A Monte Carlo comparison between template-based and Wiener-filter CMB dipole estimators
H. Thommesen, K. J. Andersen, R. Aurlien, R. Banerji, M. Brilenkov, H., K. Eriksen, U. Fuskeland, M. Galloway, L. M. Mocanu, T. L. Svalheim, I. K., Wehus

TL;DR
This paper compares two CMB dipole estimators, showing that the Wiener filter approach significantly reduces uncertainty by accounting for correlations with higher-order fluctuations, improving dipole measurement accuracy.
Contribution
It provides a Monte Carlo comparison of template-based and Wiener-filter CMB dipole estimators, highlighting the advantages of the Wiener filter in incomplete sky data.
Findings
Wiener filter reduces dipole amplitude uncertainty by a factor of six.
Wiener filter approach yields about 0.5 μK uncertainty, compared to 3 μK for template method.
Methodology underpins recent Planck CMB dipole measurements.
Abstract
We review and compare two different CMB dipole estimators discussed in the literature, and assess their performances through Monte Carlo simulations. The first method amounts to simple template regression with partial sky data, while the second method is an optimal Wiener filter (or Gibbs sampling) implementation. The main difference between the two methods is that the latter approach takes into account correlations with higher-order CMB temperature fluctuations that arise from non-orthogonal spherical harmonics on an incomplete sky, which for recent CMB data sets (such as Planck) is the dominant source of uncertainty. For an accepted sky fraction of 81% and an angular CMB power spectrum corresponding to the best-fit Planck 2018 CDM model, we find that the uncertainty on the recovered dipole amplitude is about six times smaller for the Wiener filter approach than for the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
