Projects per year
Abstract / Description of output
We quantify the cosmological constraining power of the ‘lensing probability density function (PDF)’ – the one-point probability density of weak lensing convergence maps – by modelling this statistic numerically with an emulator trained on w cold dark matter cosmic shear simulations. After validating our methods on Gaussian and lognormal fields, we show that ‘multiscale’ PDFs – measured from maps with multiple levels of smoothing – offer considerable gains over two-point statistics, owing to their ability to extract non-Gaussian information: For a mock Stage-III survey, lensing PDFs yield 33 per cent tighter constraints on the clustering parameter S8=σ8Ωm/0.3−−−−−−√ than the two-point shear correlation functions. For Stage-IV surveys, we achieve >90 per cent tighter constraints on S8, but also on the Hubble and dark energy equation-of-state parameters. Interestingly, we find improvements when combining these two probes only in our Stage-III set-up; in the Stage-IV scenario the lensing PDFs contain all information from the standard two-point statistics and more. This suggests that while these two probes are currently complementary, the lower noise levels of upcoming surveys will unleash the constraining power of the PDF.
Keywords / Materials (for Non-textual outputs)
- Gravitational lensing
- Observations - cosmological parameters - large-scale structure of Universe
- Weak - cosmology
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- 2 Finished
Shining Light in the Dark: Enhancing Insights into the Dark Universe with Gravitational Lensing and Machine Learning
28/03/22 → 27/03/23