标题： 卷积深度去噪自编码器用于射电天文图像 显示英文标题

标题： Convolutional Deep Denoising Autoencoders for Radio Astronomical Images

摘要： 我们应用一种称为卷积去噪自动编码器的机器学习技术，对最先进的射电望远镜的合成图像进行去噪，目的是检测预测会表征射电宇宙网的微弱、弥散的射电源。在我们的应用中，去噪旨在同时减少随机仪器噪声和最小化附加的虚假伪影，如孔径综合技术产生的旁瓣。该方法的有效性和准确性针对不同类型的损坏输入图像进行了分析，并结合其计算性能进行了评估。特别关注于利用宇宙学数值模拟的结果，创建真实的模拟观测数据用于训练，以生成对应于LOFAR HBA 8小时观测在150 MHz频率的图像。我们的自动编码器能够有效去噪复杂图像，在仪器灵敏度极限下识别和提取微弱目标。该方法可以高效地扩展到大型数据集，利用高性能计算解决方案，完全自动化（即训练后不需要人工监督）。它可以准确地进行图像分割，识别弥散源的低亮度边缘，证明是检测隐藏在噪声射电观测中的具有挑战性的扩展目标的可行解决方案。 显示英文摘要

摘要： We apply a Machine Learning technique known as Convolutional Denoising Autoencoder to denoise synthetic images of state-of-the-art radio telescopes, with the goal of detecting the faint, diffused radio sources predicted to characterise the radio cosmic web. In our application, denoising is intended to address both the reduction of random instrumental noise and the minimisation of additional spurious artefacts like the sidelobes, resulting from the aperture synthesis technique. The effectiveness and the accuracy of the method are analysed for different kinds of corrupted input images, together with its computational performance. Specific attention has been devoted to create realistic mock observations for the training, exploiting the outcomes of cosmological numerical simulations, to generate images corresponding to LOFAR HBA 8 hours observations at 150 MHz. Our autoencoder can effectively denoise complex images identifying and extracting faint objects at the limits of the instrumental sensitivity. The method can efficiently scale on large datasets, exploiting high performance computing solutions, in a fully automated way (i.e. no human supervision is required after training). It can accurately perform image segmentation, identifying low brightness outskirts of diffused sources, proving to be a viable solution for detecting challenging extended objects hidden in noisy radio observations.