论文标题
宽银河系分类，并支持卫生
Radio Galaxy Classification with wGAN-Supported Augmentation
论文作者
论文摘要
新颖的技术是必不可少的，即可处理新一代射电望远镜中的大量数据。特别是，图像中天文来源的分类具有挑战性。射电星系的形态学分类可以通过需要大量标记训练数据的深度学习模型来自动化。在这里，我们演示了生成模型，特别是Wasserstein Gans（Wgan）的使用，以生成不同类别的射电星系的人工数据。随后，我们使用wgan的图像来增强培训数据。我们发现，通过将生成的图像包括在训练集中，可以显着改善一个简单的完全连接的用于分类的神经网络。
Novel techniques are indispensable to process the flood of data from the new generation of radio telescopes. In particular, the classification of astronomical sources in images is challenging. Morphological classification of radio galaxies could be automated with deep learning models that require large sets of labelled training data. Here, we demonstrate the use of generative models, specifically Wasserstein GANs (wGAN), to generate artificial data for different classes of radio galaxies. Subsequently, we augment the training data with images from our wGAN. We find that a simple fully-connected neural network for classification can be improved significantly by including generated images into the training set.
