发表机构
Instituto de Física de Cantabria; Laboratoire d’Annecy de Physique Theorique (LAPTh), CNRS/USMB（坎塔布里亚物理研究所; 阿讷西理论物理实验室，法国国家科学研究中心/萨瓦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究首次从第一性原理推导LIM功率谱的超样本协方差，结合晕模型与标准微扰理论，经N体模拟验证后发现该协方差对高信噪比LIM巡天的参数推断影响显著。
AI 中文摘要
在本研究中，我们首次从第一性原理推导了线强度映射（LIM）功率谱的超样本协方差（SSC），同时首次附带推导了箱内非高斯协方差贡献。以往研究通常采用高斯近似或从模拟数据、实际观测数据得到的估计值来建模LIM功率谱协方差，忽略了所观测的有限体积是否处于宇宙学过密区域带来的不确定性。这种被称为SSC（或依语境称为场间方差）的贡献无法从观测数据中估计，但对于正确推断全局量（即集合平均参数，而非仅观测天区内的实际值）至关重要。我们的推导结合了晕模型和标准微扰理论，可捕捉协方差的非线性与非高斯性。在将预测结果与带示踪粒子的N体模拟成功验证后，我们探究了当前及未来LIM实验相关的不同场景，量化了箱内非高斯贡献与SSC的相对重要性。研究发现，新推导的LIM功率谱协方差贡献在中等和小尺度上至关重要，尤其当协方差不受仪器噪声主导时；SSC相对于其他协方差贡献的相对重要性大致与巡天体积无关，但取决于每条谱线和红移对应的功率谱对大尺度模式的特定响应。因此，对于当前及下一代高信噪比LIM巡天的参数推断，SSC的影响将日益显著。
英文摘要
In this work, we provide the first derivation of the line-intensity mapping (LIM) power spectrum super-sample covariance (SSC) from first principles, and also derive as a by-product the non-Gaussian in-box contributions to the covariance for the first time. Previous studies have typically modelled the LIM power spectrum covariance using either the Gaussian approximation or estimates obtained from mocks or the data itself, neglecting uncertainties related to whether the limited volume surveyed sits in a cosmological overdensity. This contribution, known as the SSC or, depending on the context, the field-to-field variance, cannot be estimated from the data, but it is crucial for a correct inference of global quantities, i.e., for ensemble-averaged parameters rather than the actual values just within the patch of the Universe observed. For our derivation, we employ a combination of the halo model and standard perturbation theory that allows us to capture the nonlinearity and non-Gaussianity of the covariance. After a successful validation of our predictions against painted N-body simulations, we explore different scenarios related to current and future LIM experiments, quantifying the relative importance of the non-Gaussian in-box and SSC. We find that the newly derived contributions to the LIM power spectrum covariance are crucial at intermediate and small scales, especially for cases in which the covariance is not dominated by instrumental noise. We find that the relative relevance of the SSC with respect to the other covariance contributions is roughly independent of the survey volume, but does depend on the specific response of the power spectrum to large-scale modes for each line and redshift. Therefore, the impact of the SSC will be increasingly significant for parameter inference from the current and the next generation high signal-to-noise LIM surveys.
Comments36 Pages (27 main-text- and 4 appendix-pages), 9 Figures
