Automating strong galaxy-scale gravitational lens modelling with neural networks - Pearson, Christopher James (2022)
Strong galaxy-scale gravitational lensing provides a powerful means of studying galaxy formation, constraining cosmology and understanding the evolution of large-scale structure. The presence of a foreground lensing galaxy deflects the light rays of a background source into multiple images, with their configuration dependent upon the lens mass distribution. Modelling of this distribution to reproduce the lensed images not only aids in measuring the dark matter content of the foreground galaxy, but allows for the reconstruction of the background source in order to study high-redshift galaxy populations. Modelling is typically performed by relatively slow parametric parameter-fitting techniques requiring manual inspection. However, upcoming large-scale surveys like the Legacy Survey of Space and Time (LSST) and Euclid will discover tens of thousands of strong lenses, with this vast quantity driving the growing use of automated machine learning for both identifying and modelling such systems extremely quickly. Convolutional neural networks (CNNs) can extract information from lensed images in order to predict parameters of the lens mass model, but require large simulated data sets for training and testing.