使用模糊原型和自组织映射推导测光红移。II。使用模拟数据比较采样技术
Deriving Photometric Redshifts using Fuzzy Archetypes and Self-Organizing Maps. II. Comparing Sampling Techniques Using Mock Data
摘要：
在一篇配套论文中，我们提出将大量的“模糊原型”与自组织映射（SOM）相结合，以数据驱动的方式导出光度红移。在本文中，我们研究了几种采样方法的性能，这些方法建立在使用模拟目录来近似模拟LSST的一般思想的基础上（$ugrizY$）和欧几里得在固定LSST $Y=24$磁下，从$z=0-6$获得的（$YJH$）数据。我们测试了八种不同的方法：两种蛮力方法，两种基于马尔可夫链蒙特卡罗（MCMC）的方法，两种分层抽样方法，和两个“快速搜索”的方法，在初始SOM训练过程中得出的数量的基础上。我们发现大多数方法都能很好地处理小的灾难性异常值，并且能够鲁棒地识别多模态和/或约束不佳的红移概率分布函数。一旦这些不安全的对象被删除，结果通常是在严格的精度要求，以满足欧几里德弱透镜的目标，大多数红移以上$z \sim 0.8$。这些结果表明，我们的数据聚类为基础的方法的实用性，并强调其有效性，以获得快速和准确的照片Z的使用大量的模板。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
