When combining data sets to perform parameter inference, the results will be unreliable if there are unknown systematics in data or models. Here we introduce a flexible methodology, BACCUS: BAyesian Conservative Constraints and Unknown Systematics, which deals in a conservative way with the problem of data combination, for any degree of tension between experiments. We introduce hyperparameters that describe a bias in each model parameter for each class of experiments. A conservative posterior for the model parameters is then obtained by marginalization both over these unknown shifts and over the width of their prior. We contrast this approach with an existing hyperparameter method in which each individual likelihood is scaled, comparing the performance of each approach and their combination in application to some idealized models. Using only these rescaling hyperparameters is not a suitable approach for the current observational situation, in which internal null tests of the errors are passed, and yet different experiments prefer models that are in poor agreement. The possible existence of large shift systematics cannot be constrained with a small number of data sets, leading to extended tails on the conservative posterior distributions. We illustrate our method with the case of the $H_0$ tension between results from the cosmic distance ladder and physical measurements that rely on the standard cosmological model.
Cosmology and modified GW propagation from the BNS mass function at third-generation detector networks
Institut de Física d’Altes Energies (IFAE), Barcelona Institute of Science and Technology（高等能源物理研究所，巴塞罗那科学技术学院）；Département de Physique Théorique, Université de Genève（日内瓦大学理论物理系）；Gravitational Wave Science Center (GWSC), Université de Genève（日内瓦大学引力波科学中心）；Aix-Marseille Université, Université de Toulon, CNRS, CPT（艾克斯-马赛大学，土伦大学，法国国家科学研究中心，CPT）
The Atacama Cosmology Telescope: DR6 Power Spectrum Foreground Model and Validation
Universite Paris Cite, CNRS, Astroparticule et Cosmologie（巴黎西岱大学、法国国家科学研究中心、亚原子粒子与宇宙学）；Cardiff University（卡迪夫大学）；Columbia University（哥伦比亚大学）；Flatiron Institute（平顿研究所）；Princeton University（普林斯顿大学）；Cornell University（康奈尔大学）；Kavli Institute for Cosmology Cambridge（卡弗里宇宙学剑桥研究所）；University of California, Riverside（加州大学河滨分校）；Stanford University（斯坦福大学）；California Institute of Technology（加州理工学院）；Jet Propulsion Laboratory（喷气推进实验室）；University of Toronto（多伦多大学）；Arizona State University（亚利桑那州立大学）；University of Oxford（牛津大学）；IJCLab（伊吉克拉布）；University of KwaZulu-Natal（夸祖鲁-纳塔尔大学）；Institute of Theoretical Astrophysics, University of Oslo（奥斯陆大学理论天体物理研究所）；Haverford College（哈弗福德学院）；SLAC National Accelerator Laboratory（斯坦福直线加速器中心）；Stony Brook University（石溪大学）；Carnegie Mellon University（卡内基梅隆大学）；NASA/Goddard Space Flight Center（美国宇航局戈达德太空飞行中心）
