PIRSA:18040098

Video URL

https://pirsa.org/18040098

Prospects for CMB lensing-galaxy clustering cross-correlations and initial condition reconstruction

Schmittfull, M. (2018). Prospects for CMB lensing-galaxy clustering cross-correlations and initial condition reconstruction. Perimeter Institute for Theoretical Physics. https://pirsa.org/18040098

Schmittfull, Marcel. Prospects for CMB lensing-galaxy clustering cross-correlations and initial condition reconstruction. Perimeter Institute for Theoretical Physics, Apr. 24, 2018, https://pirsa.org/18040098 

          @misc{ scivideos_PIRSA:18040098,
            doi = {10.48660/18040098},
            url = {https://pirsa.org/18040098},
            author = {Schmittfull, Marcel},
            keywords = {Cosmology},
            language = {en},
            title = {Prospects for CMB lensing-galaxy clustering cross-correlations and initial condition reconstruction},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2018},
            month = {apr},
            note = {PIRSA:18040098 see, \url{https://scivideos.org/pirsa/18040098}}
          }

Marcel Schmittfull Institute for Advanced Study (IAS)

Talk numberPIRSA:18040098
DOI10.48660/18040098
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

The lensing convergence measurable with future CMB experiments will be highly correlated with the clustering of galaxies that will be observed by imaging surveys such as LSST. I will discuss prospects for using that cross-correlation signal to constrain local primordial non-Gaussianity, the amplitude of matter fluctuations as a function of redshift, halo bias, and possibly the sum of neutrino masses. A key limitation for such analyses and large-scale structure analyses in general is that the mapping from initial conditions to observables is nonlinear for wavenumbers k>0.1h/Mpc. This can destroy cosmological information or move it to non-Gaussian tails of the probability distribution that are difficult to measure. I will describe how we can use recently developed initial condition reconstruction methods to help us recover some of that information in the nonlinear regime.