觉醒CatWISE：利用基于模拟的推断来解释黄道偏差并确认宇宙二极子过剩
Wising up to CatWISE: using simulation-based inference to interpret the ecliptic bias and confirm the cosmic dipole excess
AI总结：
本文首次利用基于模拟的推断方法分析CatWISE样本，发现其二极子过剩现象，并揭示了光度误差和扫描规律的相关性可能造成系统性偏差。
摘要：
我们首次将基于模拟的推断（'SBI'）应用于宇宙二极子问题，测量CatWISE2020（'CatWISE'）样本中类星体在天空上的分布。我们显示，CatWISE中的四极子各向异性可以归因于WISE的扫描规律与W1和W2星等的光度不确定性之间的相关性，导致一个随天空位置变化的爱德ington偏差。在明确用SBI建模之后，我们使用神经似然估计器找到CatWISE的二极子的后验分布，确认了二极子的大小是CMB预期值的两倍，但更严重偏离CMB方向（约3σ）。我们还利用所学的似然性来推断贝叶斯证据，发现增加CatWISE光度误差尺度的模型最受青睐。这表明样本的误差可能被低估，或者存在其他未解决的系统性因素产生相同效果。尽管我们的结果表明宇宙二极子过剩是ΛCDM模型中的持续问题，但展示SBI可以解开影响任何真实天文数据衍生样本的微妙和复杂系统性问题。
英文摘要：
We apply Simulation-Based Inference ('SBI') to the cosmic dipole problem for the first time, measuring the distribution of quasar counts over the sky in the CatWISE2020 ('CatWISE') sample. We show that the quadrupole anisotropy in CatWISE can be attributed to the correlation between WISE's scanning law and photometric uncertainty in the $W1$ and $W2$ magnitudes, inducing an Eddington bias which varies with sky position. After explicitly modelling this with SBI, we use a neural likelihood estimator to find the posterior distribution for CatWISE's dipole, confirming the presence of a dipole twice as large as the CMB expectation but more seriously misaligned with the CMB direction ($\approx 3 σ$). We also use our learned likelihood to infer the Bayesian evidence, learning that models which increase the scale of CatWISE's photometric errors are most favoured. This is strong evidence that the sample's errors are underestimated or that there is an additional, unresolved systematic producing the same effect as Eddington bias. While our results indicate that the cosmic dipole excess is a persistent issue for $Λ$CDM, we showcase that SBI can untangle the subtle and complex systematic issues affecting any sample derived from real astronomical data.
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