• Corpus ID: 245117553

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… 

Figures from this paper

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

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

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

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.

References

SHOWING 1-10 OF 30 REFERENCES

Hierarchical Inference with Bayesian Neural Networks: An Application to Strong Gravitational Lensing

This work incorporates BNNs with flexible posterior parameterizations into a hierarchical inference framework that allows for the reconstruction of population hyperparameters and removes the bias introduced by the training distribution.

Targeted Likelihood-Free Inference of Dark Matter Substructure in Strongly-Lensed Galaxies

A new analysis pipeline is presented that tackles diverse challenges in optical images of galaxy-galaxy strong gravitational lensing systems by bringing together many recent machine learning developments in one coherent approach, including variational inference, Gaussian processes, differentiable probabilistic programming, and neural likelihood-to-evidence ratio estimation.

Fast automated analysis of strong gravitational lenses with convolutional neural networks

The use of deep convolutional neural networks are reported to be used to estimate lensing parameters in an extremely fast and automated way, circumventing the difficulties that are faced by maximum likelihood methods.

Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning

The subtle and unique imprint of dark matter substructure on extended arcs in strong-lensing systems contains a wealth of information about the properties and distribution of dark matter on small

The population of galaxy-galaxy strong lenses in forthcoming optical imaging surveys

Ongoing and future imaging surveys represent significant improvements in depth, area, and seeing compared to current data sets. These improvements offer the opportunity to discover up to three orders

Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing

The results suggest that the application of approximate Bayesian neural networks to astrophysical modeling problems can be a fast alternative to Monte Carlo Markov Chains, allowing orders of magnitude improvement in speed.

Fast likelihood-free cosmology with neural density estimators and active learning

NDEs are used to learn the likelihood function from a set of simulated datasets, with active learning to adaptively acquire simulations in the most relevant regions of parameter space on-the-fly, demonstrating the approach on a number of cosmological case studies.

DETECTION OF LENSING SUBSTRUCTURE USING ALMA OBSERVATIONS OF THE DUSTY GALAXY SDP.81

We study the abundance of substructure in the matter density near galaxies using ALMA Science Verification observations of the strong lensing system SDP.81. We present a method to measure the

The Sloan Lens ACS Survey. V. The Full ACS Strong-Lens Sample

We present the definitive data for the full sample of 131 strong gravitational lens candidates observed with the Advanced Camera for Surveys (ACS) aboard the Hubble Space Telescope by the Sloan Lens

CLASH: THREE STRONGLY LENSED IMAGES OF A CANDIDATE z ≈ 11 GALAXY

We present a candidate for the most distant galaxy known to date with a photometric redshift of z = 10.7+0.6−0.4 (95% confidence limits; with z < 9.5 galaxies of known types ruled out at 7.2σ). This