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:以模拟为基础的推论(SBI)正在迅速建立,成为分析宇宙学调查数据的标准机器学习技术。尽管通过所学模型不断提高密度估计的质量,但将这种技术应用于实际数据完全依赖于在培训分布之外(大多没有限制)的神经网络的普及能力。由于科学家创造的模拟的不完善,以及产生所有可能的参数组合的计算费用巨大,履行机构在宇宙学中的方法很容易受到这种概括问题的影响。在这里,我们讨论这两个问题的效果,并表明如何利用贝耶斯神经网络框架来培训履行机构来减少偏见,并在培训组之外产生更可靠的推论。我们引入宇宙合成系统,这是对宇宙学的首次应用斯托切斯特光能对宇宙学的推论,并应用到经培训的履行机构关于宇宙微波背景的推论。