标题： 利用模糊原型和自组织映射推导 photometric redshifts。 比较使用 mock 数据的采样技术 显示英文标题

标题： Deriving Photometric Redshifts using Fuzzy Archetypes and Self-Organizing Maps. II. Comparing Sampling Techniques Using Mock Data

摘要： 在一篇相关论文中，我们提出将大量“模糊原型”与自组织映射（SOM）结合，以数据驱动的方式推导出光谱红移。 在本文中，我们研究了几种基于这一总体思路的采样方法的表现，这些方法使用了一个模拟目录，该目录旨在近似模拟来自 LSST（$ugrizY$）和欧几里得（$YJH$）任务在固定 LSST$Y=24$星等下的数据$z=0-6$。 我们测试了八种不同的方法：两种蛮力法，两种基于马尔可夫链蒙特卡洛（MCMC）的方法，两种分层抽样方法，以及两种基于初始 SOM 训练过程中得出量度的“快速搜索”方法。 我们发现大多数方法表现合理，灾难性异常值比例较小，并能够稳健地识别出多模态和/或约束不足的红移概率分布函数。 一旦排除这些不确定对象后，对于高于$z \sim 0.8$的大多数红移，结果通常符合满足欧几里得弱透镜目标所需的严格精度要求。 这些结果证明了我们基于数据聚类方法的有效性，并突显了使用大量模板快速准确推导光谱红移的能力。 显示英文摘要

摘要： In a companion paper, we proposed combining large numbers of "fuzzy archetypes" with Self-Organizing Maps (SOMs) to derive photometric redshifts in a data-driven way. In this paper, we investigate the performance of several sampling approaches that build on this general idea using a mock catalog designed to approximately simulate LSST ($ugrizY$) and Euclid ($YJH$) data from $z=0-6$ at fixed LSST $Y=24$ mag. We test eight different approaches: two brute-force methods, two Markov Chain Monte Carlo (MCMC)-based methods, two hierarchical sampling methods, and two "quick-search" methods based on quantities derived during the initial SOM training process. We find most methods perform reasonably well with small catastrophic outlier fractions and are able to robustly identify redshift probability distribution functions that are multi-modal and/or poorly constrained. Once these insecure objects are removed, the results are generally in good agreement with the strict accuracy requirements necessary to meet Euclid weak lensing goals for most redshifts above $z \sim 0.8$. These results demonstrate the utility of our data clustering-based approach and highlight its effectiveness to derive quick and accurate photo-z's using large numbers of templates.