Speaker
Description
Maximising the information that can be extracted from weak lensing measurements is a key goal for LSST and Euclid. This is typically achieved through statistics that are complementary to the cosmic shear two-point correlation function.
In this talk I will present the development of two such complementary statistics.
The first is weak lensing peaks. Typically, only the peak abundance is used to test cosmology. I will show how the clustering of peaks can be used to significantly improve the sensitivity of weak lensing peaks to cosmological parameters.
Secondly, I will present weak lensing voids, a new weak lensing statistic, that corresponds to a new void definition, which also captures a wealth of cosmological information.
I use the cosmoSLICS, FORGE, and BRIDGE simulations to measure the weak lensing peak and void statistics for a range of cosmological and gravitational parameters. The simulation data is used to train a machine learning emulator, which is used to generate parameter constraint forecasts from mock observations.
I will show that both peaks and voids can double our constraining power on cosmological models, relative to the standard shear two-point correlation function.
Finally, I will present ongoing work applying these methods to the DES Y3 data.
| Abstract title | Cosmology with weak lensing peaks and voids |
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