The intrinsic alignments (IA) of galaxies, a key contaminant in weak lensing analyses, arise from correlations in galaxy shapes driven by tidal interactions and galaxy formation processes. Accurate IA modeling is essential for robust cosmological inference, but current approaches rely on perturbative methods that break down on nonlinear scales or on expensive simulations. We introduce IAEmu, a neural network-based emulator that predicts the galaxy position-position ($ξ$), position-orientation ($ω$), and orientation-orientation ($η$) correlation functions and their uncertainties using mock catalogs based on the halo occupation distribution (HOD) framework. Compared to simulations, IAEmu achieves ~3% average error for $ξ$ and ~5% for $ω$, while capturing the stochasticity of $η$ without overfitting. The emulator provides both aleatoric and epistemic uncertainties, helping identify regions where predictions may be less reliable. We also demonstrate generalization to non-HOD alignment signals by fitting to IllustrisTNG hydrodynamical simulation data. As a fully differentiable neural network, IAEmu enables $\sim$10,000$\times$ speed-ups in mapping HOD parameters to correlation functions on GPUs, compared to CPU-based simulations. This acceleration facilitates inverse modeling via gradient-based sampling, making IAEmu a powerful surrogate model for galaxy bias and IA studies with direct applications to Stage IV weak lensing surveys.

翻译：星系的内禀排列（IA）是弱引力透镜分析中的关键污染源，源于潮汐相互作用和星系形成过程驱动的星系形状相关性。精确的IA建模对于稳健的宇宙学推断至关重要，但现有方法依赖于在线性尺度上失效的微扰方法或计算昂贵的模拟。我们提出了IAEmu，一种基于神经网络的模拟器，它利用基于晕占据分布（HOD）框架的模拟星表，预测星系位置-位置（ξ）、位置-取向（ω）和取向-取向（η）相关函数及其不确定性。与模拟相比，IAEmu对ξ的平均误差约为3%，对ω约为5%，同时在不发生过拟合的情况下捕捉η的随机性。该模拟器提供偶然性和认知性不确定性，有助于识别预测可能不可靠的区域。我们还通过拟合IllustrisTNG流体动力学模拟数据，展示了其对非HOD排列信号的泛化能力。作为一个完全可微分的神经网络，IAEmu在GPU上将HOD参数映射到相关函数的速度比基于CPU的模拟快约10,000倍。这种加速通过基于梯度的采样促进了逆向建模，使IAEmu成为星系偏置和IA研究中强大的代理模型，可直接应用于第四阶段弱引力透镜巡天。