We seek to remove foreground contaminants from 21cm intensity mapping observations. We demonstrate that a deep convolutional neural network (CNN) with a UNet architecture and three-dimensional convolutions, trained on simulated observations, can effectively separate frequency and spatial patterns of the cosmic neutral hydrogen (HI) signal from foregrounds in the presence of noise. Cleaned maps recover cosmological clustering statistics within 10% at all relevant angular scales and frequencies. This amounts to a reduction in prediction variance of over an order of magnitude on small angular scales ($\ell > 300$), and improved accuracy for small radial scales ($k_{\parallel} > 0.17\ \rm h\ Mpc^{-1})$ compared to standard Principal Component Analysis (PCA) methods. We estimate posterior confidence intervals for the network's prediction by training an ensemble of UNets. Our approach demonstrates the feasibility of analyzing 21cm intensity maps, as opposed to derived summary statistics, for upcoming radio experiments, as long as the simulated foreground model is sufficiently realistic. We provide the code used for this analysis on Github https://github.com/tlmakinen/deep21 as well as a browser-based tutorial for the experiment and UNet model via the accompanying http://bit.ly/deep21-colab Colab notebook.
翻译:我们试图从21厘米的强度绘图观测中去除前景污染物。 我们证明,一个带有UNet结构和三维变相的深卷动神经网络(CNN),经过模拟观测培训,能够有效地将宇宙中性氢信号的频率和空间模式从前景表面与噪音面前分离。清洁的地图在所有相关的角尺度和频率中将宇宙群集统计数据恢复到10%之内。这相当于在小角尺度上减少预测量在数量级上的差异($>300美元),并且提高小辐射尺度(k ⁇ paraillel} > 0.17\rm h\\ m\ Mpc ⁇ -1})的精确度,与标准的主元元元分析方法相比。我们通过培训一个共振式的UNets。我们的方法表明,对21厘米密度地图进行分析的可行性,而不是为即将到来的简要统计数据,只要模拟的地面模型模型是足够现实的。我们提供了用于本次主要构件分析的代码。我们为Gigh/comblibbial 提供了用于这项分析的Gib/ brobbma broma 和Unibly UN 提供了一个数据库。