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Planck 2015 results. X. Diffuse component separation: Foreground maps

Authors:
  • Saint pauls hosiptal millennium medical college

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Planck has mapped the microwave sky in temperature over nine frequency bands between 30 and 857 GHz and in polarization over seven frequency bands between 30 and 353 GHz in polarization. In this paper we consider the problem of diffuse astrophysical component separation, and process these maps within a Bayesian framework to derive an internally consistent set of full-sky astrophysical component maps. Component separation dedicated to cosmic microwave background (CMB) reconstruction is described in a companion paper. For the temperature analysis, we combine the Planck observations with the 9-yr Wilkinson Microwave Anisotropy Probe (WMAP) sky maps and the Haslam et al. 408 MHz map, to derive a joint model of CMB, synchrotron, free-free, spinning dust, CO, line emission in the 94 and 100 GHz channels, and thermal dust emission. Full-sky maps are provided for each component, with an angular resolution varying between 7.5 and 1deg. Global parameters (monopoles, dipoles, relative calibration, and bandpass errors) are fitted jointly with the sky model, and best-fit values are tabulated. For polarization, the model includes CMB, synchrotron, and thermal dust emission. These models provide excellent fits to the observed data, with rms temperature residuals smaller than 4μK over 93% of the sky for all Planck frequencies up to 353 GHz, and fractional errors smaller than 1% in the remaining 7% of the sky. The main limitations of the temperature model at the lower frequencies are internal degeneracies among the spinning dust, free-free, and synchrotron components; additional observations from external low-frequency experiments will be essential to break these degeneracies. The main limitations of the temperature model at the higher frequencies are uncertainties in the 545 and 857 GHz calibration and zero-points. For polarization, the main outstanding issues are instrumental systematics in the 100-353 GHz bands on large angular scales in the form of temperature-to-polarization leakage, uncertainties in the analogue-to-digital conversion, and corrections for the very long time constant of the bolometer detectors, all of which are expected to improve in the near future.
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... For each simulation, we estimate β and construct the histograms given in figure 6. These are obtained by building the covariance through simulations, setting a harmonic binning ∆ℓ = 100 in the range [100,700], displayed in orange, or [50,750], displayed in blue. In the same figure 6, we show histograms of β obtained through spectra with and without noise + foregrounds debiasing. ...
... • Pipeline "D-estimator" Figure 6: Histograms of β with and without debiasing, as obtained for all simulation phases. For each phase, we considered two harmonic ranges: [100,700], shown in orange, and [50,750], shown in blue. Shaded histograms represent the case without debiasing, while empty histograms indicate the case with debiasing. ...
... An accurate estimation of β ≈ 0.3 • is still possible under a small mismodelling of foreground emission, like assuming d0s0 templates to describe an d1s1 sky. Models d0 and d1 are based on the same dust template derived from Planck 2015 data, but scaled with different SEDs: a fixed β d = 1.54 and T d = 20K for d0; and the spatially varying T d and β d obtained by Commander from the analysis of Planck 2015 data [100] for d1. Likewise, s0 and s1 share the synchrotron template derived from Haslam and WMAP data [101,102], but are scaled with different SEDs: a fixed β s = −3.0 for s0; and the spatially varying β s obtained by [103] from the analysis of Haslam and WMAP data for s1. ...
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... Common choices are spatial (real-space) domains, harmonic domains (harmonic ILC; HILC), and needlet domains (NILC) [6], the last of which combines some of the aspects of the first two. ILC has been applied to CMB datasets for many years; see [9] for an application to COBE data and [10,47] for WMAP data (all spatial ILC), as well as later applications of NILC on WMAP data [6], Planck data [48,49] (including to isolate the tSZ effect [13,16,50,51] and other signals like the spatially varying μ distortion [52]), and ACT data [19], and HILC on Planck data [12] and ACT data [14]. We will briefly discuss spatial and HILC below. ...
... We refer the reader to the PySM3 documentation for descriptions of these models, although we note that the dust SED is described by a modified blackbody with spatially varying temperature and spectral index and that the synchrotron SED is described by a power-law with spatially varying spectral index. The non-Gaussian templates at reference frequencies are based on the Commander dust map from the Planck data [48] and the synchrotron map based on the WMAP 27 GHz data [63]. ...
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... (B. T. Draine 2011), which is supposed to be valid for the frequency down to 10 MHz (R.Adam et al. 2016;B. Thorne et al. 2017): ...
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