基于机器学习的射电选择数据的星系红移估计
Estimating Galaxy Redshift in Radio-Selected Datasets using Machine Learning
摘要：
全天空无线电调查将以新的发现彻底改变这一领域。然而，数千万个射电星系中的绝大多数都没有大量科学案例所需的光谱红移测量。在这里，我们评估技术估计星系的红移从无线电选择的调查。使用具有红外和光学波长的宽带测光的无线电选择样本，我们测试了k最近邻（kNN）和随机森林机器学习算法，在回归和分类模式下测试它们。此外，我们测试了kNN算法使用的不同距离度量，包括标准欧几里得距离，Mahalanobis距离和回归模式（核回归度量学习）和分类模式（大间隔最近邻度量）的学习距离度量。我们发现，所有的回归为基础的模式失败的星系在红移$z > 1$。然而，低于该范围，使用Mahalanobis距离度量的kNN算法表现最好，$η_{0.15}$离群值率为5.85\%。在分类模式中，使用Mahalanobis距离度量的kNN算法也表现最好，$η_{0.15}$离群率为5.85%，正确地将74%的星系放置在顶部$z > 1.02$箱中。最后，我们还测试了在一个领域进行训练并将训练后的算法应用于另一个领域的类似数据的效果，发现跨领域的变化不会导致预测红移的统计学显著差异。重要的是，我们发现，虽然我们可能无法预测高红移射电源的连续值，但我们可以使用现有技术的分类模式来识别其中的大多数。
英文摘要：
All-sky radio surveys are set to revolutionise the field with new discoveries. However, the vast majority of the tens of millions of radio galaxies won't have the spectroscopic redshift measurements required for a large number of science cases. Here, we evaluate techniques for estimating redshifts of galaxies from a radio-selected survey. Using a radio-selected sample with broadband photometry at infrared and optical wavelengths, we test the k-Nearest Neighbours (kNN) and Random Forest machine learning algorithms, testing them both in their regression and classification modes. Further, we test different distance metrics used by the kNN algorithm, including the standard Euclidean distance, the Mahalanobis distance and a learned distance metric for both the regression mode (the Metric Learning for Kernel Regression metric) and the classification mode (the Large Margin Nearest Neighbour metric). We find that all regression-based modes fail on galaxies at a redshift $z > 1$. However, below this range, the kNN algorithm using the Mahalanobis distance metric performs best, with an $η_{0.15}$ outlier rate of 5.85\%. In the classification mode, the kNN algorithm using the Mahalanobis distance metric also performs best, with an $η_{0.15}$ outlier rate of 5.85\%, correctly placing 74\% of galaxies in the top $z > 1.02$ bin. Finally, we also tested the effect of training in one field and applying the trained algorithm to similar data from another field and found that variation across fields does not result in statistically significant differences in predicted redshifts. Importantly, we find that while we may not be able to predict a continuous value for high-redshift radio sources, we can identify the majority of them using the classification modes of existing techniques.
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