Auto from cross: CMB lensing power spectrum without noise bias
arxiv(2024)
摘要
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 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 paper, we propose 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 our 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)). We show that our
estimator is as robust as standard methods in the presence realistic
inhomogeneous noise (e.g. from scan strategy) and masking. Moreover, our method
can be combined with split-based methods, making it completely insensitive to
mode coupling from inhomogeneous atmospheric and detector noise. We derive the
corresponding expressions for our estimator when estimating lensing from CMB
temperature and polarization. Although in this paper we specifically consider
CMB weak lensing power spectrum estimation, we illuminate the relation between
our new estimator, RDN^(0) subtraction, and general optimal
trispectrum estimation. Through this discussion we conclude that our estimator
is applicable to analogous problems in other fields which rely on estimating
connected trispectra/four-point functions like large-scale structure.
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