Project description
New physical insights from the large-scale structures in our Universe
The Vera C. Rubin Observatory is a huge telescope that will observe the Universe in unprecedented detail. The resulting catalogues will contain billions of galaxies and other exotic objects, such as the electromagnetic counterparts of gravitational wave sources. The EU-funded CosmicExplorer project aims to derive new physical insight from these catalogues and will address three main challenges: reducing the computational expense of accurately modelling these complex data; teasing out subtle signals from (possibly unknown) systematic errors; and developing new probes of the large-scale structure of the Universe. Combining advanced data emulation techniques, AI methods and hierarchical Bayesian models, CosmicExplorer aims to achieve a breakthrough in our understanding of physical cosmology.
Objective
The standard model of cosmology postulates ingredients that are not present in the standard model of particle physics – dark matter, dark energy, and a primordial origin for cosmic structure. Their physical nature remains a mystery. In the past few years, it has become clear that formidable modelling and data analysis challenges stand in the way of establishing how these ingredients fit into fundamental physics.
Starting in 2023, we have an opportunity to uncover the physical underpinning of the cosmological model, using a new window on the universe opened by the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST). Just in its first year, LSST will see more of the universe than all previous surveys combined. By repeatedly mapping huge sky areas, it will create the first motion picture of our universe. The resulting catalogues will contain billions of galaxies and other, more exotic objects such as the electromagnetic counterparts of gravitational wave sources.
This project aims to turn these catalogues into physical insight by solving three pressing challenges: (1) the formidable computational expense of accurately modelling this complex data; (2) the need to tease out subtle signals from (possibly unknown) systematics; (3) capturing the rich information in the “cosmic web” of galaxies tracing the large-scale structure of the universe, inaccessible by standard analysis methods. I will achieve these aims through a unique approach, combining innovative data emulation techniques to accelerate forward-modelling, a ground-breaking ‘explainable artificial intelligence’ approach to scientific discovery, and advanced hierarchical Bayesian methods to achieve not just precision (small error-bars) but accuracy (unbiased results). The combination of the unique tools I will develop, and LSST’s new view of the universe, opens up a vast discovery space. I will explore this space with the aim of achieving a breakthrough in our understanding of physical cosmology.
Fields of science
- natural sciencesphysical sciencesastronomyobservational astronomygravitational waves
- natural sciencesmathematicsapplied mathematicsstatistics and probabilitybayesian statistics
- natural sciencesphysical sciencesastronomyastrophysicsdark matter
- natural sciencesphysical sciencesastronomyphysical cosmology
Programme(s)
Topic(s)
Funding Scheme
ERC-ADG - Advanced GrantHost institution
CB2 1TN Cambridge
United Kingdom