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arXiv 2205.13622astro-ph.CO
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用聚类互相关优化测光红移分布的形状

Optimising the shape of photometric redshift distributions with clustering cross-correlations

Benjamin Stölzner, Benjamin Joachimi, Andreas Korn, the LSST Dark Energy Science Collaboration

摘要:

我们提出了一个优化的方法分配光度星系到一组选定的红移箱。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(一种观察到的星系颜色的自组织映射(SOM))相结合来实现的。从一个样本的星系被划分成红移箱的基础上的光度红移点估计,模拟退火算法反复重新分配SOM选定的子样本的星系,这是接近的颜色,替代红移箱。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。通过动态增加SOM的分辨率,该算法最终收敛到一个解决方案,最大限度地减少每个层析红移箱中的失配星系的数量,从而提高了其相应的红移分布的紧凑性。这种方法在合成LSST cosmoDC2目录上得到了证明。我们发现,在所有的层析箱中的红移分布中的灾难性异常值的比例显着减少,最显着的是在最高的红移箱中,异常值比例从57%下降到16%。

英文摘要:

We present an optimisation method for the assignment of photometric galaxies into a chosen set of redshift bins. This is achieved by combining simulated annealing, an optimisation algorithm inspired by solid-state physics, with an unsupervised machine learning method, a self-organising 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 optimise 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 minimises 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 LSST 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.

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