Searches and analyses of strong gravitational lenses are challenging due to the rarity and image complexity of these astronomical objects. Next-generation surveys (both ground- and space-based) will provide more opportunities to derive science from these objects, but only if they can be analyzed on realistic time-scales. Currently, these analyses are expensive. In this work, we present a regression analysis with uncertainty estimates using deep learning models to measure four parameters of strong gravitational lenses in simulated Dark Energy Survey data. Using only gri-band images, we predict Einstein Radius, lens velocity dispersion, lens redshift to within 10−15% of truth values and source redshift to 30% of truth values, along with predictive uncertainties. This work helps to take a step along the path of faster analyses of strong lenses with deep learning frameworks.
@article{arxiv.1911.06341,
title = {Deep Learning in Wide-field Surveys: Fast Analysis of Strong Lenses in Ground-based Cosmic Experiments},
author = {Clecio Bom and Jason Poh and Brian Nord and Manuel Blanco-Valentin and Luciana Dias},
journal= {arXiv preprint arXiv:1911.06341},
year = {2019}
}