论文标题
来自SDSS，Wise和Galex颜色的QSO光度红移
QSO photometric redshifts from SDSS, WISE and GALEX colours
论文作者
论文摘要
机器学习技术，特别是应用于光节颜色的K-Nearest邻居算法，在预测准恒星对象（QSOS）的光度红移方面取得了一些成功：尽管光谱镜和光度数量之间差异之间差异的平均值接近零，但这些差异接近这些差异，但这些差异仍然是这些差异的分布和不同的非gaussian。根据我们以前对光度红移的经验估计，我们发现通过添加其他波段的颜色，即近红外和紫外线，可以显着改善预测。通过使用33 643强QSO样品训练算法的一半来进行自我测试，从而导致其余一半的样品的扩展明显窄。使用整个QSO样品训练算法，相同的幅度集返回了一个相似的无线电源样品（类星体）。尽管匹配的巧合相对较低（在相关频段中具有光度法的3663个来源中的739个），但这仍然明显大于经验方法（2％），因此可以提供一种方法，可以提供一种获得大量的连续源无线电源的红移，该方法可与下一代大型无线电望远镜一起检测到。
Machine learning techniques, specifically the k-nearest neighbour algorithm applied to optical band colours, have had some success in predicting photometric redshifts of quasi-stellar objects (QSOs): Although the mean of differences between the spectroscopic and photometric redshifts is close to zero, the distribution of these differences remains wide and distinctly non-Gaussian. As per our previous empirical estimate of photometric redshifts, we find that the predictions can be significantly improved by adding colours from other wavebands, namely the near-infrared and ultraviolet. Self-testing this, by using half of the 33 643 strong QSO sample to train the algorithm, results in a significantly narrower spread for the remaining half of the sample. Using the whole QSO sample to train the algorithm, the same set of magnitudes return a similar spread for a sample of radio sources (quasars). Although the matching coincidence is relatively low (739 of the 3663 sources having photometry in the relevant bands), this is still significantly larger than from the empirical method (2%) and thus may provide a method with which to obtain redshifts for the vast number of continuum radio sources expected to be detected with the next generation of large radio telescopes.
