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We have conducted a search for strong gravitational lensing systems in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys Data Release 10 (DR10). This paper is the fourth in a series of searches (following Huang et al. 2020; Huang et al. 2021; Storfer et al. 2024, Paper I, II, & III respectively). This is the first catalog of lens candidates covering nearly the entirety of the extragalactic sky south of declination δ ≈ +32°, all of it observed by the DECam, covering ~ 14,000 deg². We impose a z-band magnitude cut of < 20 in AB magnitude. We deploy a Residual Neural Network and EfficientNet as an ensemble trained on a compilation of known lensing systems and high-grade candidates as well as non-lenses in the same footprint. The predictions from these two base models are aggregated using a meta-learner. After applying our ensemble to the survey data, we exclude known lenses and candidates, and use our own visual inspection portal to rank images in the top 0.01 percentile of all neural network recommendations. We have found 811 new lens candidates. These include 484 new candidates in the Legacy Surveys DR9 footprint, all parts of which have been searched for strong lenses at least once before, either by our group or others. Combining the discoveries from this work with those from Paper I (335), II (1210), and III (1512), we have discovered a total of 3868 new candidates in the DESI Legacy Surveys.
We present results on extending the strong lens discovery space down to much smaller Einstein radii (θ_E ≲ 0.03′′) and much lower halo mass (M_halo < 10^11M⊙) through the combination of JWST observations and machine learning (ML) techniques. First, we forecast detectable strong lenses with JWST using CosmoDC2 as the lens catalog, and a source catalog down to 29th magnitude. By further incorporating the VELA hydrodynamical simulations of high-redshift galaxies, we simulate strong lenses. We train a ResNet on these images, achieving near-100% completeness and purity for ``conventional" strong lenses (θ_E ≳ 0.5′′), applicable to JWST, HST, the Roman Space Telescope and Euclid VIS. For the first time, we also search for very low halo mass strong lenses (M_halo < 10^11M⊙) in simulations, with θ_E ≪ 0.5′′, down to the best resolution (0.03′′) and depth (10,000 sec) limits of JWST using ResNet. A U-Net model is employed to pinpoint these small lenses in images, which are otherwise virtually impossible for human detection. Our results indicate that JWST can find ∼17/deg^2 such low-halo-mass lenses, with the locations of ∼1.1/deg of these detectable by the U-Net at ∼100% precision (and ∼7.0/deg^2 at a 99.0% precision). To validate our model for finding "conventional" strong lenses, we apply it to HST images, discovering two new strong lens candidates previously missed by human classifiers in a crowdsourcing project (Garvin et al. 2022). This study demonstrates the (potentially "superhuman") advantages of ML combined with current and future space telescopes for detecting conventional, and especially, low-halo-mass strong lenses, which are critical for testing CDM models.
We present the Dark Energy Spectroscopic Instrument (DESI) Strong Lens Foundry. We discovered ∼ 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 fully forward-modeling Bayesian approach (GIGA-Lens), using multiple GPUs, for the first time in both regards, to a strong lens with HST data, or any high resolution imaging.
Over the past few years alone, the lensing community has discovered thousands of strong lens candidates, and spectroscopically confirmed hundreds of them. In this time of abundance, it becomes pragmatic to focus our time and resources on the few extraordinary systems, in order to most efficiently study the universe. In this paper, we present such a system: DESI-090.9854-35.9683, a cluster-scale lens at z_l = 0.49, with seven observed lensed sources around the core, and additional lensed sources further out in the cluster. From the number and the textbook configuration of the lensed images, a tight constraint on the mass potential of the lens is possible. This would allow for detailed analysis on the dark and luminous matter content within galaxy clusters, as well as a probe into dark energy and high-redshift galaxies. We present our spatially resolved kinematic measurements of this system from the Very Large Telescope Multi Unit Spectroscopic Explorer, which confirm five of these source galaxies (in ascending order, at z_s = 0.962, 0.962, 1.166, 1.432, and 1.432). With previous Hubble Space Telescope imaging in the F140W and F200LP bands, we also present a simple two power-law profile flux-based lens model that, for a cluster lens, well models the five lensed arc families with redshifts. We determine the mass to be M(<θ_E) = 4.78×10^13M⊙ for the primary mass potential. From the model, we extrapolate the redshift of one of the two source galaxies not yet spectroscopically confirmed to be at z_s = 4.52 (+1.03−0.71).
We present a pipeline to identify photometric variability within strong gravitationally lensing candidates, in the DESI Legacy Imaging Surveys. In our first paper (Sheu et al. 2023), we laid out our pipeline and presented seven new gravitationally lensed supernovae candidates in a retrospective search. In this companion paper, we apply a modified version of that pipeline to search for gravitationally lensed quasars. From a sample of 5807 strong lenses, we have identified 13 new gravitationally lensed quasar candidates (three of them quadruply-lensed). We note that our methodology differs from most lensed quasar search algorithms that solely rely on the morphology, location, and color of the candidate systems. By also taking into account the temporal photometric variability of the posited lensed images in our search via difference imaging, we have discovered new lensed quasar candidates. While variability searches using difference imaging algorithms have been done in the past, they are typically preformed over vast swathes of sky, whereas we specifically target strong gravitationally lensed candidates. We also have applied our pipeline to 655 known gravitationally lensed quasar candidates from past lensed quasar searches, of which we identify 13 that display significant variability (one of them quadruply-lensed). This pipeline demonstrates a promising search strategy to discover gravitationally lensed quasars in other existing and upcoming surveys.
Storfer et al. 2024: New Strong Gravitational Lenses from the DESI Legacy Imaging Surveys Data Release 9
Storfer et al. 2024: New Strong Gravitational Lenses from the DESI Legacy Imaging Surveys Data Release 9
We have conducted a search for strong gravitational lensing systems in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys Data Release 9. We use a deep residual neural network, trained on a compilation of known lensing systems. We have found 1895 lens candidates. Out of these, 1512 are identified for the first time. Combining the discoveries from this work, Huang et al. 2020, 2021 (335 and 1210 candidates respectively), the total number of strong lens candidates from the Legacy Surveys that we have discovered is 3057.
We present spectroscopic confirmation and lens modeling of the strong lensing system DESI-253.2534+26.8843, discovered in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys data. This system consists of a massive elliptical galaxy surrounded by four blue images forming an Einstein Cross pattern. We obtained spectroscopic observations of this system using the Multi Unit Spectroscopic Explorer (MUSE) on ESO's Very Large Telescope (VLT) and confirmed its lensing nature. The main lens, which is the elliptical galaxy, has a redshift of zL1=0.636±0.001, while the spectra of the background source images are typical of a starburst galaxy and have a redshift of zs=2.597±0.001. Additionally, we identified a faint galaxy foreground of one of the lensed images, with a redshift of zL2=0.386. We employed the GIGA-Lens modeling code to characterize this system and determined the Einstein radius of the main lens to be θE=2.520′′+0.032−0.031, which corresponds to a velocity dispersion of σ = 379 ± 2 km/s. Our study contributes to a growing catalog of this rare kind of strong lensing systems and demonstrates the effectiveness of spectroscopic integral field unit observations and advanced modeling techniques in understanding the properties of these systems.
We develop a pipeline to perform a targeted lensed transient search. We apply this pipeline to 5807 strong lenses and candidates, identified in the literature, in the DESI Legacy Imaging Surveys Data Release 9 (DR9) footprint. For each system, we analyze every exposure in all observed bands (DECam g, r, and z). Our pipeline finds, groups, and ranks detections that are in sufficient proximity temporally and spatially. After the first round of inspection, for promising candidate systems, we further examine the newly available DR10 data (with additional i and Y bands). Here we present our targeted lensed supernova search pipeline and seven new lensed supernova candidates, including a very likely lensed supernova − probably a Type Ia − in a system with an Einstein radius of ~1.5′′.
We conduct a search for strongly lensed quasars in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys (Dey et al. 2019) by applying an autocorrelation algorithm to ~ 5 million objects classified as quasars in the DESI Quasar Sample (Yeche et al. 2020). These systems are visually inspected and ranked. We present 436 new multiply-lensed and binary quasar candidates, 65 of which have redshifts from SDSS DR16. Redshifts are provided for an additional 17 candidates from the SuperNova Integral Field Spectrograph (SNIFS).
We present GIGA-Lens: a gradient-informed, GPU-accelerated Bayesian framework for modeling strong gravitational lensing systems, implemented in TensorFlow and JAX. The robustness, speed, and scalability offered by this framework make it possible to model the large number of strong lenses found in current surveys and present a very promising prospect for the modeling of O(10^5) lensing systems expected to be discovered in the era of the Vera C. Rubin Observatory, Euclid, and the Nancy Grace Roman Space Telescope.
We search in the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys for new strong lensing systems by using deep residual neural networks, building on previous work presented in Huang et al. 2020. After applying our trained neural networks to the survey data, we visually inspect and rank images with probabilities above a threshold. Here we present 1210 new strong lens candidates.
Sixteen of the 1210 new strong gravitational lens candidates in Huang et al. 2021
We have performed a semi-automated search for strong gravitational lensing systems in the 9,000 deg2 Dark Energy Camera Legacy Surveys (DECaLS), part of the DESI Legacy Imaging Surveys. We adopted the deep residual neural network architecture developed by Lanusse et al.. We compiled a training sample that consists of observed non-lenses and known lensing systems. In this paper we present 335 candidate strong lensing systems, identified for the first time.
Five of the 335 new strong gravitational lens candidates in Huang et al. 2020
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