Enhancing cosmic shear with the multiscale lensing probability density function

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 S-8 = sigma(8)root Qm/ 0 . 3 than the two-point shear correlation functions. F or Stage-IV surveys, we achieve > 90 per cent tighter constraints on S-8 , 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.

Automated summary

We quantified the cosmological constraining power of the lensing probability density function (PDF) by modeling it numerically and showed that multiscale PDFs offer considerable gains over two-point statistics. For a mock Stage-III survey, lensing PDFs yield 33% tighter constraints on the clustering parameter compared to the two-point shear correlation functions. For Stage-IV surveys, we achieve > 90% tighter constraints not only on the clustering parameter but also on the Hubble and dark energy equation-of-state parameters. Interestingly, combining these two probes only improves the constraints in the Stage-III setup, suggesting that upcoming surveys with lower noise levels will unleash the constraining power of the PDF.