论文标题
DESI传统调查中星系与多上述图像堆栈之间的虚假相关性
Spurious correlations between galaxies and multi-epoch image stacks in the DESI Legacy Surveys
论文作者
论文摘要
在星系调查的宇宙学分析中，系统偏见的不可忽略的来源是前景和可变图像特征（例如观察条件）引起的天内调制。标准缓解技术在观察到的星系密度场和潜在污染物的天空图之间进行回归。这样的地图是临时，有损的摘要，这些摘要是对调查有助的共同添加的曝光。我们提出了一种解决这一局限性的方法，并提取了观察到的星系分布与单个上古暴露的任意堆栈之间的虚假相关性。我们研究了Desi传统调查的三个区域（北，南，DES）的四种类型的星系（LRG，ELG，QSO，LBGS），这导致了十二个样本，具有不同水平和污染类型的样品。我们发现，在所有情况下，新技术都胜过传统技术，并且能够消除更高水平的污染。这为新方法铺平了道路，这些方法可以从多上述星系调查数据中提取更多信息，并更有效地减轻大规模偏见。
A non-negligible source of systematic bias in cosmological analyses of galaxy surveys is the on-sky modulation caused by foregrounds and variable image characteristics such as observing conditions. Standard mitigation techniques perform a regression between the observed galaxy density field and sky maps of the potential contaminants. Such maps are ad-hoc, lossy summaries of the heterogeneous sets of co-added exposures that contribute to the survey. We present a methodology to address this limitation, and extract the spurious correlations between the observed distribution of galaxies and arbitrary stacks of single-epoch exposures. We study four types of galaxies (LRGs, ELGs, QSOs, LBGs) in the three regions of the DESI Legacy Surveys (North, South, DES), which results in twelve samples with varying levels and type of contamination. We find that the new technique outperforms the traditional ones in all cases, and is able to remove higher levels of contamination. This paves the way for new methods that extract more information from multi-epoch galaxy survey data and mitigate large-scale biases more effectively.
