Quantifying Bias due to non-Gaussian Foregrounds in an Optimal Reconstruction of CMB Lensing and Temperature Power Spectra

TL;DR

This paper assesses how non-Gaussian extragalactic foregrounds bias the optimal reconstruction of CMB lensing and temperature spectra using a Bayesian method, finding biases that are generally below statistical significance.

Contribution

It introduces an analysis of foreground bias on CMB reconstructions using the MUSE Bayesian method applied to simulated data, highlighting the potential magnitude of such biases.

Findings

Bias in lensing potential amplitude is about 0.7 sigma at high multipoles.

No significant bias detected at lower multipoles, around -0.4 sigma.

Foreground bias could impact future CMB analyses if not properly accounted for.

Abstract

We estimate the magnitude of the bias due to non-Gaussian extragalactic foregrounds on the optimal reconstruction of the cosmic microwave background (CMB) lensing potential and temperature power spectra. The reconstruction is performed using a Bayesian inference method known as the marginal unbiased score expansion (MUSE). We apply MUSE to a minimum variance combination of multifrequency maps drawn from the Agora publicly available simulations of the lensed CMB and correlated extragalactic foreground emission. Taking noise levels appropriate to the SPT-3G D1 release, we find non-Gaussian foregrounds may bias the MUSE reconstruction of the lensing potential amplitude at the level of $(0.7\pm 0.3)\,\sigma$ when using modes up to $\ell_{max}=3500$. We do not detect a statistically significant bias, finding a value of $(-0.4\pm 0.3)\,\sigma$, when restricted to lower angular multipoles, $\ell_{max}=3000$. This work is a first step toward understanding the impact of extragalactic foregrounds on optimal reconstructions of CMB temperature and lensing potential power spectra.