Augmenting machine learning photometric redshifts with Gaussian mixture models
Augmenting machine learning photometric redshifts with Gaussian mixture models
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DOI：
10.1093/mnras/staa2741
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发表时间：
2020-09
影响因子：
4.8
通讯作者：
P. Hatfield;I. Almosallam;M. Jarvis;M. Jarvis;N. Adams;R. Bowler;Zahra Gomes;S. Roberts;C. Schreiber
中科院分区：
文献类型：
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作者：
P. Hatfield;I. Almosallam;M. Jarvis;M. Jarvis;N. Adams;R. Bowler;Zahra Gomes;S. Roberts;C. Schreiber
广域成像巡天是未来几年推进我们对宇宙学、星系形成物理学和宇宙大尺度结构的理解的关键途径之一。这些调查通常需要计算大量（数亿至数十亿）星系的红移-几乎所有这些都必须来自测光而不是光谱学。在本文中，我们研究如何使用统计模型来理解构成星系颜色星等分布的人口，可以与机器学习光度红移代码相结合，以提高红移估计。特别是，我们联合收割机结合使用高斯混合模型与高性能的机器学习photo-z算法GPz，并表明，建模和占不同的颜色幅度分布的训练和测试数据分别可以得到改进的红移估计，减少一半的估计偏差，并加快算法的运行时间。这些方法说明使用数据从深光学和近红外数据在两个单独的深领域，不同的颜色星等分布的训练和测试数据构建从星系与已知的光谱红移，来自几个异构的调查。
Wide-area imaging surveys are one of the key ways of advancing our understanding of cosmology, galaxy formation physics, and the large-scale structure of the Universe in the coming years. These surveys typically require calculating redshifts for huge numbers (hundreds of millions to billions) of galaxies - almost all of which must be derived from photometry rather than spectroscopy. In this paper we investigate how using statistical models to understand the populations that make up the colour-magnitude distribution of galaxies can be combined with machine learning photometric redshift codes to improve redshift estimates. In particular we combine the use of Gaussian Mixture Models with the high performing machine learning photo-z algorithm GPz and show that modelling and accounting for the different colour-magnitude distributions of training and test data separately can give improved redshift estimates, reduce the bias on estimates by up to a half, and speed up the run-time of the algorithm. These methods are illustrated using data from deep optical and near infrared data in two separate deep fields, where training and test data of different colour-magnitude distributions are constructed from the galaxies with known spectroscopic redshifts, derived from several heterogeneous surveys.