arXiv 1909.007182019-09astro-ph.EPastro-ph.IM

使用无监督机器学习分类优化系外行星大气反演

Optimizing exoplanet atmosphere retrieval using unsupervised machine-learning classification

Joshua J. C. Hayes, E. Kerins, S. Awiphan, I. McDonald, J. S. Morgan, P. Chuanraksasat, S. Komonjinda, N. Sanguansak, P. Kittara

备注：: Accepted for publication in MNRAS
DOI：: 10.1093/mnras/staa978

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摘要：

在全天空调查的时代，大气特征的主要瓶颈之一是快速，自主和强大的大气反演方法的可用性。我们提出了一种新的方法，使用无监督的机器学习，以产生知情的先验知识，用于从透射光谱中检索系外行星大气参数。我们使用主成分分析（PCA），有效地压缩使用PLATON包生成的透射光谱正演模型库的信息内容。然后，我们在PCA空间中应用$k$ -均值聚类算法将文库分离成离散类。我们表明，我们的分类器几乎总是能够瞬间将一个以前看不见的频谱到正确的类，低到中等的频谱分辨率，$R$，在范围$R~=~30-300$和噪声水平高达$10$ ~ %的峰谷频谱幅度。因此，该类所有成员的物理参数的分布为标准检索方法（如嵌套采样）提供了一个有根据的先验。我们基准我们的信息先验的方法对标准的统一先验嵌套采样器，发现我们的方法是一个因素两个更快，可以忽略不计的准确性降低。我们展示了这种方法的应用，现有的和不久的将来的天文台，并表明它是适合于现实世界的应用。我们的一般方法并不特定于透射光谱，应该更广泛地适用于涉及重复拟合可信高维模型到大数据目录的情况，包括超越系外行星科学。

英文摘要：

One of the principal bottlenecks to atmosphere characterisation in the era of all-sky surveys is the availability of fast, autonomous and robust atmospheric retrieval methods. We present a new approach using unsupervised machine learning to generate informed priors for retrieval of exoplanetary atmosphere parameters from transmission spectra. We use principal component analysis (PCA) to efficiently compress the information content of a library of transmission spectra forward models generated using the PLATON package. We then apply a $k$-means clustering algorithm in PCA space to segregate the library into discrete classes. We show that our classifier is almost always able to instantaneously place a previously unseen spectrum into the correct class, for low-to-moderate spectral resolutions, $R$, in the range $R~=~30-300$ and noise levels up to $10$~per~cent of the peak-to-trough spectrum amplitude. The distribution of physical parameters for all members of the class therefore provides an informed prior for standard retrieval methods such as nested sampling. We benchmark our informed-prior approach against a standard uniform-prior nested sampler, finding that our approach is up to a factor two faster, with negligible reduction in accuracy. We demonstrate the application of this method to existing and near-future observatories, and show that it is suitable for real-world application. Our general approach is not specific to transmission spectroscopy and should be more widely applicable to cases that involve repetitive fitting of trusted high-dimensional models to large data catalogues, including beyond exoplanetary science.

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