用可调尺度的神经压缩方法，检测宇宙学模型在不同尺度上的偏差。
Detecting Model Misspecification in Cosmology with Scale-Dependent Normalizing Flows
- 基于平滑尺度条件的神经统计量压缩数据，提升信息保留率。
- 在三个CAMELS模拟中成功识别出不同物理假设下的模型失效点。
- 适合做宇宙学模拟验证与理论模型诊断的研究者使用。
当前及未来的宇宙学巡天将产生前所未有的高维数据，需要复杂的高保真前向模拟来准确建模物理过程和系统效应。然而，验证理论模型是否真实描述观测数据仍是根本挑战。此外，如何选择既能保留全部宇宙学信息又降低数据维度的表示方式也增加了难度。本文提出一种新框架，结合尺度依赖的神经摘要统计量与归一化流，通过贝叶斯证据估计检测宇宙学模拟中的模型误设。通过将神经网络的数据压缩与证据估计过程按平滑尺度条件化，实现数据驱动地系统性识别理论模型在不同尺度上的失效位置。首次应用该方法于三个包含不同亚网格物理实现的CAMELS模拟中的物质与气体密度场，验证了其有效性。
原文摘要 · Abstract (English)
Current and upcoming cosmological surveys will produce unprecedented amounts of high-dimensional data, which require complex high-fidelity forward simulations to accurately model both physical processes and systematic effects which describe the data generation process. However, validating whether our theoretical models accurately describe the observed datasets remains a fundamental challenge. An additional complexity to this task comes from choosing appropriate representations of the data which retain all the relevant cosmological information, while reducing the dimensionality of the original dataset. In this work we present a novel framework combining scale-dependent neural summary statistics with normalizing flows to detect model misspecification in cosmological simulations through Bayesian evidence estimation. By conditioning our neural network models for data compression and evidence estimation on the smoothing scale, we systematically identify where theoretical models break down in a data-driven manner. We demonstrate a first application to our approach using matter and gas density fields from three CAMELS simulation suites with different subgrid physics implementations.
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