论文标题
欧几里得准备：xxii。使用机器学习从模拟光度法中选择静态星系
Euclid preparation: XXII. Selection of Quiescent Galaxies from Mock Photometry using Machine Learning
论文作者
论文摘要
欧几里得太空望远镜将在光学和近红外波长以及无狭窄的近红外光谱上提供深层成像，跨越约15,000平方英尺的天空。预计欧几里得将检测到约120亿个天文来源，促进对宇宙学，银河发展和其他各种主题的新见解。为了最佳利用预期的非常大的数据集，需要开发适当的方法和软件。在这里，我们提出了一种基于机器学习的新方法，用于使用宽带欧几里得I_E，y_e，j_e，h_e光度法选择静态星系，并结合来自其他调查的多波长光度法。 Ariadne管道使用元学习来融合决策树的合奏，最近的邻居和深度学习方法，将其分为单个分类器的精度明显高于任何单独的学习方法。该管道具有“稀疏性意识”，因此缺少的光度计值仍然是分类的信息。我们的管道衍生出选择为静止的星系的光度红移，并在“伪标记”半监督方法的帮助下。应用异常过滤器后，我们的管道实现了〜<0.03的归一化绝对偏差，当针对COSMOS2015光度红移时测量时，〜<0.02的灾难性异常值的一部分为〜<0.02。我们将分类管道应用于对应于三种主要情况的模拟星系光度法目录：（i）使用辅助Ugriz，Wise和Radio Data的Euclid深入调查； （ii）与辅助Ugriz，Wise和Radio Data的Euclid广泛调查； （iii）欧几里得广泛的调查。除了Euclid I_E-Y_E，J_E-H_E和U-I_E，I_E-J_E-J_E颜色颜色的方法外，我们的分类管道优于UVJ选择，除了完整性以及最高2个因子的F1得分。
The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15,000 sq deg of the sky. Euclid is expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. To optimally exploit the expected very large data set, there is the need to develop appropriate methods and software. Here we present a novel machine-learning based methodology for selection of quiescent galaxies using broad-band Euclid I_E, Y_E, J_E, H_E photometry, in combination with multiwavelength photometry from other surveys. The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods separately. The pipeline has `sparsity-awareness', so that missing photometry values are still informative for the classification. Our pipeline derives photometric redshifts for galaxies selected as quiescent, aided by the `pseudo-labelling' semi-supervised method. After application of the outlier filter, our pipeline achieves a normalized mean absolute deviation of ~< 0.03 and a fraction of catastrophic outliers of ~< 0.02 when measured against the COSMOS2015 photometric redshifts. We apply our classification pipeline to mock galaxy photometry catalogues corresponding to three main scenarios: (i) Euclid Deep Survey with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey with ancillary ugriz, WISE, and radio data; (iii) Euclid Wide Survey only. Our classification pipeline outperforms UVJ selection, in addition to the Euclid I_E-Y_E, J_E-H_E and u-I_E,I_E-J_E colour-colour methods, with improvements in completeness and the F1-score of up to a factor of 2. (Abridged)
