to residual time-varying or non-common path aberrations and variations in the detector conditions. It is, therefore, desirable to infer these from science data alone, facing the prohibitively high dimensional problems of phase retrieval and pixel-level calibration. We introduce a new Python package for physical optics simulation, ∂ Lux, which uses the machine learning framework Jax to achieve graphics processing unit acceleration and automatic differentiation (autodiff), and apply this to simulating astronomical imaging. In this first of a series of papers, we show that gradient descent enabled by autodiff can be used to simultaneously perform phase retrieval and calibration of detector sensitivity, scaling efficiently to inferring millions of parameters. This new framework enables high dimensional optimization and inference in data analysis and hardware design in astronomy and beyond, which we explore in subsequent papers in this series.
∂Lux is a newly developed optical modelling framework deigned to harness the tools underpinning the modern machine learning revolution and directly apply them to optics. Both neural networks and optical systems map an input vector to some output vector employing a series of intermediary linear transformations and nonlinear matrix operations. This isomorphism allows for optical models to be directly constructed within existing automatic differentiation libraries. ∂Lux exploits this relationship harnessing automatic differentiation libraries to create a naively end-to-end fully differentiable optical modelling framework. This may precipitate a paradigm shift in the power and utility of these optical models, opening the possibility to entirely novel algorithms and approaches. This manuscript explores some of the many ways to harness the potential of these codes, particularly focusing on the application example provided by the Toliman space telescope mission.
Although discovery technologies are now populating exoplanet catalogs into the thousands, contemporary astronomy is poorly equipped to find the most compelling exoplanetary real-estate: earth-analog systems within our immediate solar neighbourhood. The TOLIMAN space telescope program aims to develop low-cost, agile mission concepts dedicated to astrometric detection of exoplanets within 10PC, and in particularly targeting the Alpha Cen system. It accomplishes this by deploying an innovative optical and signal encoding architecture that targets the most promising technique for this critical stellar sample: high precision astrometric monitoring. Two pathfinder missions, the first a cubesat slated for 2021 launch, and the second a 10cm space telescope under development at the University of Sydney. We will present an overview of the family of missions and the novel technologies underlying the signal detection strategy.
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