标题： 无监督机器学习在系外行星传输光谱探索性数据分析中的应用 显示英文标题

标题： Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra

摘要： 凌星光谱学是一种强大的工具，可用于解码太阳系外行星大气的化学组成。 本文专注于分析凌星系外行星光谱数据的无监督技术。 我们演示了以下方法：i) 数据清理与验证，ii) 基于汇总统计量（位置和变异性估计）的初始探索性数据分析，iii) 探索和量化数据中存在的相关性，iv) 数据预处理和线性变换到主成分，v) 维度约简和流形学习，vi) 聚类和异常检测，vii) 数据可视化与解释。 为了说明所提出的无监督方法，我们使用了一个著名的公共基准合成凌星光谱数据集。 我们发现光谱数据具有高度的相关性，这需要适当的低维表示。 我们探索了许多用于此类维度约简的技术，并在汇总统计量、主成分等方面确定了几种合适的选项。 我们在主成分基中发现了有趣结构，即对应于底层大气不同化学环境的良好定义的分支。 我们证明，这些分支可以成功地通过K均值聚类算法以完全无监督的方式恢复。 我们提倡用前三个主成分来表示光谱数据，以便揭示数据中的现有结构并快速表征行星的化学类别。 显示英文摘要

摘要： 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.