Galaxy-Galaxy Strong Lensing simulation with the GPU acceleration across surveys and multi-bands
Abstract
We present a GPU-accelerated, PyTorch tensor-based simulation framework designed to generate high-fidelity galaxy-galaxy strong lensing images. By integrating synthetic Spectral Energy Distribution (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 (PSF), magnitude limits, and zero points, to replicate specific survey conditions, thereby enabling robust cross-survey joint analyses. As an application, we simulate multi-band images for KiDS, 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(103) over traditional CPU-based pipelines, demonstrating the potential feasibility of joint gradient-based lens modeling across next-generation surveys.
Related
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- The suppression of the matter power spectrum: strong feedback from X-ray gas mass fractions, kSZ effect profiles, and galaxy–galaxy lensing
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BibTeX
@misc{zhong2026galaxygalaxy,
title = {{Galaxy-Galaxy Strong Lensing simulation with the GPU acceleration across surveys and multi-bands}},
author = {Fucheng Zhong and Ruibiao Luo and Nicola R. Napolitano and Crescenzo Tortora and Valerio Busillo and Rui Li},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.18180},
eprint = {2609.18180},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.18180},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Galaxy-Galaxy Strong Lensing simulation with the GPU acceleration across surveys and multi-bands
AU - Fucheng Zhong
AU - Ruibiao Luo
AU - Nicola R. Napolitano
AU - Crescenzo Tortora
AU - Valerio Busillo
AU - Rui Li
PY - 2026
DA - 2026/09/16
PB - arXiv
DO - 10.48550/arxiv.2609.18180
UR - https://arxiv.org/abs/2609.18180
AB - We present a GPU-accelerated, PyTorch tensor-based simulation framework designed to generate high-fidelity galaxy-galaxy strong lensing images. By integrating synthetic Spectral Energy Distribution (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 (PSF), magnitude limits, and zero points, to replicate specific survey conditions, thereby enabling robust cross-survey joint analyses. As an application, we simulate multi-band images for KiDS, 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(10^3) over traditional CPU-based pipelines, demonstrating the potential feasibility of joint gradient-based lens modeling across next-generation surveys.
ER -