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arXiv 1104.3758astro-ph.CO
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斯隆数字巡天中的星系动物园形态和测光红移

Galaxy Zoo Morphology and Photometric Redshifts in the Sloan Digital Sky Survey

M. J. Way

摘要:

它最近被证明,人们可以准确地从特定的主要和次要的等照度线形状估计斯隆数字巡天成像目录中的星系形态。这是通过将机器学习技术应用于Galaxy Zoo形态学目录来实现的。使用斯隆数字巡天的宽带通测光结合星系形态的精确知识,应有助于估计更准确的光度红移的星系。使用银河动物园分离的螺旋和椭圆相结合的斯隆数字巡天测光,我们试图计算光度红移。在最好的情况下,我们发现,根均方误差的发光红星系归类为椭圆低至0.0118。鉴于这些有希望的结果,我们相信,如果研究人员也可以通过机器学习利用它们的衍生形态,那么斯隆数字巡天($\sim$ 3.5亿)中所有星系的更好的光度红移估计将是可行的。这些初步结果看起来是有希望的,为那些有兴趣估计弱透镜,重子声振荡,和其他领域依赖于准确的光度红移。

英文摘要:

It has recently been demonstrated that one can accurately derive galaxy morphology from particular primary and secondary isophotal shape estimates in the Sloan Digital Sky Survey imaging catalog. This was accomplished by applying Machine Learning techniques to the Galaxy Zoo morphology catalog. Using the broad bandpass photometry of the Sloan Digital Sky Survey in combination with with precise knowledge of galaxy morphology should help in estimating more accurate photometric redshifts for galaxies. Using the Galaxy Zoo separation for spirals and ellipticals in combination with Sloan Digital Sky Survey photometry we attempt to calculate photometric redshifts. In the best case we find that the root mean square error for Luminous Red Galaxies classified as ellipticals is as low as 0.0118. Given these promising results we believe better photometric redshift estimates for all galaxies in the Sloan Digital Sky Survey ($\sim$350 million) will be feasible if researchers can also leverage their derived morphologies via Machine Learning. These initial results look to be promising for those interested in estimating Weak-Lensing, Baryonic Acoustic Oscillation, and other fields dependent upon accurate photometric redshifts.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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