Accurate photometric redshift probability density estimation – method comparison and application
Accurate photometric redshift probability density estimation – method comparison and application
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DOI：
10.1093/mnras/stv1567
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发表时间：
2015-03
影响因子：
4.8
通讯作者：
M. Rau;S. Seitz;F. Brimioulle;E. Frank;O. Friedrich;D. Gruen;B. Hoyle
中科院分区：
文献类型：
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作者：
M. Rau;S. Seitz;F. Brimioulle;E. Frank;O. Friedrich;D. Gruen;B. Hoyle
我们介绍了一个有序分类算法的光度红移估计，显着提高了重建的光度红移概率密度函数（PDF）的个别星系和星系样本。作为一个用例，我们应用我们的方法CFHTLS星系。有序分类算法将不同的红移箱视为有序值，与非有序分类架构相比，这提高了光度红移PDF的质量。我们还提出了一种新的星系红移单值点估计方法，它可以用来估计星系样本的全部红移PDF，这种方法在精度上与现有的算法（将样本中所有星系的全部红移PDF叠加在一起）具有竞争力，但需要的存储空间要少几个数量级。本文所描述的方法大大提高了个别对象红移PDF的对数似然，相比流行的神经网络代码（ANNZ）。在我们的用例中，对于高红移物体（z ≥ 0.75），这种改进达到了50%。我们表明，使用这些更准确的光度红移PDF将导致减少系统的偏差高达4倍，当与不太准确的PDF从常用的
We introduce an ordinal classification algorithm for photometric redshift estimation, which significantly improves the reconstruction of photometric redshift probability density functions (PDFs) for individual galaxies and galaxy samples. As a use case we apply our method to CFHTLS galaxies. The ordinal classification algorithm treats distinct redshift bins as ordered values, which improves the quality of photometric redshift PDFs, compared with non-ordinal classification architectures. We also propose a new single value point estimate of the galaxy redshift,whichcan beusedtoestimatethefullredshiftPDFofagalaxy sample.Thismethod is competitive in terms of accuracy with contemporary algorithms, which stack the full redshift PDFs of all galaxies in the sample, but requires orders of magnitude less storage space. The methods described in this paper greatly improve the log-likelihood of individual object redshift PDFs, when compared with a popular neural network code (ANNZ). In our use case, this improvement reaches 50percent for high-redshift objects (z ≥ 0.75). We show that using these more accurate photometric redshift PDFs will lead to a reduction in the systematic biases by up to a factor of 4, when compared with less accurate PDFs obtained from commonly used