DESI Strong Lens Foundry I: HST Observations and Modeling with GIGA-Lens

TL;DR

This paper introduces the DESI Strong Lens Foundry, discovering thousands of new lens candidates, confirming a subset with HST, and demonstrating a novel Bayesian lens modeling approach using GIGA-Lens with high-resolution imaging.

Contribution

It presents the discovery of approximately 3500 strong lens candidates, confirms a subset with HST, and showcases the first application of GPU-accelerated Bayesian modeling with GIGA-Lens on high-resolution data.

Findings

Confirmed all 51 HST candidates as strong lenses.

Demonstrated the first GPU-based Bayesian lens modeling with GIGA-Lens.

Provided initial lens models and redshift measurements for selected candidates.

Abstract

We present the Dark Energy Spectroscopic Instrument (DESI) Strong Lens Foundry. We discovered $\sim 3500$ new strong gravitational lens candidates in the DESI Legacy Imaging Surveys using residual neural networks (ResNet). We observed a subset (51) of our candidates using the Hubble Space Telescope (HST). All of them were confirmed to be strong lenses. We also briefly describe spectroscopic follow-up observations by DESI and Keck NIRES programs. From this very rich dataset, a number of studies will be carried out, including evaluating the quality of the ResNet search candidates and lens modeling. In this paper, we present our initial effort in these directions. In particular, as a demonstration, we present the lens model for DESI-165.4754-06.0423, with imaging data from HST, and lens and source redshifts from DESI and Keck NIRES, respectively. In this effort, we have applied a \emph{fully} forward-modeling Bayesian approach (GIGA-Lens), using \emph{multiple} GPUs, for the first time in both regards, to a strong lens with HST data, or any high resolution imaging.