PIRSA:16100063

Video URL

https://pirsa.org/16100063

Cosmic shear as a probe of galaxy formation physics

Foreman, S. (2016). Cosmic shear as a probe of galaxy formation physics. Perimeter Institute for Theoretical Physics. https://pirsa.org/16100063

Foreman, Simon. Cosmic shear as a probe of galaxy formation physics. Perimeter Institute for Theoretical Physics, Oct. 18, 2016, https://pirsa.org/16100063 

          @misc{ scivideos_PIRSA:16100063,
            doi = {10.48660/16100063},
            url = {https://pirsa.org/16100063},
            author = {Foreman, Simon},
            keywords = {Cosmology},
            language = {en},
            title = {Cosmic shear as a probe of galaxy formation physics},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2016},
            month = {oct},
            note = {PIRSA:16100063 see, \url{https://scivideos.org/index.php/pirsa/16100063}}
          }

Simon Foreman Arizona State University

Talk numberPIRSA:16100063
DOI10.48660/16100063
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

The precision of current and future cosmological observations at Megaparsec scales demands a detailed understanding of the effects of baryonic processes on the clustering of matter at these scales. In this talk, I will explore how to use measurements of cosmic shear to constrain the impact of these processes on the total matter power spectrum. I will present forecasts demonstrating that shear measurements from Stage III surveys (such as DES and HSC) and beyond will be able to strongly constrain (or even rule out) current simulation-based implementations of baryonic physics (such as AGN feedback). These forecasts make use of a model-independent parametrization of the impact of baryons on the matter power spectrum, and marginalize over several key observational and theoretical systematics. The results indicate that cosmic shear can likely be used as a robust probe of the physics of galaxy formation, and provide an important observational input for future simulations or modeling efforts.