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

R Legin, C Stone, Y Hezaveh… - arXiv preprint arXiv …, 2022 - arxiv.org
Astrophysics paper astro-ph.IM Suggest

… Alexandre Adam for useful discussions in regards to simulation-based inference methods. … Simulation-Based Inference of Strong Gravitational Lensing Parameters. arXiv e-prints, art. …

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BibTeX

@article{2207.04123v1,
Author        = {Ronan Legin and Connor Stone and Yashar Hezaveh and Laurence Perreault-Levasseur},
Title         = {Population-Level Inference of Strong Gravitational Lenses with Neural
  Network-Based Selection Correction},
Eprint        = {2207.04123v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.IM},
Abstract      = {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.},
Year          = {2022},
Month         = {Jul},
Url           = {http://arxiv.org/abs/2207.04123v1},
File          = {2207.04123v1.pdf}
}

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