Neural network prediction of model parameters for strong lensing samples from Hyper Suprime-Cam Survey

arXiv (Cornell University)

Abstract

Strong lensing of background galaxies provides important information about the matter distribution around lens galaxies. Traditional modelling of such strong lenses is both time and resource intensive. Fast and automated analysis methods are the need of the hour given large upcoming surveys. In this work, we build and train a simple convolutional neural network with an aim of rapidly predicting model parameters of gravitational lenses. We focus on the inference of the Einstein radius, and ellipticity components of the mass distribution. We train our network on a variety of simulated data with increasing degree of realism and compare its performance on simulated test data in a quantitative manner. We also model 182 gravitational lenses from the HSC survey using {\sc YattaLens} pipeline to infer their model parameters, which allow a benchmark to compare the predictions of the network. Given all considerations, we conclude that the network trained on simulated samples with lensed sources injected in empty HSC cutouts is the most robust, reproducing Einstein radii with an accuracy of about $10-20$ percent, a bias less than 5 percent, and an outlier fraction of the order of 10 percent. We argue in favour of the subtraction of the lens light before modelling the lens mass distribution. Our comparisons of the inferred parameters of 10 HSC lenses previously modelled in the literature, demonstrate agreement on the Einstein radius. However, the ellipticity components from the network as well as the individual modelling methods, seem to have systematic uncertainties beyond the quoted errors.

Authors 7

1. Priyanka Gawade

   Inter-University Centre for Astronomy and Astrophysics

   Affiliation as printed

   Inter-University Centre for Astronomy and Astrophysics , Ganeshkhind , Pune 411007 , India

2. Anupreeta More
3. Surhud More

   Inter-University Centre for Astronomy and Astrophysics · Kavli Institute for the Physics and Mathematics of the Universe

   Affiliation as printed

   Inter-University Centre for Astronomy and Astrophysics , Ganeshkhind , Pune 411007 , India

   Kavli Institute for the Physics and Mathematics of the Universe (WPI) , University of Tokyo , 5-1-5 , Kashiwa , Chiba 277-8583 , Japan

4. Akisato Kimura

   NTT (Japan)

   Affiliation as printed

   NTT Communication Science Laboratories , 3-1 Morinosato-Wakamiya , Atsugi Shi , 243-0198 , Kanagawa , Japan

5. Alessandro Sonnenfeld Aachen Leiden Observatory

   Leiden University · Shanghai Jiao Tong University · Leiden Observatory

   Affiliation as printed

   Department of Astronomy , School of Physics and Astronomy , Shanghai Jiao Tong University , Shanghai 200240 , China

   Leiden Observatory , Leiden University , P.O. Box 9513 , 2300 RA Leiden , The Netherlands

6. Masamune Oguri
7. Naoki Yoshida