LeMoN: Lens Modelling with Neural networks -- I. Automated modelling of strong gravitational lenses with Bayesian Neural Networks

Abstract

The unprecedented number of gravitational lenses expected from new-generation facilities such as the ESA Euclid telescope and the Vera Rubin Observatory makes it crucial to rethink our classical approach to lens-modelling. In this paper, we present LeMoN (Lens Modelling with Neural networks): a new machine-learning algorithm able to analyse hundreds of thousands of gravitational lenses in a reasonable amount of time. The algorithm is based on a Bayesian Neural Network: a new generation of neural networks able to associate a reliable confidence interval to each predicted parameter. We train the algorithm to predict the three main parameters of the Singular Isothermal Ellipsoid model (the Einstein radius and the two components of the ellipticity) by employing two simulated datasets built to resemble the imaging capabilities of the Hubble Space Telescope and the forthcoming Euclid satellite. In this work, we assess the accuracy of the algorithm and the reliability of the estimated uncertainties by applying the network to several simulated datasets of 10.000 images each. We obtain accuracies comparable to previous studies present in the current literature and an average modelling time of just 0.5s per lens. Finally, we apply the LeMoN algorithm to a pilot dataset of real lenses observed with HST during the SLACS program, obtaining unbiased estimates of their SIE parameters. The code is publicly available on GitHub (https://github.com/fab-gentile/LeMoN).

Submitted to MNRAS; The code will be available on GitHub upon acceptance of the paper

References in corpus (32)

• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• A Bayesian approach to strong lensing modelling of galaxy clusters
• COSMOS2020: A panchromatic view of the Universe to $z\sim 10$ from two complementary catalogs
• The Sloan Lens ACS Survey. V. The Full ACS Strong-Lens Sample
• Bayesian Strong Gravitational-Lens Modeling on Adaptive Grids: Objective Detection of Mass Substructure in Galaxies
• Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks
• Inference of the Cold Dark Matter substructure mass function at z=0.2 using strong gravitational lenses
• The Sloan Digital Sky Survey Quasar Lens Search. I. Candidate Selection Algorithm
• The SL2S Galaxy-scale Lens Sample. V. Dark Matter Halos and Stellar IMF of Massive Early-type Galaxies out to Redshift 0.8
• Classifying Radio Galaxies with Convolutional Neural Network
• Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
• Dark matter halos of massive elliptical galaxies at $z\sim 0.2$ are well described by the Navarro-Frenk-White profile
• LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using Convolutional Neural Networks
• The Sloan Lens ACS Survey. XIII. Discovery of 40 New Galaxy-Scale Strong Lenses
• New high-quality strong lens candidates with deep learning in the Kilo Degree Survey
• An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
• Automated galaxy-galaxy strong lens modelling: no lens left behind
• Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing
• The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples
• Line-of-sight effects in strong gravitational lensing
• Deep Learning for Strong Lensing Search: Tests of the Convolutional Neural Networks and New Candidates from KiDS DR3
• HOLISMOKES -- IV. Efficient mass modeling of strong lenses through deep learning
• Up to 100,000 reliable strong gravitational lenses in future dark energy experiments
• Statistical strong lensing. I. Constraints on the inner structure of galaxies from samples of a thousand lenses
• GIGA-Lens: Fast Bayesian Inference for Strong Gravitational Lens Modeling
• Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses
• Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant
• Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting
• The Very Knotty Lenser: exploring the role of regularization in source and potential reconstructions using Gaussian Process Regression
• Statistical strong lensing. II. Cosmology and galaxy structure with time-delay lenses
• HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images
• Discovery of two Einstein crosses from massive post--blue nugget galaxies at z>1 in KiDS

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