Accelerating lensed quasar discovery and modeling with physics-informed variational autoencoders

TL;DR

This paper introduces VariLens, a physics-informed deep learning model that rapidly detects and models strongly lensed quasars, significantly accelerating the discovery process in large astronomical datasets.

Contribution

The paper presents a novel variational autoencoder-based model that integrates image reconstruction, classification, and lens modeling for fast, automated analysis of strong gravitational lenses.

Findings

VariLens achieves rapid lens parameter estimation within milliseconds on a CPU.

Good agreement with traditional models for known lenses, within $2\sigma$ for systems with $\theta_E<3$ arcsecs.

Identified 42 promising new lens candidates from a large astronomical dataset.

Abstract

Strongly lensed quasars provide valuable insights into the rate of cosmic expansion, the distribution of dark matter in foreground deflectors, and the characteristics of quasar hosts. However, detecting them in astronomical images is difficult due to the prevalence of non-lensing objects. To address this challenge, we developed a generative deep learning model called VariLens, built upon a physics-informed variational autoencoder. This model seamlessly integrates three essential modules: image reconstruction, object classification, and lens modeling, offering a fast and comprehensive approach to strong lens analysis. VariLens is capable of rapidly determining both (1) the probability that an object is a lens system and (2) key parameters of a singular isothermal ellipsoid (SIE) mass model -- including the Einstein radius ($\theta_\mathrm{E}$), lens center, and ellipticity -- in just milliseconds using a single CPU. A direct comparison of VariLens estimates with traditional lens modeling for 20 known lensed quasars within the Subaru Hyper Suprime-Cam (HSC) footprint shows good agreement, with both results consistent within $2\sigma$ for systems with $\theta_\mathrm{E}<3$ arcsecs. To identify new lensed quasar candidates, we begin with an initial sample of approximately 80 million sources, combining HSC data with multiwavelength information from various surveys. After applying a photometric preselection aimed at locating $z>1.5$ sources, the number of candidates was reduced to 710,966. Subsequently, VariLens highlights 13,831 sources, each showing a high likelihood of being a lens. A visual assessment of these objects results in 42 promising candidates that await spectroscopic confirmation. These results underscore the potential of automated deep learning pipelines to efficiently detect and model strong lenses in large datasets.