Resolving cosmological tensions with diverse data, novel theories and Bayesian machine learning

我们的宇宙正在以比我们预期更快的速度膨胀。标准的宇宙学模型预测的哈勃常数与我们测量的结果有很大不同。这远不是一个问题，这是令人兴奋的，因为它表明需要一个新的宇宙模型。然而，尽管经过了三年的努力，宇宙学家仍未能解决这个宇宙难题。首先，没有一种单一的理论解决方案能够令人满意地解决所有差异。其次，将新物理学从测量误差中分离出来是一个具有挑战性的悬而未决的问题。第三，我们的模拟和数据分析管道是在标准模型的背景下设计和调整的，即使是最仔细设计的方法也可能产生偏差。这个雄心勃勃的项目提出要解决以上三个问题，并为理论和数据分析发现和建立下一个宇宙学范式。一项由PI、三名博士后和四名博士后在五年时间里开展的理论、推理和观测研究的连锁计划将旨在同时解决宇宙学理论和数据处理方面的紧张。该项目的主要目标是(A)解决使用新的宇宙标准模型进行的宇宙学观测与下一代数值技术之间的紧张关系(B)在我们的宇宙学分析工具包的核心建立无似然推理，为未来的大宇宙学数据的冲击做准备，以及(C)汇集一套不同的宇宙学和粒子物理数据集，并将它们组织在一个连贯的统计框架中。这是一项重要和实质性的研究工作，只有欧洲资源中心的起始拨款才能支持这项工作。

Our Universe is expanding faster than we expected. The standard model of cosmology predicts a Hubble constant which differs substantially from what we measure it to be. Far from being a problem, this is exciting as it indicates the need for a new model of the Universe. Despite three years of effort however, cosmologists have been unable to resolve this cosmic conundrum.The reasons for this community failure are threefold. First, no single theoretical solution is capable of satisfactorily resolving all discrepancies. Second, disentangling new physics from measurement error is a challenging unsolved problem. Third, our simulation and data analysis pipelines have been designed and tuned in the context of the standard model, which can bias even the most carefully designed approach.This ambitious project proposes to resolve all three of the above and uncover and establish the next cosmological paradigm for theory and data analysis. An interlocking programme of theory, inference and observational research, undertaken by the PI, three postdocs and four PhD students over five years will aim to simultaneously resolve the tensions in both cosmological theories and data processing.The broad aims of the project are to (a) Resolve the tensions between cosmological observations with a new standard model of the universe and next-generation numerical techniques (b) Establish likelihood-free inference at the heart of our cosmological analysis toolkit in preparation for the future onslaught of big cosmological data, and (c) Bring together a diverse set of cosmological and particle physics datasets and organise them in a coherent statistical framework.This is an essential and substantial research effort which only an ERC starting grant can support.