Skip to main content
SearchLogin or Signup

Understanding biases to CMB lensing and delensing on the road to precision science

Presentation #214.06D in the session “CMB”.

Published onJan 11, 2021
Understanding biases to CMB lensing and delensing on the road to precision science

For cosmologists, CMB lensing can be both a blessing and a nuisance. It’s a nuisance because it generates spurious B-mode polarisation which obscures the highly-sought-after signal from inflationary gravitational waves, but it’s a blessing because it can be used to reconstruct maps of the projected matter distribution of the Universe, from which the neutrino masses, dark energy, and any physics affecting the growth of structure can be constrained. In this talk, I will focus on systematics that need to be addressed in order to harness the full statistical power of upcoming probes and make progress in both of these exciting areas. In the first part of my talk, I will explain how the lensing contamination to CMB B-modes can be removed — what is known as delensing — in order to facilitate searches for inflationary gravitational waves, and present preparatory efforts to delens data from the upcoming Simons Observatory. In doing so, I will discuss biases affecting the procedure (and how to mitigate them) when lensing is reconstructed internally from the CMB itself, and in the alternative scenario where the CIB is used as a tracer of the matter. Looking to the future, I will highlight previously unknown limitations of the delensing methodologies implemented to date. In the second part, I will focus on biases to the power spectrum of CMB lensing reconstructions and their cross-correlations with tracers of the large-scale structure due to extragalactic emission such as the CIB or the tSZ effect from galaxy clusters. So far, efforts to mitigate these have relied on simulations and hardening techniques which incur noise penalties and are sub-optimal when the sources are clustered. I will conclude by briefly discussing ongoing work on an alternative approach where we model these biases analytically as a function of experimental characteristics, enabling improved physical insight and potentially opening the door to more optimal mitigation methods.


Comments
0
comment

No comments here