利用小角尺度截断估计量缓解DESI光纤分配不完整性对两点聚类的影响
Mitigation of DESI fiber assignment incompleteness effect on two-point clustering with small angular scale truncated estimators
AI总结：
本文提出通过截断估计量中的小角尺度来缓解DESI巡天光纤分配不完整性对两点聚类的影响，并推导了相应的修改窗口，验证表明该方法能以低计算成本和极小统计损失恢复无偏的宇宙学约束。
摘要：
我们提出了一种方法，通过从估计量中截断小角尺度，来缓解星系光谱巡天中光纤分配不完整性对两点功率谱和关联函数测量的影响。我们推导了相应的修改后的关联函数和功率谱窗口，以在理论预测中考虑小角尺度截断。我们在重现Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) 的模拟上验证了该方法，包含和不包含光纤分配的情况。我们表明，使用来自包含光纤分配不完整性的模拟的小角尺度截断估计量，相对于来自完整模拟的标准估计量，我们恢复了无偏的宇宙学约束。此外，我们提出了一种方法，通过对数据向量和窗口矩阵应用变换，来消除拟合对理论功率谱中高 $k$ 模式的敏感性。我们发现，我们的方法在低计算成本和极小统计损失的情况下，有效地缓解了光纤分配不完整性对两点关联函数和功率谱测量的影响。
英文摘要：
We present a method to mitigate the effects of fiber assignment incompleteness in two-point power spectrum and correlation function measurements from galaxy spectroscopic surveys, by truncating small angular scales from estimators. We derive the corresponding modified correlation function and power spectrum windows to account for the small angular scale truncation in the theory prediction. We validate this approach on simulations reproducing the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) with and without fiber assignment. We show that we recover unbiased cosmological constraints using small angular scale truncated estimators from simulations with fiber assignment incompleteness, with respect to standard estimators from complete simulations. Additionally, we present an approach to remove the sensitivity of the fits to high $k$ modes in the theoretical power spectrum, by applying a transformation to the data vector and window matrix. We find that our method efficiently mitigates the effect of fiber assignment incompleteness in two-point correlation function and power spectrum measurements, at low computational cost and with little statistical loss.
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