星系-星系强引力透镜模拟：跨巡天与多波段的GPU加速
Galaxy-Galaxy Strong Lensing simulation with the GPU acceleration across surveys and multi-bands
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中文总结 AI 辅助
该研究提出一个GPU加速的PyTorch张量模拟框架，通过合成SED和多波段观测参数生成高保真强透镜图像，实现跨巡天联合分析，并比CPU流程快约千倍。
中文摘要 AI 辅助
我们提出一个基于GPU加速、PyTorch张量计算的模拟框架，用于生成高保真度的星系-星系强引力透镜图像。通过整合合成光谱能量分布（SEDs），该流程精确模拟了透镜星系和源星系随红移变化的光度特性，确保多波段观测中的物理一致性。该框架纳入了关键观测参数，包括点扩散函数（PSF）、星等极限和零点，以复现特定巡天条件，从而支持稳健的跨巡天联合分析。作为应用，我们使用相同的透镜模型参数为KiDS、LSST和Euclid模拟多波段图像，并采用深度学习网络评估图像去混叠性能。特别地，该模拟利用PyTorch确保完全自动微分和GPU加速，使其成为需要基于梯度优化（超越标准模型训练）的高级深度学习算法的高效工具。我们的框架相比传统基于CPU的流程实现了约$\mathcal{O}(10^3)$倍的加速，展示了在下一代巡天中进行联合基于梯度透镜建模的潜在可行性。
英文摘要
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 $\mathcal{O}(10^3)$ over traditional CPU-based pipelines, demonstrating the potential feasibility of joint gradient-based lens modeling across next-generation surveys.
