Campus, Varian 355

Zoom info: https://stanford.zoom.us/my/sihanyuan?pwd=QnpsUHZWWGJ2ekVYWmZVL3BmM0gzZ…

Upcoming surveys will measure the cosmic microwave background (CMB) weak lensing power spectrum in exquisite detail, allowing for strong constraints on the sum of neutrino masses among other cosmological parameters. Standard CMB lensing power spectrum estimators aim to extract the connected non-Gaussian trispectrum of CMB temperature maps. However, they are generically dominated by a large disconnected, or Gaussian, noise bias, which thus needs to be subtracted at high accuracy. This is currently done with realistic map simulations of the CMB and noise, whose finite accuracy currently limits our ability to recover the CMB lensing on small-scale. In this talk, I will describe a novel estimator which instead avoids this large Gaussian bias. This estimator relies only on the data and avoids the need for bias subtraction with simulations. Thus, this bias avoidance method is (1) insensitive to misestimates in simulated CMB and noise models and (2) avoids the large computational cost of standard simulation-based methods like "realization-dependent N(0)'' (RDN(0)). I will show that this estimator is as robust as standard methods in the presence of realistic inhomogeneous noise (e.g. from scan strategy) and masking. Moreover, this method can be combined with split-based methods, making it completely insensitive to mode coupling from inhomogeneous atmospheric and detector noise. Although in this talk I specifically consider CMB weak lensing power spectrum estimation, I will illuminate the relation between this new estimator, RDN(0) subtraction, and general optimal trispectrum estimation. Through this discussion I will show that our estimator can be applicable to analogous problems in other fields which rely on estimating connected trispectra/four-point functions like large-scale structure.