# Simulation-Based Inference of Strong Gravitational Lensing Parameters

@inproceedings{Legin2021SimulationBasedIO, title={Simulation-Based Inference of Strong Gravitational Lensing Parameters}, author={Ronan Legin and Yashar D. Hezaveh and Laurence Perreault Levasseur and Benjamin Dan Wandelt}, year={2021} }

In the coming years, a new generation of sky surveys, in particular, Euclid Space Telescope (2022), and the Rubin Observatory’s Legacy Survey of Space and Time (LSST, 2023) will discover more than 200,000 new strong gravitational lenses, which represents an increase of more than two orders of magnitude compared to currently known sample sizes [1]. Accurate and fast analysis of such large volumes of data under a statistical framework is therefore crucial for all sciences enabled by strong…

## 3 Citations

### Strong Lensing Parameter Estimation on Ground-Based Imaging Data Using Simulation-Based Inference

- Physics
- 2022

Current ground-based cosmological surveys, such as the Dark Energy Survey (DES), are predicted to discover thousands of galaxy-scale strong lenses, while future surveys, such as the Vera Rubin…

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

- Computer Science
- 2022

This work shows that it is possible to model the selection function of a CNN-based lens with a neural network classer, enabling fast inference of population-level parameters without the need for expensive Monte Carlo simulations.

### Machine Learning and Cosmology

- Computer ScienceArXiv
- 2022

Current and ongoing developments relating to the application of machine learning within cosmology and a set of recommendations aimed at maximizing the scientific impact of these burgeoning tools over the coming decade through both technical development as well as the fostering of emerging communities are summarized.

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