Galaxy–galaxy strong lensing simulation with GPU acceleration across surveys and multi-bands
TL;DR
A GPU-accelerated, PyTorch tensor-based simulation framework designed to generate high-fidelity galaxy–galaxy strong lensing (GGSL) images, demonstrating, for the first time, the feasibility of joint gradient-based lens modeling across next-generation surveys.
Abstract
We present a GPU-accelerated, PyTorch tensor-based simulation framework designed to generate high-fidelity galaxy–galaxy strong lensing (GGSL) images. By integrating synthetic spectral energy distributions (SEDs), the pipeline accurately models the redshift-dependent photometric properties of lens and source galaxies, ensuring physical consistency across multi-band observations. The framework incorporates key observational parameters, including point spread functions (PSFs), magnitude limits, and zero points, to replicate specific survey conditions and enable robust cross-survey joint analyses. As an application, we simulate multi-band images for the Kilo-Degree Survey (KiDS), Legacy Survey of Space and Time (LSST), and Euclid using identical lens model parameters and employ a deep learning network to evaluate image deblending performance. In particular, the simulation leverages PyTorch to ensure full auto-differentiability and GPU acceleration, making it a highly efficient tool for advanced deep learning algorithms that require gradient-based optimization beyond standard model training. Our framework achieves a speedup of approximately O ( 1 0 3 ) over traditional CPU-based pipelines, demonstrating, for the first time, the feasibility of joint gradient-based lens modeling across next-generation surveys.