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arXiv 2406.19367astro-ph.CO
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PUREPath：一种用于估计天空大角尺度上CMB后验的深度潜在变分模型

PUREPath: A Deep Latent Variational Model for Estimating CMB Posterior over Large Angular Scales of the Sky

Vipin Sudevan, Pisin Chen

备注：
Updated Figure 3 and 7, earlier version corresponds to the pixels estimated at healpix nside 32 and smoothed by a gaussian beam of 6 deg. Updated Figure 4 for better clarity on error map Updated Figure 9 with mean cl estimated from cleaned cmb map samples
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AI总结：

PUREPath提出了一种结合贝叶斯统计与变分方法的嵌套概率多模态U-Net架构，用于估计CMB信号后验密度，有效降低前景污染，并提供逐像素的不确定性度量以提升宇宙学参数测量精度。

摘要：

我们提出了一种综合的神经架构PUREPath，它利用嵌套的概率多模态U-Net框架，并在解码器的扩展路径中加入概率ResNet块，以估计以观测的宇宙微波背景（CMB）数据和训练数据集为条件的CMB信号的后验密度。通过无缝整合贝叶斯统计和变分方法，我们的模型有效减少了观测CMB图中的前景污染。该模型使用在Planck LFI和HFI频率通道30-353 GHz下模拟的受前景和噪声污染的CMB温度图进行训练，这些模拟利用了公开的微波背景各向异性代码（CAMB）和Python天空模型（PySM）软件包。在训练期间，我们的模型将模型参数的初始先验分布转换为基于训练数据的后验分布。在推理期间，从模型参数的联合完整后验中，估计出预测性CMB后验以及清洁后CMB图的预测均值、方差等汇总统计量。预测标准差图为预测均值CMB图中的每个像素提供了直接且可解释的不确定性度量。清洁后的CMB图连同误差估计可用于更准确地测量宇宙学参数及其他宇宙学分析。

英文摘要：

We present a comprehensive neural architecture, the PUREPath, which leverages a nested Probabilistic multi-modal U- Net framework, augmented by the inclusion of probabilistic ResNet blocks in the Expanding Pathway of the decoders, to estimate the posterior density of the Cosmic Microwave Background (CMB) signal conditioned on the observed CMB data and the training dataset. By seamlessly integrating Bayesian statistics and variational methods our model effectively minimizes foreground contamination in the observed CMB maps. The model is trained using foreground and noise contaminated CMB temperature maps simulated at Planck LFI and HFI frequency channels 30 - 353 GHz using publicly available Code for Anisotropies in the Microwave Background (CAMB) and Python Sky Model (PySM) packages. During training, our model transforms initial prior distribution on the model parameters to posterior distributions based on the training data. From the joint full posterior of the model parameters, during inference, a predicitve CMB posterior and summary statistics such as the predictive mean, variance etc of the cleaned CMB map is estimated. The predictive standard deviation map provides a direct and interpretable measure of uncertainty per pixel in the predicted mean CMB map. The cleaned CMB map along with the error estimates can be used for more accurate measurements of cosmological parameters and other cosmological analyses.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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