标题： 利用连续时间流模型检测弱透镜图中的建模偏差 显示英文标题

标题： Detecting Modeling Bias with Continuous Time Flow Models on Weak Lensing Maps

摘要： 基于仿真的推理提供了一个强大的框架，可以从当前和即将进行的宇宙学调查中的非线性尺度中提取丰富的信息，并确保其稳健性需要对前向模型进行严格的验证。在这项工作中，我们将前向模型验证重新定义为机器学习（ML）-基于仿真的推理（SBI）框架内的分布外（OoD）检测问题。我们采用概率密度作为OoD检测的度量，并比较了各种密度估计技术，表明通过连续时间流模型（CTFM）进行场级概率密度估计显著优于结合散射变换（ST）或卷积神经网络（CNN）与归一化流（NFs）的特征级方法，以及由接收者操作特征曲线下的面积（AUROC）量化得出的基于NF的场级估计器。我们的分析显示，CTFM不仅在检测OoD样本方面表现出色，还提供了模型选择的强大指标。此外，我们验证了CTFM在不同宇宙学下保持了一致的有效性，同时减轻了NF架构中存在的归纳偏差。尽管我们的概念验证研究采用了简化的前向建模和噪声设置，但我们的框架为识别宇宙学数据集中的未知系统性提供了一个有前景的途径。 显示英文摘要

摘要： Simulation-based inference provides a powerful framework for extracting rich information from nonlinear scales in current and upcoming cosmological surveys, and ensuring its robustness requires stringent validation of forward models. In this work, we recast forward model validation as an out-of-distribution (OoD) detection problem within the framework of machine learning (ML)-based simulation-based inference (SBI). We employ probability density as the metric for OoD detection, and compare various density estimation techniques, demonstrating that field-level probability density estimation via continuous time flow models (CTFM) significantly outperforms feature-level approaches that combine scattering transform (ST) or convolutional neural networks (CNN) with normalizing flows (NFs), as well as NF-based field-level estimators, as quantified by the area under the receiver operating characteristic curve (AUROC). Our analysis shows that CTFM not only excels in detecting OoD samples but also provides a robust metric for model selection. Additionally, we verified CTFM maintains consistent efficacy across different cosmologies while mitigating the inductive biases inherent in NF architectures. Although our proof-of-concept study employs simplified forward modeling and noise settings, our framework establishes a promising pathway for identifying unknown systematics in the cosmology datasets.