A divide-and-conquer framework using QAOA and neural network surrogates accelerates constrained MCMC by factors of 7.6 to 20.3 over classical methods.
A needlet ILC analysis of WMAP 7-year data: estimation of CMB temperature map and power spectrum
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
The WMAP 7-year temperature maps have been re-analized to extract a CMB map and CMB power spectrum with reduced contamination by astrophysical foregrounds and noise. The method used is based on linear combinations of WMAP data and foreground templates, implemented on a decomposition of the observations onto a needlet frame. We obtain a clean CMB map with low contamination from foregrounds and noise, as well as a new estimate of the CMB temperature power spectrum. The latter is essentially compatible with the power spectrum published by the WMAP collaboration, although it is slightly but systematically lower on large scales. The exact origin of this discrepancy is not clear.
citation-role summary
background 2
citation-polarity summary
fields
astro-ph.CO 3years
2026 3roles
background 2polarities
background 2representative citing papers
Two NILC extensions—one deprojecting foreground moments and one marginalizing residuals at the likelihood level—yield unbiased r estimates and consistent lensing B-mode reconstruction in SO-SAT-like simulations.
BROOM is a Python package that applies ILC and GILC techniques for model-independent separation of CMB, SZ, and foreground signals in microwave data along with diagnostic and simulation utilities.
citing papers explorer
-
Forecasts of CMB $E$-mode anomalies for AliCPT-1
A divide-and-conquer framework using QAOA and neural network surrogates accelerates constrained MCMC by factors of 7.6 to 20.3 over classical methods.
-
Blind mitigation of foreground-induced biases on primordial $B$ modes for ground-based CMB experiments
Two NILC extensions—one deprojecting foreground moments and one marginalizing residuals at the likelihood level—yield unbiased r estimates and consistent lensing B-mode reconstruction in SO-SAT-like simulations.
-
BROOM: a python package for model-independent analysis of microwave astronomical data
BROOM is a Python package that applies ILC and GILC techniques for model-independent separation of CMB, SZ, and foreground signals in microwave data along with diagnostic and simulation utilities.