减轻权重：缓解3×2pt分析中灾难性异常值对参数偏差的影响
Taking the Weight Off: Mitigating Parameter Bias from Catastrophic Outliers in 3$\times$2pt Analysis
AI总结：
本文提出了一种在3×2pt分析中减轻灾难性红移误差对参数偏差影响的加权方案，通过降低易受污染的数据元素权重，有效减少宇宙学参数偏移至1σ以下。
摘要：
第IV阶段宇宙学调查将以前所未有的精度测绘宇宙，将统计不确定性降低到如此低的水平，以至于未建模的系统误差可能显著偏移推断结果。特别是，光谱红移（photo-z）误差和固有对齐（IA）必须被稳健地考虑，以确保准确推断宇宙学参数。第IV阶段调查的深度增加加剧了这些挑战，因为它们产生了信号与噪声比低的星系群体，容易产生不准确的光谱红移测量。灾难性误识别的红移在3×2pt推断中尤其成问题，因为3×2pt推断结合了弱透镜和星系聚类信息。我们证明，即使适度的异常值比例（例如5%）也会导致宇宙学参数估计的显著偏移：在Ω_M和σ_8上高达1.8σ，在IA红移演变参数η上超过8σ。为了解决这个问题，我们引入了一种灵活的加权方案，在似然水平上对数据向量中最易受污染影响的元素进行降权。这种方法在不扩大参数空间的情况下缓解了偏移，将宇宙学参数偏移降低到1σ以下，而不会显著降低约束能力。我们的方法为未来的分析提供了一种实用的解决方案，使在存在灾难性红移误差的情况下能够实现稳健的宇宙学推断。
英文摘要：
Stage IV cosmological surveys will map the universe with unprecedented precision, reducing statistical uncertainties to levels where unmodelled systematics can significantly bias inference. In particular, photometric redshift (photo-z) errors and intrinsic alignments (IA) must be robustly accounted for to ensure accurate inference of cosmological parameters. The increasing depth of Stage IV surveys exacerbates these challenges by producing low signal-to-noise galaxy populations prone to inaccurate photo-z measurements. Catastrophically misidentified redshifts are especially problematic for 3$\times$2pt inferences that combine weak lensing and galaxy clustering information. We demonstrate that even modest outlier fractions (e.g. 5%) can lead to substantial biases in cosmological parameter estimates: up to 1.8$σ$ in $Ω_M$ and $σ_8$, and over 8$σ$ in the IA redshift evolution parameter $η$. To address this, we introduce a flexible weighting scheme at the likelihood level that down-weights the most contamination-sensitive elements of the data vector during inference. This method mitigates biases without inflating the parameter space, reducing cosmological parameter biases to below 1$σ$ without substantially degrading constraining power. Our approach offers a practical solution for future analyses, enabling robust cosmological inference in the presence of catastrophic redshift errors.
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