Population-Level Inference of Strong Gravitational Lenses with Neural Network-Based Selection Correction

Ronan Legin, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur

Submitted on 8 July 2022


A new generation of sky surveys is poised to provide unprecedented volumes of data containing hundreds of thousands of new strong lensing systems in the coming years. Convolutional neural networks are currently the only state-of-the-art method that can handle the onslaught of data to discover and infer the parameters of individual systems. However, many important measurements that involve strong lensing require population-level inference of these systems. In this work, we propose a hierarchical inference framework that uses the inference of individual lensing systems in combination with the selection function to estimate population-level parameters. In particular, we show that it is possible to model the selection function of a CNN-based lens finder with a neural network classifier, enabling fast inference of population-level parameters without the need for expensive Monte Carlo simulations.


Comment: 8 pages, 5 figures, accepted at the ICML 2022 Workshop on Machine Learning for Astrophysics

Subjects: Astrophysics - Instrumentation and Methods for Astrophysics; Astrophysics - Cosmology and Nongalactic Astrophysics