星系密度涨落与成团测量中乘法性和加法性系统误差的联合推断
Joint inference of multiplicative and additive systematics in galaxy density fluctuations and clustering measurements
AI总结：
提出一种基于极大似然估计的新方法，通过联合推断星系超密度场污染物，有效缓解星系成团测量中的加法与乘法系统误差，将信号纠正至亚百分点水平。
摘要：
星系成团测量是探测宇宙物质密度场的关键手段。随着精确宇宙学时代的到来，巡天观测依赖于对成团信号的精确测量以进行有意义的宇宙学分析。然而，系统性污染物的存在会使观测到的星系数密度产生偏差，进而导致星系两点统计量出现偏差。随着统计不确定性的减小，纠正这些系统性污染物对于无偏宇宙学分析变得越来越重要。我们提出并验证了一种新方法，该方法通过使用极大似然估计（MLE）对星系超密度场（一点函数）中的污染物进行联合推断，来理解和缓解星系成团测量（两点函数）中的加法性和乘法性系统误差。我们使用类KiDS模拟星系目录和合成系统误差模板图对该方法进行了测试。我们通过量化推断的观测星系成团信号与真实信号之间关系的不确定性和可能偏差，估计了此类缓解措施的宇宙学影响。我们的方法将成团信号稳健地纠正到亚百分点水平，并在我们测试的场景中，将众多加法性和乘法性系统误差从1.5σ降低到小于0.1σ。此外，我们提供了一种经验方法，用于识别特定系统误差污染星系数密度的函数形式（加法性、乘法性或其他）。尽管这种方法是针对污染星系数密度的系统误差进行测试和设计的，但这些方法可以扩展到其他两点相关测量的系统误差缓解中。
英文摘要：
Galaxy clustering measurements are a key probe of the matter density field in the Universe. With the era of precision cosmology upon us, surveys rely on precise measurements of the clustering signal for meaningful cosmological analysis. However, the presence of systematic contaminants can bias the observed galaxy number density, and thereby bias the galaxy two-point statistics. As the statistical uncertainties get smaller, correcting for these systematic contaminants becomes increasingly important for unbiased cosmological analysis. We present and validate a new method for understanding and mitigating both additive and multiplicative systematics in galaxy clustering measurements (two-point function) by joint inference of contaminants in the galaxy overdensity field (one-point function) using a maximum-likelihood estimator (MLE). We test this methodology with KiDS-like mock galaxy catalogs and synthetic systematic template maps. We estimate the cosmological impact of such mitigation by quantifying uncertainties and possible biases in the inferred relationship between the observed and the true galaxy clustering signal. Our method robustly corrects the clustering signal to the sub-percent level and reduces numerous additive and multiplicative systematics from $1.5 σ$ to less than $0.1σ$ for the scenarios we tested. In addition, we provide an empirical approach to identifying the functional form (additive, multiplicative, or other) by which specific systematics contaminate the galaxy number density. Even though this approach is tested and geared towards systematics contaminating the galaxy number density, the methods can be extended to systematics mitigation for other two-point correlation measurements.
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