Optimizing the shape of photometric redshift distributions with clustering cross-correlations
Optimizing the shape of photometric redshift distributions with clustering cross-correlations
复制标题
通过聚类互相关优化光度红移分布的形状
DOI：
10.1093/mnras/stac3630
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发表时间：
2023
影响因子：
4.8
通讯作者：
Stölzner B
中科院分区：
文献类型：
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作者：
Stölzner B
我们提出了一种将光度星系分配到一组选定的红移箱的优化方法。这是通过将模拟退火（一种受固态物理学启发的优化算法）与无监督机器学习方法（观测星系颜色的自组织图（SOM））相结合来实现的。从基于光度红移点估计将星系样本划分为红移箱开始，模拟退火算法反复将som选择的颜色接近的星系子样本重新分配到替代的红移箱。我们优化了光度星系和具有良好校准红移的星系参考样本之间的聚类互相关信号。根据对聚类信号的影响，重新分配被接受或拒绝。通过动态提高SOM的分辨率，该算法最终收敛到一个解，该解使每个层析红移仓中的不匹配星系数量最小化，从而提高了相应红移分布的紧凑性。该方法在spacedc2综合遗留巡天目录上得到了验证。我们发现，在所有层析箱中，红移分布中灾难性异常值的比例显著下降，最显著的是在最高红移箱中，异常值比例从57%下降到16%。
We present an optimization method for the assignment of photometric galaxies to a chosen set of redshift bins. This is achieved by combining simulated annealing, an optimization algorithm inspired by solid-state physics, with an unsupervised machine learning method, a self-organizing map (SOM) of the observed colours of galaxies. Starting with a sample of galaxies that is divided into redshift bins based on a photometric redshift point estimate, the simulated annealing algorithm repeatedly reassigns SOM-selected subsamples of galaxies, which are close in colour, to alternative redshift bins. We optimize the clustering cross-correlation signal between photometric galaxies and a reference sample of galaxies with well-calibrated redshifts. Depending on the effect on the clustering signal, the reassignment is either accepted or rejected. By dynamically increasing the resolution of the SOM, the algorithm eventually converges to a solution that minimizes the number of mismatched galaxies in each tomographic redshift bin and thus improves the compactness of their corresponding redshift distribution. This method is demonstrated on the synthetic Legacy Survey of Space and Time cosmoDC2 catalogue. We find a significant decrease in the fraction of catastrophic outliers in the redshift distribution in all tomographic bins, most notably in the highest redshift bin with a decrease in the outlier fraction from 57 per cent to 16 per cent.