Convolutional Deep Denoising Autoencoders for Radio Astronomical Images
Convolutional Deep Denoising Autoencoders for Radio Astronomical Images
复制标题
用于射电天文图像的卷积深度去噪自动编码器
DOI：
10.1093/mnras/stab3044
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
F. Vazza
中科院分区：
文献类型：
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作者：
C. Gheller;F. Vazza
我们应用了一种称为卷积去噪自动编码器的机器学习技术来对最先进的射电望远镜的合成图像进行去噪，目的是检测预测将覆盖无线电宇宙网的微弱、扩散的射电源。在我们的应用中，去噪旨在解决随机仪器噪声的减少和额外的虚假伪像的旁瓣，从孔径合成技术产生的最小化。针对不同类型的输入图像，分析了该方法的有效性和准确性，以及计算性能。特别注意创建现实的模拟观测的培训，利用宇宙学数值模拟的结果，以产生相应的LOFAR HBA 8小时观测在150 MHz的图像。我们的自动编码器可以有效地消除复杂图像的噪声，在仪器灵敏度的限制下识别和提取微弱的物体。该方法可以在大型数据集上有效地扩展，利用高性能计算解决方案，以完全自动化的方式（即在训练后不需要人工监督）。它可以准确地执行图像分割，识别扩散源的低亮度边缘，证明是一个可行的解决方案，用于检测隐藏在嘈杂的无线电观测中的具有挑战性的扩展对象。
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 minimization 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.