Optimal bispectrum estimator and simulations of the the CMB Lensing-ISW non-Gaussian signal
Anna Mangilli, Benjamin Wandelt, Franz Elsner, Michele Liguori

TL;DR
This paper develops optimal tools and simulations for extracting the Lensing-ISW bispectrum signal from future CMB data, improving estimation accuracy and addressing biases in primordial non-Gaussianity measurements.
Contribution
It introduces two methods for simulating L-ISW non-Gaussian maps and implements an optimal estimator analysis, enhancing the accuracy of L-ISW signal extraction from CMB data.
Findings
Estimator approaches the Cramer-Rao bound.
Wiener filtering improves $f_{NL}^{L-ISW}$ estimate by up to 10%.
Proper accounting of L-ISW prevents underestimation of primordial non-Gaussianity errors.
Abstract
In this paper we present the tools to optimally extract the Lensing-Integrated Sachs Wolfe (L-ISW) bispectrum signal from future CMB data. We implement two different methods to simulate the non-Gaussian CMB maps with the L-ISW signal: a non-perturbative method based on the FLINTS lensing code and the separable mode expansion method. We implement the Komatsu, Spergel and Wandelt (KSW) optimal estimator analysis for the Lensing-ISW bispectrum and we test it on the non-Gaussian simulations in the case of a realistic CMB experimental settings with an inhomogeneous sky coverage. We show that the estimator approaches the Cramer-Rao bound and that Wiener filtering the L-ISW simulations gives a slight improvement on the estimate of of . For a realistic CMB experimental setting accounting for anisotropic noise and masked sky, we show that the linear term of the…
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