Kernel regression for determining photometric redshifts from Sloan broad‐band photometry
Kernel regression for determining photometric redshifts from Sloan broad‐band photometry
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DOI：
10.1111/j.1365-2966.2007.12129.x
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发表时间：
2007-06
影响因子：
4.8
通讯作者：
D. Wang;Y. X. Zhang;C. Liu;Y. H.Zhao
中科院分区：
文献类型：
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作者：
D. Wang;Y. X. Zhang;C. Liu;Y. H.Zhao
在斯隆数字巡天(SDSS)的第五次数据发布中，我们提出了一种新的方法，即核回归方法来确定399 929个星系的光度学红移。核回归是查询点的邻域光谱红移的加权平均，更高的权重与更接近查询点的点相关联。使用核回归时的一个重要设计决策是带宽的选择。我们应用10次交叉验证来选择最优带宽，该带宽是在交叉验证误差接近其最小值时获得的。结果表明，对于不同的输入模式，最佳带宽是不同的：以COLOR+ECLASE作为输入，测光红移估计的最小均方根误差达到0.019，以UGRIZ+ECLASE作为输入，最小均方根误差达到0.020。其中eClass是星系光谱类型，0.021使用COLOR+R作为输入。因此，除了幅值和颜色等参数外，eClass是预测光度学红移的有效参数。此外，结果还表明，将样本分为早型星系和晚型星系时，测光红移的估计精度有所提高；特别是对于早型星系，以COLOR+ECLASS为输入，均方根散射值为0.016。此外，当使用颜色+r作为输入模式来预测光度学eClass(σ均方根=0.034)时，核回归实现了高精度。对于核回归，光度红移的精度并不总是随着所考虑的参数的数量而增加，但只有在选择适当的参数时才能令人满意。核回归是一种易于理解和准确的回归方法。实验表明，与其他经验训练方法相比，核回归方法具有更好的性能。
We present a new approach, namely kernel regression, to determine photometric redshifts for 399 929 galaxies in the Fifth Data Release of the Sloan Digital Sky Survey (SDSS). Kernel regression is a weighted average of spectral redshifts of the neighbours for a query point, and higher weights are associated with points that are closer to the query point. One important design decision when using kernel regression is the choice of bandwidth. We apply 10-fold cross-validation to choose the optimal bandwidth, which is obtained as the cross-validation error approaches its minimum. The results show that the optimal bandwidth is different for different input patterns: the lowest rms error of photometric redshift estimation arrives at 0.019 using colour+eClass as the inputs, the lowest rms errors comes to 0.020 using ugriz+eClass as the inputs. Where eClass is a galaxy spectral type, and 0.021 using colour+ r as the inputs. Thus, in addition to parameters such as magnitude and colour, eClass is a valid parameter with which to predict photometric redshifts. Moreover, the results suggest that the accuracy of estimating photometric redshifts is improved when the sample is divided into early-type and late-type galaxies; in particular, for early-type galaxies, the rms scatter is 0.016 with colour+eClass as the inputs. In addition, kernel regression achieves high accuracy when predicting the photometric eClass (σ rms = 0.034) using colour+ r as the input pattern. For kernel regression, the accuracy of the photometric redshifts does not always increase with the number of parameters considered, but is satisfactory only when appropriate parameters are chosen. Kernel regression is a comprehensible and accurate regression method. Experiments reveal the superiority of kernel regression over other empirical training approaches.