APA

Schaan, E. (2016). Understanding large-scale structure from the CMB. Perimeter Institute. https://pirsa.org/16100045

MLA

Schaan, Emmanuel. Understanding large-scale structure from the CMB. Perimeter Institute, Oct. 04, 2016, https://pirsa.org/16100045

BibTex

@misc{ pirsa_PIRSA:16100045,
  doi = {10.48660/16100045},
  url = {https://pirsa.org/16100045},
  author = {Schaan, Emmanuel},
  keywords = {Cosmology},
  language = {en},
  title = {Understanding large-scale structure from the CMB},
  publisher = {Perimeter Institute},
  year = {2016},
  month = {oct},
  note = {PIRSA:16100045 see, \url{https://pirsa.org}}
}
            

Abstract

In this seminar, I will present two promising ways in which the cosmic microwave background (CMB) sheds light on critical uncertain physics and systematics of the large-scale structure.
       Shear calibration with CMB lensing (arXiv:1607.01761):
Realizing the full potential of upcoming weak lensing surveys requires an exquisite understanding of the errors in galaxy shape estimation. In particular, such errors lead to a multiplicative bias in the shear, degenerate with the matter density parameter and the amplitude of fluctuations. Its redshift-evolution can hide the true evolution of the growth of structure, which probes dark energy and possible modifications to general relativity. I will show that CMB lensing from a stage 4 experiment (CMB S4) can self-calibrate the shear for an LSST-like optical lensing experiment. This holds in the presence of photo-z errors and intrinsic alignment.
       Evidence for the kinematic Sunyaev-Zel'dovich (kSZ) effect (arXiv:1510.06442); cluster energetics:

Through the kSZ effect, the baryon momentum field is imprinted on the CMB. I will report significant evidence for the kSZ effect from ACTPol and peculiar velocities reconstructed from BOSS. I will present the prospects for constraining cluster gas profiles and energetics from the kSZ effect with SPT-3G, AdvACT and CMB S4. This will provide constraints for galaxy formation and feedback models.