利用相干性归约识别和修复星系性质中的灾难性误差
Identifying and Repairing Catastrophic Errors in Galaxy Properties Using Dimensionality Reduction
摘要：
我们对星系演化的理解来自大型调查，旨在通过只观察推断典型星系属性所需的最小量来最大限度地提高效率。然而，在每次调查中，对于百分之几的星系，这些观测是不够的，导出的属性可能是灾难性的错误。此外，目前很难或不可能确定哪些物体已经失败，因此这些物体污染了每一项星系性质的研究。我们开发了一种新的方法来识别这些对象相结合的天文代码推断星系属性的降维算法t-SNE，组相似的对象，以确定哪些推断的属性是不合适的。这种方法提供了一个改进的COSMOS目录，它已经使用现有的技术消除灾难性的错误，因此应该提高质量的大型目录和任何研究是敏感的大红移误差。
英文摘要：
Our understanding of galaxy evolution is derived from large surveys designed to maximize efficiency by only observing the minimum amount needed to infer properties for a typical galaxy. However, for a few percent of galaxies in every survey, these observations are insufficient and derived properties can be catastrophically wrong. Further, it is currently difficult or impossible to determine which objects have failed, so that these contaminate every study of galaxy properties. We develop a novel method to identify these objects by combining the astronomical codes which infer galaxy properties with the dimensionality reduction algorithm t-SNE, which groups similar objects to determine which inferred properties are out of place. This method provides an improvement for the COSMOS catalog, which already uses existing techniques for catastrophic error removal, and therefore should improve the quality of large catalogs and any studies which are sensitive to large redshift errors.
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