Polarized cosmic microwave background map recovery with sparse component separation

Abstract

The polarization modes of the cosmological microwave background are an invaluable source of information for cosmology and a unique window to probe the energy scale of inflation. Extracting this information from microwave surveys requires distinguishing between foreground emissions and the cosmological signal, which means solving a component separation problem. Component separation techniques have been widely studied for the recovery of cosmic microwave background (CMB) temperature anisotropies, but very rarely for the polarization modes. In this case, most component separation techniques make use of second-order statistics to distinguish between the various components. More recent methods, which instead emphasize the sparsity of the components in the wavelet domain, have been shown to provide low-foreground, full-sky estimates of the CMB temperature anisotropies. Building on sparsity, we here introduce a new component separation technique dubbed the polarized generalized morphological component analysis (PolGMCA), which refines previous work to specifically work on the estimation of the polarized CMB maps: i) it benefits from a recently introduced sparsity-based mechanism to cope with partially correlated components; ii) it builds upon estimator aggregation techniques to further yield a better noise contamination/non-Gaussian foreground residual trade-off. The PolGMCA algorithm is evaluated on simulations of full-sky polarized microwave sky simulations using the Planck Sky Model (PSM). The simulations show that the proposed method achieves a precise recovery of the CMB map in polarization with low-noise and foreground contamination residuals. It provides improvements over standard methods, especially on the Galactic center, where estimating the CMB is challenging.

Kinematic Sunyaev-Zel’dovich effect with projected fields. II. Prospects, challenges, and comparison with simulations

“…We use a cleaned CMB temperature map constructed from a joint analysis of the nine-year WMAP [31] and Planck full mission [2] full-sky temperature maps [30]. 4 The CMB is separated from other components in the microwave sky using "local-generalized morphological component analysis" (LGMCA), a technique relying on the sparse distribution of non-CMB foregrounds in the wavelet domain.…”

“…4 The CMB is separated from other components in the microwave sky using "local-generalized morphological component analysis" (LGMCA), a technique relying on the sparse distribution of non-CMB foregrounds in the wavelet domain. We refer the reader to [30,52] for a thorough description of this component separation technique and characterization of the resulting maps. The method reconstructs a full-sky CMB map with minimal dust contamination and essentially zero contamination from the thermal SZ (tSZ) effect, which is explicitly projected out in the map construction (unlike in, e.g., the official Planck SEVEM, NILC, or SMICA component-separated CMB maps, which all possess significant tSZ residuals).…”

“…We used a galaxy catalog constructed from WISE data [26] and CMB temperature maps cleaned via "local-generalized morphological component analysis" (LGMCA) [30] constructed from the Planck full mission [2] and Wilkinson Microwave Anisotropy Probe (WMAP ) nine-year survey (WMAP9) data [31]. We detected the kSZ 2 signal with signal-tonoise (S/N ) ≈ 3.8 − 4.5, depending on the use of external CMB lensing information, and thus obtained a 13% measurement of the baryon abundance at z ≈ 0.4.…”

Optimizing NILC Extractions of the Thermal Sunyaev–Zel’Dovich Effect with Deep Learning

“…While GMCA could outperform a few other extraction techniques, it yielded similar results to NILC. Over the years, refinements have been made to improve its performance (Bobin et al 2015;Wagner-Carena et al 2020). One could also do parametric modeling of the components, which has largely been done with the Commander algorithm (Eriksen et al 2008;Galloway et al 2023).…”

Learning sparse representations on the sphere

“…Wavelets on the sphere (Starck et al 2015) are now standard tools in astronomy and have been widely used for purposes such as FERMI-LAT data analysis (Schmitt et al 2010;McDermott et al 2016), the recovery of CMB and polarized CMB maps (Bobin et al 2015(Bobin et al , 2016, string detection (McEwen et al 2017), point source removal in CMB data (Sureau et al 2014), the detection of CMB anomalies (Naidoo et al 2017;Rassat et al 2014), or stellar turbulent convection studies (Bessolaz & Brun 2011). While wavelets are well suited for representing isotropic components in an image, they are far from optimal for analyzing anisotropic features such as filamentary structures.…”