A Robust Classification of Galaxy Spectra: Dealing with Noisy and Incomplete Data
A Robust Classification of Galaxy Spectra: Dealing with Noisy and Incomplete Data
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DOI：
10.1086/300839
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发表时间：
1999-01
期刊：
影响因子：
--
通讯作者：
A. Connolly;A. Connolly;A. Szalay
中科院分区：
文献类型：
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作者：
A. Connolly;A. Connolly;A. Szalay
在接下来的几年里，新的光谱观测(从斯隆数字天空巡天和2DF银河巡天的光学观测到GALEX等天基紫外线卫星)将提供机会和挑战，以了解不同光谱类型的星系如何随着红移而演化。已经开发出了根据星系的连续谱和线谱对星系进行分类的技术。其中一些最有希望的星系使用了卡尔胡宁和洛埃夫变换(或称主成分分析)来将星系划分为不同的类别。它们的局限性在于，它们假设给定样本内的所有星系的光谱复盖率和光谱质量都是恒定的。在这篇文章中，我们发展了一种通用的形式，它解释了观测光谱内的丢失数据(如天际线的去除或由于星系红移而采样不同的固有静止波长范围的影响)。我们证明，通过纠正这些差距，我们可以恢复几乎红移无关的分类方案。从这个分类中，我们可以得到一个最优的插值法，该插值法可以重建丢失数据区域中的星系光谱能量分布。这提供了一种简单而有效的机制，可以直接从可能有噪声、不完整或来自许多不同来源的数据建立星系光谱能量分布。
Over the next few years new spectroscopic surveys (from the optical surveys of the Sloan Digital Sky Survey and the 2dF Galaxy Survey through to space-based ultraviolet satellites such as GALEX) will provide the opportunity and challenge of understanding how galaxies of different spectral type evolve with redshift. Techniques have been developed to classify galaxies based on their continuum and line spectra. Some of the most promising of these have used the Karhunen & Loève transform (or principal component analysis) to separate galaxies into distinct classes. Their limitation has been that they assume that the spectral coverage and quality of the spectra are constant for all galaxies within a given sample. In this paper we develop a general formalism that accounts for the missing data within the observed spectra (such as the removal of sky lines or the effect of sampling different intrinsic rest-wavelength ranges due to the redshift of a galaxy). We demonstrate that by correcting for these gaps we can recover an almost redshift-independent classification scheme. From this classification we can derive an optimal interpolation that reconstructs the underlying galaxy spectral energy distributions in the regions of missing data. This provides a simple and effective mechanism for building galaxy spectral energy distributions directly from data that may be noisy, incomplete, or drawn from a number of different sources.