标题： 来自机器学习的百万类星体目录中的射电源的红移 显示英文标题

标题： Redshifts of radio sources in the Million Quasars Catalogue from machine learning

摘要： 为了利用机器学习技术仅基于源的射电光谱来获得光变红移，我们从百万类星体目录中提取了射电源。其中，有44,119个源具有用于模型验证的光谱红移，并且可以获取其测光数据。尽管随着训练样本规模的增加模型似乎有所改进，但使用射电光谱特性作为特征时，我们未能找到一个能够可靠预测红移的模型。然而，使用近红外-光学-紫外波段的亮度值，我们根据12,503个拥有所有必要测光数据的射电源得到了可靠的预测。通过80:20的训练-验证划分，这仅提供了2501个验证源，但在我们的先前SDSS模型上训练样本时，对全部12,503个源给出了可比的结果。这使我们确信，SkyMapper（它将在u、v、g、r、i、z波段对南天进行巡天）可用于预测平方公里阵列探测到的射电源的红移。通过使用机器学习来填补样本中缺失的亮度值，我们可以预测32,698个源的红移，这一比例从样本的28%增加到了74%，但代价是异常值的比例增加了1.4倍。虽然“光学”波段数据证明是成功的，但在现阶段，如果我们有足够的数据（可能需要克服相对无特征的射电光谱），我们还不能排除射电测光红移的可能性。 显示英文摘要

摘要： With the aim of using machine learning techniques to obtain photometric redshifts based upon a source's radio spectrum alone, we have extracted the radio sources from the Million Quasars Catalogue. Of these, 44,119 have a spectroscopic redshift, required for model validation, and for which photometry could be obtained. Using the radio spectral properties as features, we fail to find a model which can reliably predict the redshifts, although there is the suggestion that the models improve with the size of the training sample. Using the near-infrared--optical--ultraviolet bands magnitudes, we obtain reliable predictions based on the 12,503 radio sources which have all of the required photometry. From the 80:20 training--validation split, this gives only 2501 validation sources, although training the sample upon our previous SDSS model gives comparable results for all 12,503 sources. This makes us confident that SkyMapper, which will survey southern sky in the u, v, g, r, i, z bands, can be used to predict the redshifts of radio sources detected with the Square Kilometre Array. By using machine learning to impute the magnitudes missing from much of the sample, we can predict the redshifts for 32,698 sources, an increase from 28% to 74% of the sample, at the cost of increasing the outlier fraction by a factor of 1.4. While the "optical" band data prove successful, at this stage we cannot rule out the possibility of a radio photometric redshift, given sufficient data which may be necessary to overcome the relatively featureless radio spectra.