论文标题
具有深度正常流的恒星光谱的无监督学习
Unsupervised Learning for Stellar Spectra with Deep Normalizing Flows
论文作者
论文摘要
恒星光谱编码有关星星的详细信息。但是，大多数机器学习方法中的恒星光谱方法都集中在监督学习上。我们介绍了一种无监督的学习方法Mendis，该方法采用了由神经条件流和光芒组成的正常流，以描述光谱空间的复杂分布。门迪斯的一个关键优点是，我们可以描述光谱的条件分布，条件在恒星参数上，以进一步揭示光谱的基础结构。特别是，我们的研究表明，门迪斯可以在光谱中稳健地捕获像素相关性，从而导致可能从恒星光谱中检测出未知的原子过渡。门迪斯的概率性质还可以在广泛的光谱调查中严格确定异常值，而无需事先通过现有的分析管道来衡量元素丰度。
Stellar spectra encode detailed information about the stars. However, most machine learning approaches in stellar spectroscopy focus on supervised learning. We introduce Mendis, an unsupervised learning method, which adopts normalizing flows consisting of Neural Spline Flows and GLOW to describe the complex distribution of spectral space. A key advantage of Mendis is that we can describe the conditional distribution of spectra, conditioning on stellar parameters, to unveil the underlying structures of the spectra further. In particular, our study demonstrates that Mendis can robustly capture the pixel correlations in the spectra leading to the possibility of detecting unknown atomic transitions from stellar spectra. The probabilistic nature of Mendis also enables a rigorous determination of outliers in extensive spectroscopic surveys without the need to measure elemental abundances through existing analysis pipelines beforehand.
