发表机构
Università di Trento; Dipartimento di Fisica, Università degli Studi di Milano; INFN sezione di Milano; Instituto de Tecnologías en Detección y Astropartículas (CNEA, CONICET, UNSAM); Sapienza Università di Roma; Istituto Nazionale di Fisica Nucleare – Sezione di Roma1; Mohammed VI Polytechnic University（特伦托大学; 米兰大学物理系; 意大利国家核物理研究所米兰分部; 探测与天体粒子技术研究所（阿根廷原子能委员会、国家科学研究委员会、萨米特国立大学）; 罗马智慧大学; 意大利国家核物理研究所罗马一分部; 穆罕默德六世理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究在Commander2中实现频率相关噪声处理框架，经模拟验证其可提升CMB组分分离的后验估计准确性，为相关分析提供工具支持。
AI 中文摘要
宇宙微波背景（CMB）分析中使用的参数化组分分离方法通常假设仪器噪声在频率通道之间不相关。对于设计会自然引入通道间相关性的实验（如干涉测量或多波段测辐射热计系统），这种近似可能不再适用。本研究旨在提供一个一致的框架，将频率相关噪声纳入Commander2中，并在受控及受仪器驱动的模拟中量化其对组分分离的影响。我们对Commander2框架的振幅采样进行了泛化，以一致地处理密集的频率-频率噪声协方差矩阵，修改了似然评估和并行架构。该实现通过类CMB-S4简化配置的低分辨率模拟进行验证；我们将泛化后的框架应用于QUBIC测辐射热计干涉仪的受仪器驱动模拟，采用了端到端模拟得到的代表性噪声模型。验证测试显示采样参数与解析最大似然估计之间具有优异的收敛性和一致性。与对角噪声近似相比，考虑频率相关性可使最大似然残差分布的宽度平均减少多达8%，单个参数的宽度减少多达11%。在基于QUBIC的模拟中，考虑反相关噪声会使后验宽度减少约60%，而正相关测试则使后验宽度增加约40%。这表明忽略通道间相关性可能导致后验估计出现双向错误。本文提出的泛化实现使Commander2软件能够将此类相关性一致地传播到参数估计中，为未来CMB分析中评估其影响提供了框架。
英文摘要
Parametric component separation methods used in cosmic microwave background (CMB) analyses commonly assume instrumental noise to be uncorrelated between frequency channels. This approximation may become insufficient for experiments whose design naturally introduces cross-channel correlations, such as interferometric or multi-band bolometric systems. This work aims to provide a consistent framework to include frequency-correlated noise in Commander2 and to quantify its impact on component separation in controlled and instrument-motivated simulations. We generalized the amplitude sampling of the Commander2 framework to consistently handle dense frequency-frequency noise covariance matrices, modifying the likelihood evaluation and parallel architecture. The implementation is validated on low-resolution simulations of a simplified CMB-S4-like configuration. We applied the generalized framework to instrument-motivated simulations of the QUBIC bolometric interferometer, using representative noise models derived from end-to-end simulations. Validation tests show excellent convergence and agreement between sampled parameters and analytical maximum-likelihood estimates. Accounting for frequency correlations reduces the width of the maximum-likelihood residual distributions by up to 8% on average, and up to 11% for individual parameters, relative to the diagonal-noise approximation. In QUBIC-based simulations, accounting for an anti-correlated noise produces posterior widths reduced by about 60%, while a positive-correlation test increases them by about 40%. This shows that neglecting cross-channel correlations can lead to incorrect posterior estimates in either direction. The generalized implementation presented here enables the Commander2 software to consistently propagate such correlations into parameter estimation, providing a framework for assessing their impact in future CMB analyses.
