Testing the analytical blind separation method in simulated CMB polarization maps

Abstract

Context. Multi-frequency observations are needed to separate the cosmic microwave background (CMB) from foreground emission and accurately extract cosmological information from the data. The analytical blind separation (ABS) method is dedicated to extracting the CMB power spectrum from multi-frequency observations in the presence of contamination from astrophysical foreground emission and instrumental noise. Aims. In this study, we apply the ABS method to simulated sky maps as could be observed with a future space-borne survey in order to test its capability of determining the CMB polarization E- and B-mode power spectra. Methods. We present the ABS method performance on simulations for both a full-sky analysis and for an analysis concentrating on sky regions less impacted by Galactic foreground emission. Results. We discuss the origin and minimization of biases in the estimated CMB polarization angular power spectra. We find that the ABS method performs quite well for the analysis of full-sky observations at intermediate and small angular scales, in spite of strong foreground contamination. On the largest scales, extra work is still required to reduce biases of various origins and the impact of confusion between CMB E and B polarization for partial sky analyses.

Performance forecasts for the primordial gravitational wave detection pipelines for AliCPT-1

“…The ABS estimator acts directly on measured multi-frequency cross band powers, which extracts CMB power spectra by projecting the data onto CMB subspace. In the presence of instrumental noise and a limited number of frequency channels, the ILC and ABS would yield slightly different results [44,69,70].…”

“…The analytic blind separation (ABS) method is a novel, computationally efficient method for the blind separation of the CMB from foregrounds [67], which has been tested extensively for extracting the CMB temperature and polarization E-and B-mode power spectra from the simulated maps [44,68]. The ABS estimator acts directly on measured multi-frequency cross band powers, which extracts CMB power spectra by projecting the data onto CMB subspace.…”

“…Presently, data analysis pipelines for PGW constraints mostly adopt a likelihood framework assuming a parametric model for foreground and the CMB [28,43]. However, recently proposed component separation methods [44,45] provide alternative approaches to tackle this problem. For AliCPT-1, we have five different pipelines: 1. the analytic blind separation (ABS), 2. the generalized least squares (GLS), 3. the constrained internal linear combination (cILC), 4. the multi-component multi-frequency likelihood (McMfL), and 5. the template fitting (TF) pipelines.…”

Performance forecasts for the primordial gravitational wave detection pipelines for AliCPT-1

“…The analytic blind separation (ABS) method is a novel, computationally efficient method for the blind separation of the CMB from foregrounds [72], which has been tested extensively for extracting the CMB temperature and polarization E-and B-mode power spectra from the simulated maps [47,73]. The ABS estimator acts directly on measured multi-frequency cross band powers, which extracts CMB power spectra by projecting the data onto CMB subspace.…”

“…Presently, data analysis pipelines for PGW constraints mostly adopt a likelihood framework assuming a parametric model for foreground and the CMB [30,46]. However, recently proposed component separation methods [47,48] provide alternative approaches to tackle this problem. For AliCPT-1, we have five different pipelines: 1. the analytic blind separation (ABS), 2. the generalized least squares (GLS), 3. the constrained internal linear combination (cILC), 4. the multi-component multi-frequency likelihood (McMfL), and 5. the template fitting (TF) pipelines.…”