基于自组织映射的星系分布不完全性检验
Galaxy Distribution Incompleteness Testing Using Self-Organizing Maps
摘要：
利用分光巡天对光度测量样品的红移分布进行校准，受到分光巡天选择函数建模困难的困扰。在这项工作中，我们分析了这些选择函数如何影响红移的推断和量化的诱导偏差使用本地校准测试测光空间。这项研究是使用模拟的径向选择功能的光谱调查和伴随的模拟目录的光度星系调查目录。我们使用一个自组织映射来划分测光空间，并进行本地$χ^2$测试，以研究使用光谱数据进行校准的红移推断的概率校准。本工作的目的是研究未校正的选择函数在定标数据中对红移预测精度的影响，并批判性地讨论缓解方法。特别是，我们测试基于剔除的偏差校正技术，其目的是通过识别光谱校准数据很少的测光区域来消除红移校准偏差，并为未来的研究提出了途径。我们发现，在颜色-星等空间中去除光谱校准数据不足的区域并不能消除由选择函数引起的红移推断中的所有偏差。
英文摘要：
The calibration of redshift distributions for photometric samples using spectroscopic surveys is plagued by the difficulty in modelling the selection functions of spectroscopic surveys. In this work, we analyse how these selection functions impact redshift inference and quantify the induced biases using local calibration tests in photometry space. The study is carried out using simulations that mimic the radial selection function of a spectroscopic survey and an accompanying mock catalog of a photometric galaxy survey catalog. We use a self-organizing map to partition the photometry space and perform a local $χ^2$ test to study the probability calibration of redshift inferences that use the spectroscopic data for calibration. The goal of this work is to investigate the effect of uncorrected selection functions in the calibration data on redshift prediction accuracy and critically discuss mitigation methods. In particular we test culling-based bias correction techniques, that aim to remove redshift calibration biases by identifying regions in photometry with few spectroscopic calibration data, and propose avenues for future research. We found that removing regions in color-magnitude space that are underpopulated with spectroscopic calibration data does not remove all biases in redshift inference induced by the selection function.
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