HOLISMOKES XV. Search for strong gravitational lenses combining ground-based and space-based imaging
arXiv:2411.18694 · doi:10.1051/0004-6361/202453195
Abstract
In the past, researchers have mostly relied on single-resolution images from individual telescopes to detect gravitational lenses. We propose a search for galaxy-scale lenses that, for the first time, combines high-resolution single-band images (in our case the Hubble Space Telescope, HST) with lower-resolution multi-band images (in our case Legacy survey, LS) using machine learning. This methodology aims to simulate the operational strategies that will be employed by future missions, such as combining the images of Euclid and the Rubin Observatory's Legacy Survey of Space and Time (LSST). To compensate for the scarcity of lensed galaxy images for network training, we have generated mock lenses by superimposing arc features onto HST images, saved the lens parameters, and replicated the lens system in the LS images. We test four architectures based on ResNet-18: (1) using single-band HST images, (2) using three bands of LS images, (3) stacking these images after interpolating the LS images to HST pixel scale for simultaneous processing, and (4) merging a ResNet branch of HST with a ResNet branch of LS before the fully connected layer. We compare these architecture performances by creating Receiver Operating Characteristic (ROC) curves for each model and comparing their output scores. At a false-positive rate of , the true-positive rate is 0.41, 0.45, 0.51 and 0.55, for HST, LS, stacked images and merged branches, respectively. Our results demonstrate that models integrating images from both the HST and LS significantly enhance the detection of galaxy-scale lenses compared to models relying on data from a single instrument. These results show the potential benefits of using both Euclid and LSST images, as wide-field imaging surveys are expected to discover approximately 100,000 lenses.
12 pages, 21 figures, submitted to A&A
References in corpus (28)
- The Hubble Ultra Deep Field
- Euclid. I. Overview of the Euclid mission
- The Sloan Lens ACS Survey. V. The Full ACS Strong-Lens Sample
- Multiple Images of a Highly Magnified Supernova Formed by an Early-Type Cluster Galaxy Lens
- Target Selection and Validation of DESI Luminous Red Galaxies
- The Halos of Satellite Galaxies: the Companion of the Massive Elliptical Lens SL2S J08544-0121
- The MUSE Hubble Ultra Deep Field Survey: II. Spectroscopic redshifts and comparisons to color selections of high-redshift galaxies
- The Sloan Digital Sky Survey Quasar Lens Search. I. Candidate Selection Algorithm
- First catalog of strong lens candidates in the COSMOS field
- LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using Convolutional Neural Networks
- Galaxy Zoo: Morphological Classifications for 120,000 Galaxies in HST Legacy Imaging
- Strong lens systems search in the Dark Energy Survey using Convolutional Neural Networks
- HOLISMOKES. VI. New galaxy-scale strong lens candidates from the HSC-SSP imaging survey
- Lensed Type Ia Supernova "Encore" at z=2: The First Instance of Two Multiply-Imaged Supernovae in the Same Host Galaxy
- HOLISMOKES. VIII. High-redshift, strong-lens search in the Hyper Suprime-Cam Subaru Strategic Program
- Shock cooling of a red-supergiant supernova at redshift 3 in lensed images
- Hubble Asteroid Hunter: II. Identifying strong gravitational lenses in HST images with crowdsourcing
- Strong Lensing by Galaxies
- Detecting gravitational lenses using machine learning: exploring interpretability and sensitivity to rare lensing configurations
- Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). VIII. New galaxy-scale lenses from the HSC SSP
- New Strong Gravitational Lenses from the DESI Legacy Imaging Surveys Data Release 9
- Euclid: The Early Release Observations Lens Search Experiment
- Identification of Galaxy-Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
- Spectroscopy of the Supernova H0pe Host Galaxy at Redshift 1.78
- HOLISMOKES -- XI. Evaluation of supervised neural networks for strong-lens searches in ground-based imaging surveys
- HOLISMOKES XIII: Strong-lens candidates at all mass scales and their environments from the Hyper-Suprime Cam and deep learning
- Developing a Victorious Strategy to the Second Strong Gravitational Lensing Data Challenge
- Detecting unresolved lensed SNe Ia in LSST using blended light curves