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Capitalizing on Gravitational Shear


Our Universe appears to be filled with mysterious ingredients: 25 per cent appears to be dark matter, perhaps an as-yet undiscovered particle, and 70 per cent seems to be a bizarre fluid, dubbed dark energy, for which there is no satisfactory theory. Solving the dark energy problem is the most pressing question in cosmology today. It is possible that dark energy does not exist at all, and instead Einstein s theory of General Relativity is flawed. Cosmologists hope to measure the properties of the dark energy using the next generation of cosmological observations, in which I am playing a leading role. I believe the most promising technique to crack the dark energy problem is gravitational shear, in which images of distant galaxies are distorted as they pass through the intervening dark matter distribution. Analysis of the distortions allows a map of the dark matter to be reconstructed; by examining the dark matter distribution we uncover the nature of the apparent dark energy. However to capitalize on the great potential of gravitational shear we must measure incredibly small image distortions in the presence of much larger image modifications that occur in the measurement process. I am proposing a fresh look at this problem using an inter-disciplinary approach in collaboration with computer scientists. This grant would enable my team to play a central role in the key results from the upcoming Dark Energy Survey. We would further capitalize on the gravitational shear signal by moving away from the current dark energy bandwagon by instead focusing on testing General Relativity using novel approaches. Our work will produce results which will lead the next Einstein to solve the biggest puzzle in cosmology, and arguably physics.

Call for proposal

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Oxford road
M13 9PL Manchester
United Kingdom

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North West (England) Greater Manchester Manchester
Activity type
Higher or Secondary Education Establishments
Principal investigator
Sarah Louise Bridle (Prof.)
Administrative Contact
Liz Fay (Ms.)
EU contribution
No data

Beneficiaries (2)