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arXiv 2102.09177astro-ph.CO
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基于机器学习和神经网络的QSO测光红移

QSO photometric redshifts using machine learning and neural networks

S. J. Curran, J. P. Moss, Y. C. Perrott

备注：
Accepted by MNRAS
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摘要：

如果能够迅速可靠地估计新探测到的源的红移，下一代大型连续谱巡天的科学价值将大大增加。鉴于获得光谱红移的观测费用为大量的新的检测预期，有大量的最近的工作使用机器学习技术来获得光度红移。在这里，我们将从深度学习（DL）获得的预测光度红移的准确性与k最近邻（kNN）和决策树回归（DTR）算法进行了比较。我们发现，使用近红外，可见光和紫外线幅度的组合，在SDSS类星体的样本上训练，kNN和DL算法产生最好的自验证结果，标准差为σ = 0.24。在各种子样本上进行测试，我们发现DL算法通常具有较低的σ值，除了在其他测量中表现出更好的性能之外。我们的DL方法使用易于实现的现成算法，不需要过滤也不需要去除离群值，与其他更复杂的算法类似，精度为Δz < 0.1$，最高可达z ~ 2.5。将在我们的70，000强样本上训练的DL算法应用于其他独立（无线电选择）数据集，我们发现在很宽的无线电通量密度范围内σ < 0.36。这表明，使用这种方法来确定平方公里阵列探测到的类星体的光度红移具有很大的潜力。

英文摘要：

The scientific value of the next generation of large continuum surveys would be greatly increased if the redshifts of the newly detected sources could be rapidly and reliably estimated. Given the observational expense of obtaining spectroscopic redshifts for the large number of new detections expected, there has been substantial recent work on using machine learning techniques to obtain photometric redshifts. Here we compare the accuracy of the predicted photometric redshifts obtained from Deep Learning(DL) with the k-Nearest Neighbour (kNN) and the Decision Tree Regression (DTR) algorithms. We find using a combination of near-infrared, visible and ultraviolet magnitudes, trained upon a sample of SDSS QSOs, that the kNN and DL algorithms produce the best self-validation result with a standard deviation of σ = 0.24. Testing on various sub-samples, we find that the DL algorithm generally has lower values of σ, in addition to exhibiting a better performance in other measures. Our DL method, which uses an easy to implement off-the-shelf algorithm with no filtering nor removal of outliers, performs similarly to other, more complex, algorithms, resulting in an accuracy of Δz < 0.1$ up to z ~ 2.5. Applying the DL algorithm trained on our 70,000 strong sample to other independent (radio-selected) datasets, we find σ < 0.36 over a wide range of radio flux densities. This indicates much potential in using this method to determine photometric redshifts of quasars detected with the Square Kilometre Array.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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