Performant astronomical image processing with Python

Abstract

Image processing is fundamental to observational astronomy workflows. Astronomers acquire imaging data, and process the imagery to extract useful information. This thesis introduces two new image processing algorithms. The first, PyTorchDIA, is a GPU-accelerated approach to Difference Image Analysis (DIA). The approach is fast, without sacrificing modelling flexibility. It makes use of the Pythonic, PyTorch machine learning framework to accelerate convolution computations on the GPU, and compute gradients of user-specified objective functions with automatic differentiation methods to fit DIA models quickly and accurately. The second algorithm, The Thresher, was designed as a new tool to extracting information from Lucky Imaging (LI) data sets. We adopt a modelling approach which optimises a justifiable, physically motivated likelihood function to return the best estimate of the observed astronomical scene. It does this using all available data, and the more data the model is fit to, the better the signal-to-noise and resolution of the scene estimate. This fundamentally differs from conventional shift-and-add procedures, which typically reject the vast majority of the acquired LI data, as in these approaches, the signal-to-noise of the final coadd is inversely related to its resolution. With an eye to accessibility, integration into workflows and open science, the code for these two algorithms has been open sourced. Lastly, we show how Python image processing applications can be used to realise time-critical, demanding computational challenges in a chapter outlining the results of a novel pilot study to detect the occultations of background stars by small, outer solar system objects with high frame-rate sCMOS cameras.