论文标题
无监督的机器学习用于系外行星传输光谱的探索性数据分析
Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra
论文作者
论文摘要
过境光谱是一种强大的工具，可以解码外星外行星大气的化学成分。在本文中，我们关注的是无监督的技术，用于分析来自过境系外行星的光谱数据。 We demonstrate methods for i) cleaning and validating the data, ii) initial exploratory data analysis based on summary statistics (estimates of location and variability), iii) exploring and quantifying the existing correlations in the data, iv) pre-processing and linearly transforming the data to its principal components, v) dimensionality reduction and manifold learning, vi) clustering and anomaly detection, vii) visualization and interpretation of the 数据。为了说明所提出的无监督方法，我们使用了众所周知的公共基准数据集的合成传输光谱。我们表明，光谱数据中存在高度的相关性，该数据要求适当的低维表示。我们探索了多种降低维度的不同技术，并在汇总统计，主要成分等方面确定了几种合适的选择。我们在主要组成部分中揭示了有趣的结构，即，与基础大气的不同化学制度相对应的定义明确的分支。我们证明，这些分支可以以完全无监督的方式通过K-均值聚类算法成功恢复。我们主张根据前三个主要成分对光谱数据进行三维表示，以揭示数据中的现有结构并迅速表征行星的化学类别。
Transit spectroscopy is a powerful tool to decode the chemical composition of the atmospheres of extrasolar planets. In this paper we focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. We demonstrate methods for i) cleaning and validating the data, ii) initial exploratory data analysis based on summary statistics (estimates of location and variability), iii) exploring and quantifying the existing correlations in the data, iv) pre-processing and linearly transforming the data to its principal components, v) dimensionality reduction and manifold learning, vi) clustering and anomaly detection, vii) visualization and interpretation of the data. To illustrate the proposed unsupervised methodology, we use a well-known public benchmark data set of synthetic transit spectra. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We explore a number of different techniques for such dimensionality reduction and identify several suitable options in terms of summary statistics, principal components, etc. We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes of the underlying atmospheres. We demonstrate that those branches can be successfully recovered with a K-means clustering algorithm in fully unsupervised fashion. We advocate for a three-dimensional representation of the spectroscopic data in terms of the first three principal components, in order to reveal the existing structure in the data and quickly characterize the chemical class of a planet.
