使用无监督深度学习对第谷超新星遗迹空间结构的X射线研究
X-ray Study of Spatial Structures in Tycho's Supernova Remnant Using Unsupervised Deep Learning
摘要：
深度学习算法最近的快速发展，可以隐式地捕获高维数据中的结构，为天文数据分析打开了新的篇章。我们在这里报告了一种用于X射线分析的深度学习技术的新实现。我们应用变分自动编码器（VAE），使用深度神经网络对钱德拉X射线天文台从第谷超新星遗迹（SNR）获得的数据进行空间光谱分析。我们建立了一个无监督的学习方法相结合的VAE和高斯混合模型（GMM），其中观察到的光谱数据的维数减少的VAE，并在特征空间中的聚类是由GMM执行。我们发现，一些特征的空间结构，如东部边缘的铁结，可以自动识别这种方法，它只使用光谱特性。这一结果表明，无监督机器学习可以用于从观测数据中的光谱信息中提取特征空间结构（无需详细的光谱分析），这将减少用于理解漫射天文物体中精细结构的人力密集型预处理成本，例如，或者星系团。这种数据驱动的分析可用于选择从中提取光谱进行详细分析的区域，并帮助我们充分利用当前可用的大量光谱数据以及未来几十年的光谱数据。
英文摘要：
Recent rapid development of deep learning algorithms, which can implicitly capture structures in high-dimensional data, opens a new chapter in astronomical data analysis. We report here a new implementation of deep learning techniques for X-ray analysis. We apply a variational autoencoder (VAE) using a deep neural network for spatio-spectral analysis of data obtained by Chandra X-ray Observatory from Tycho's supernova remnant (SNR). We established an unsupervised learning method combining the VAE and a Gaussian mixture model (GMM), where the dimensions of the observed spectral data are reduced by the VAE, and clustering in feature space is performed by the GMM. We found that some characteristic spatial structures, such as the iron knot on the eastern rim, can be automatically recognised by this method, which uses only spectral properties. This result shows that unsupervised machine learning can be useful for extracting characteristic spatial structures from spectral information in observational data (without detailed spectral analysis), which would reduce human-intensive preprocessing costs for understanding fine structures in diffuse astronomical objects, e.g., SNRs or clusters of galaxies. Such data-driven analysis can be used to select regions from which to extract spectra for detailed analysis and help us make the best use of the large amount of spectral data available currently and arriving in the coming decades.
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