发表机构
National Astronomical Observatories, Chinese Academy of Sciences; Department of Astronomy, Tsinghua University; University of Chinese Academy of Sciences; Institute of Cosmology and Gravitation, University of Portsmouth; Kavli IPMU (WPI), UTIAS, The University of Tokyo; Yukawa Institute for Theoretical Physics, Kyoto University（中国科学院国家天文台; 清华大学天文学系; 中国科学院大学; 朴茨茅斯宇宙学与引力研究所; 东京大学 Kavli 宇宙物理学与数学研究所（WPI）、UTIAS; 京都大学汤川理论物理研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发了一种半解析高斯协方差模型，可用于联合分析星系重建前、重建后及交叉全形状功率谱，其宇宙学约束结果与模拟数值协方差一致。
AI 中文摘要
我们应用高斯协方差形式主义，开发了一种半解析协方差模型，用于联合分析星系功率谱的重建前、重建后及交叉全形状数据。我们利用位移场统计对重建产生的尺度相关交叉散粒噪声进行建模，并引入了一种直接测量该项的新估计量。以测得的功率谱和建模的散粒噪声预测作为输入，我们构建了高斯协方差，同时考虑了重建前与重建后密度场之间的相关性。我们用模拟星表验证了所得的半解析高斯协方差。基于模拟器的参数推断表明，该半解析高斯协方差充分捕捉了完整数据向量（$P_{\rm pre}$、$P_{\rm post}$、$P_{\rm cross}$）协方差结构的主要贡献。在对这三种功率谱的联合拟合中，它在采用的拟合范围内给出的宇宙学约束与使用基于模拟的数值协方差得到的约束一致：$P_{\rm pre}$和$P_{\rm post}$的$k_{\rm max}=0.18\,h\,{\ m Mpc}^{-1}$，$P_{\rm cross}$的$k_{\rm max}=0.12\,h\,{\ m Mpc}^{-1}$。
英文摘要
We apply the Gaussian covariance formalism to develop a semi-analytical covariance model for the joint analysis of pre-reconstruction, post-reconstruction, and cross full-shape galaxy power spectra. We model the reconstruction-reduced, scale-dependent cross shot noise using displacement-field statistics and introduce a new estimator that directly measures this term. Using the measured power spectra and the modeled shot-noise predictions as inputs, we construct the Gaussian covariance while accounting for correlations between the pre- and post-reconstruction density fields. We validate the resulting semi-analytical Gaussian covariance against mock catalogues. Using emulator-based parameter inference, we demonstrate that the semi-analytical Gaussian covariance adequately captures the dominant contribution to the covariance structure of the full data vector ($P_{\ell}^{\rm pre}, P_{\ell}^{\rm post}, P_{\ell}^{\rm cross}$). For the joint fit to these three power spectra, it yields cosmological constraints consistent with those obtained using the mock-based numerical covariance over the adopted fitting ranges: $k_{\rm max}=0.18\,h\,{\rm Mpc}^{-1}$ for $P_{\rm pre}$ and $P_{\rm post}$, and $k_{\rm max}=0.12\,h\,{\rm Mpc}^{-1}$ for $P_{\rm cross}$.
Comments17 pages, 12 figures; accepted for publication in JCAP
