PIRSA:26060011 Conference Cosmology

DOI10.48660/26060011

Conference/School: Advancing Field-level and Simulation-based Inference for Cosmology

Ge, F. (2026). SPT-3G: Cosmology from CMB Lensing and Delensed EE Power Spectra Using 2019-2020 Polarization Data. Perimeter Institute. https://pirsa.org/26060011

Ge, Fei. SPT-3G: Cosmology from CMB Lensing and Delensed EE Power Spectra Using 2019-2020 Polarization Data. Perimeter Institute, Jun. 11, 2026, https://pirsa.org/26060011 

@misc{ pirsa_PIRSA:26060011,
  doi = {10.48660/26060011},
  url = {https://pirsa.org/26060011},
  author = {Ge, Fei},
  keywords = {Cosmology},
  language = {en},
  title = {SPT-3G: Cosmology from CMB Lensing and Delensed EE Power Spectra Using 2019-2020 Polarization Data},
  publisher = {Perimeter Institute},
  year = {2026},
  month = {jun},
  note = {PIRSA:26060011 see, \url{https://pirsa.org}}
}

Abstract

I will present the cosmological analysis from the simultaneous Bayesian estimates of gravitational-lensing potential bandpowers and unlensed cosmic microwave background (CMB) EE bandpowers directly using the polarization maps from the South Pole Telescope (SPT) observed in 2019/20. These observations produce the deepest high-angular-resolution CMB polarization maps at 90, 150, and 220 GHz to date, making the standard Quadratic Estimation (QE) method suboptimal for lensing reconstruction. In this analysis, we use the Marginal Unbiased Score Expansion (MUSE) method, which is an optimal map-level Bayesian inference method for CMB lensing potential bandpowers and unlensed CMB EE bandpowers, effectively using all N-point statistics of the CMB polarization maps. The constraints on the Hubble constant (H0) and the amplitude of structure growth (S8) from this work are comparable to those from Planck using full-sky temperature and polarization observations, enabling a powerful test of the LCDM model. With the lensing potential bandpowers reconstructed from the CMB polarization signal, we test the anomaly of excess lensing power from the LCDM prediction, and detect the impact of non-linear structure evolution on CMB lensing. We also explore the extensions of the LCDM models.

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