Holismokes

Abstract

Modeling of strongly gravitationally lensed galaxies is often required in order to use them as astrophysical or cosmological probes. With current and upcoming wide-field imaging surveys, the number of detected lenses is increasing significantly such that automated and fast modeling procedures for ground-based data are urgently needed. This is especially pertinent to short-lived lensed transients in order to plan follow-up observations. Therefore, we present in a companion paper a neural network predicting the parameter values with corresponding uncertainties of a singular isothermal ellipsoid (SIE) mass profile with external shear. In this work, we also present a newly developed pipeline glee auto.py that can be used to model any galaxy-scale lensing system consistently. In contrast to previous automated modeling pipelines that require high-resolution space-based images, glee auto.py is optimized to work well on ground-based images such as those from the Hyper-Suprime-Cam (HSC) Subaru Strategic Program or the upcoming Rubin Observatory Legacy Survey of Space and Time. We further present glee tools.py, a flexible automation code for individual modeling that has no direct decisions and assumptions implemented on the lens system setup or image resolution. Both pipelines, in addition to our modeling network, minimize the user input time drastically and thus are important for future modeling efforts. We applied the network to 31 real galaxy-scale lenses of HSC and compare the results to traditional, Markov-Chain Monte-Carlo sampling-based models obtained from our semi-autonomous pipelines. In the direct comparison, we find a very good match for the Einstein radius. The lens mass center and ellipticity show reasonable agreement. The main discrepancies pretrain to the external shear, as is expected from our tests on mock systems where the neural network always predicts values close to zero for the complex components of the shear. In general, our study demonstrates that neural networks are a viable and ultra fast approach for measuring the lens-galaxy masses from ground-based data in the upcoming era with ∼ 10 5 lenses expected.

Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems Using Recurrent Inference Machines

“…Over the recent years, deep-learning methods have proven extremely successful at accurately modeling strong lensing systems (Hezaveh et al 2017;Perreault-Levasseur et al 2017;Morningstar et al 2018;Coogan et al 2020;Legin et al 2021;Park et al 2021;Wagner-Carena et al 2021;Anau Montel et al 2022;Karchev et al 2022;Legin et al 2022;Mishra-Sharma & Yang 2022;Schmidt et al 2023;Schuldt et al 2023;Wagner-Carena et al 2023). More specifically, Morningstar et al (2019) demonstrated that recurrent convolutional neural networks can implicitly learn complex prior distributions from their training data to reconstruct pixelated undistorted images of strongly lensed sources successfully, circumventing the need to specify explicitly a prior distribution over those parameters.…”