Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning technique for analyzing data in cosmological surveys. Despite continual improvements to the quality of density estimation by learned models, applications of such techniques to real data are entirely reliant on the generalization power of neural networks far outside the training distribution, which is mostly unconstrained. Due to the imperfections in scientist-created simulations, and the large computational expense of generating all possible parameter combinations, SBI methods in cosmology are vulnerable to such generalization issues. Here, we discuss the effects of both issues, and show how using a Bayesian neural network framework for training SBI can mitigate biases, and result in more reliable inference outside the training set. We introduce cosmoSWAG, the first application of Stochastic Weight Averaging to cosmology, and apply it to SBI trained for inference on the cosmic microwave background.

翻译：模拟推断（SBI）正迅速成为宇宙学巡天数据分析的标准机器学习技术。尽管学习模型在密度估计质量上持续改进，此类技术在真实数据中的应用完全依赖于神经网络在训练分布之外的泛化能力，而这基本不受约束。由于科学家构建的模拟存在缺陷，且生成所有可能参数组合的计算成本高昂，宇宙学中的SBI方法易受此类泛化问题的影响。本文讨论了这两种问题的影响，并展示了如何利用贝叶斯神经网络框架训练SBI来减轻偏差，从而在训练集之外实现更可靠的推断。我们引入了cosmoSWAG——随机权重平均在宇宙学中的首次应用，并将其应用于针对宇宙微波背景推断训练的SBI。