The universe is expanding faster than our best model of it predicts, and cosmologists have spent three years failing to explain why. This mismatch between theory and measurement—known as the Hubble tension—suggests the standard model of cosmology is incomplete. But no single tweak to the model fixes all the discrepancies, and existing data-analysis tools were designed around the old model, potentially biasing results. This project aims to break that deadlock by combining new theoretical models, diverse datasets from cosmology and particle physics, and a machine-learning technique called likelihood-free inference that does not assume the standard model is correct. If successful, the work would establish a new standard model of the universe and a new way of analysing cosmological data. This is fundamental science: it will not directly change anyone’s daily life. But past shifts in our understanding of the cosmos—from general relativity to the discovery of dark energy—have reshaped everything from satellite navigation to the search for new particles. A deeper, more accurate picture of the universe’s expansion could eventually feed into those same technologies.
View original technical description
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.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know