We have conducted a search for new strong gravitational lensing systems in the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Release 8. We use deep residual neural networks, building on previous work presented by Huang et al. These surveys together cover approximately one-third of the sky visible from the Northern Hemisphere, reaching a z-band AB magnitude of ∼22.5. We compile a training sample that consists of known lensing systems as well as non-lenses in the Legacy Surveys and the Dark Energy Survey. 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.Unified Astronomy Thesaurus concepts: Strong gravitational lensing (1643); High-redshift galaxies (734); AGN host galaxies (2017); Galaxies (573); Galaxy clusters (584); Galaxy groups (597); Quasars (1319)

An important step in today's Integrated Circuit (IC) manufacturing is optical proximity correction (OPC). In model based OPC, masks are systematically modified to compensate for the non-ideal optical and process effects of optical lithography system. The polygons in the layout are fragmented, and simulations are performed to determine the image intensity pattern on the wafer. If the simulated pattern on the wafer does not match the desired one, the mask is perturbed by moving the fragments.This iterative process continues until the pattern on the wafer matches the desired one. Although OPC increases the fidelity of pattern transfer to the wafer, it is quite CPU intensive due to the simulations performed at each iteration. In this paper, linear regression techniques from statistical learning are used to predict the fragment movements. The goal is to reduce the number of iterations required in model based OPC by using a fast, computationally efficient linear regression solution as the initial guess to model based OPC. Experimental results show that fragment movement predictions via linear regression model significantly decrease the number of iterations required in model based OPC, thereby decreasing the product development time in I.C. design and manufacturing.

Variational Quantum Algorithms (VQAs) are a promising approach for practical applications like chemistry and materials science on near-term quantum computers as they typically reduce quantum resource requirements. However, in order to implement VQAs, an efficient classical optimization strategy is required. Here we present a new stochastic gradient descent method using an adaptive number of shots at each step, called the global Coupled Adaptive Number of Shots (gCANS) method, which improves on prior art in both the number of iterations as well as the number of shots required. These improvements reduce both the time and money required to run VQAs on current cloud platforms. We analytically prove that in a convex setting gCANS achieves geometric convergence to the optimum. Further, we numerically investigate the performance of gCANS on some chemical configuration problems. We also consider finding the ground state for an Ising model with different numbers of spins to examine the scaling of the method. We find that for these problems, gCANS compares favorably to all of the other optimizers we consider.

We have conducted a search for strong gravitational lensing systems in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys Data Release 9. This is the third paper in a series (following Huang et al. 2020; Huang et al. 2021, Paper I & II, respectively). These surveys together cover ∼ 19,000 deg 2 visible from the northern hemisphere, reaching a z-band AB magnitude of ∼ 22.5. We use a deep residual neural network, trained on a compilation of known lensing systems and candidates as well as non-lenses in the same footprint. After applying our trained neural networks to the survey data, we visually inspect and rank images with probabilities above a threshold. We have found 1895 lens candidates. Out of these, 1512 are identified for the first time.Combining the discoveries from this work, Paper I (335) and II (1210), the total number of strong lens candidates from the Legacy Surveys that we have discovered is 3057.

We present GIGA-Lens: a gradient-informed, GPU-accelerated Bayesian framework for modeling strong gravitational lensing systems, implemented in TensorFlow and JAX. The three components, optimization using multistart gradient descent, posterior covariance estimation with variational inference, and sampling via Hamiltonian Monte Carlo, all take advantage of gradient information through automatic differentiation and massive parallelization on graphics processing units (GPUs). We test our pipeline on a large set of simulated systems and demonstrate in detail its high level of performance. The average time to model a single system on four Nvidia A100 GPUs is 105 s. 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  ( 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.