发表机构
Institut de Ciències de l’Espai (ICE, CSIC)（太空科学研究所（空间科学与意识研究所，西班牙国家科学研究委员会））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于暗物质晕的内在取向模型，联合拟合两点及高阶统计量，在FLAMINGO模拟中显著优于NLA类模型，为基于模拟的弱引力透镜推断提供了高效且可移植的IA建模方案。
AI 中文摘要
基于模拟的推断和其他场级弱引力透镜分析需要内在取向（IA）模型，这些模型能够在两点相关函数和非高斯观测量上生成真实的内在椭圆率场。我们开发了一种基于暗物质晕的IA模型，包含独立的中央星系和卫星星系成分，并在FLAMINGO模拟中针对星系内在形状进行了测试。我们联合拟合了涉及IA、引力透镜和密度场的两点及更高阶统计量，包括不同星系内在形状之间的相关性，并与场级非线性取向（NLA）和密度加权NLA（δNLA）模型进行了比较。我们首先在原始FLAMINGO星系的位置上评估模型，固定星系与暗物质之间的联系，从而隔离IA响应。NLA和δNLA在非线性尺度以及更高阶观测量上留下了显著差异，而晕模型则能同时描述所有统计量。随后，我们移除了FLAMINGO星系目录，并使用低维度的晕占据模型从暗物质晕场重新生成源星系群体；即使详细的占据关系仅被近似重现，这种一致性仍然得以保持。该性能的大部分可以通过一个紧凑的四参数可移植模型来保留，并且相同的架构成功迁移到了一个独立的、仅含引力的N体模拟中，该模拟使用了不同的晕定义和形状测量方法。这些结果表明，非线性场级IA建模可以比简单的NLA类模型有显著改进，而无需引入大量干扰参数空间。因此，晕模型为将内在取向纳入基于模拟的弱引力透镜推断提供了一条有前景的途径，但在应用于实际数据之前，还需要在晕质量范围、流体动力学模拟以及实际巡天选择方面进行进一步验证。
英文摘要
Simulation-based inference and other field-level weak-lensing analyses require intrinsic-alignment (IA) models generating realistic intrinsic-ellipticity fields across two-point and non-Gaussian observables. We develop a halo-based IA prescription with separate central and satellite components and test it against intrinsic galaxy shapes in FLAMINGO. We jointly fit two-point and higher-order statistics involving the IA, lensing, and density fields, including correlations between the intrinsic shapes of distinct galaxies, and compare against field-level nonlinear-alignment and density-weighted NLA prescriptions. We first evaluate the models at the positions of the original FLAMINGO galaxies, fixing the galaxy--matter connection and isolating the IA response. NLA and $δ$NLA leave substantial discrepancies on nonlinear scales and across higher-order observables, whereas the halo model provides a simultaneous description of all statistics. We then remove the FLAMINGO galaxy catalogue and regenerate the source population from the halo field using a low-dimensional halo occupation model; the agreement is preserved even when the detailed occupation is only approximately reproduced. Much of this performance can be retained with a compact four-parameter portable model, and the same architecture transfers successfully to an independent gravity-only N-body realization with different halo definitions and shape measurements. These results show that nonlinear field-level IA modelling can be substantially improved over simple NLA-like prescriptions without introducing a large nuisance-parameter space. The halo model therefore provides a promising route for incorporating intrinsic alignments into simulation-based weak-lensing inference, although further validation across halo masses, hydrodynamical simulations, and realistic survey selections will be needed before application to data.
