Impact on the tensor-to-scalar ratio of incorrect Galactic foreground modelling

被引：23

作者：

Armitage-Caplan, Charmaine ^{[1
]}

Dunkley, Joanna ^{[1
]}

Eriksen, Hans Kristian ^{[2
]}

Dickinson, Clive ^{[3
]}

机构：

[1] Univ Oxford, Subdept Astrophys, Oxford OX1 3RH, England

[2] Univ Oslo, Inst Theoret Astrophys, N-0315 Oslo, Norway

[3] Univ Manchester, Sch Phys & Astron, Jodrell Bank, Ctr Astrophys, Manchester M13 9PL, Lancs, England

来源：

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY | 2012年 / 424卷 / 03期

关键词：

cosmic background radiation; PROBE WMAP OBSERVATIONS; BAYESIAN COMPONENT SEPARATION; BACKGROUND-RADIATION; MICROWAVE EMISSION; POLARIZATION; ANISOTROPY;

D O I：

10.1111/j.1365-2966.2012.21314.x

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

A key goal of many cosmic microwave background (CMB) experiments is the detection of gravitational waves, through their B-mode polarization signal at large scales. To extract such a signal requires modelling contamination from the Galaxy. Using the Planck experiment as an example, we investigate the impact of incorrectly modelling foregrounds on estimates of the polarized CMB, quantified by the bias in tensor-to-scalar ratio r, and optical depth t. We use a Bayesian parameter estimation method to estimate the CMB, synchrotron and thermal dust components from simulated observations spanning 30353?GHz, starting from a model that fits the simulated data, returning r < 0.03 at 95?per cent confidence for an r = 0 model and r = 0.09 +/- 0.03 for an r = 0.1 model. We then introduce a set of mismatches between the simulated data and assumed model. Including a curvature of the synchrotron spectral index with frequency, but assuming a power-law model, can bias r high by similar to 1 sigma (delta r similar to 0.03). A similar bias is seen for thermal dust with a modified blackbody frequency dependence, incorrectly modelled as a power law. If too much freedom is allowed in the model, for example, fitting for spectral indices in 3 degrees pixel over the sky with physically reasonable priors, we find that r can be biased up to similar to 3 sigma high by effectively setting the indices to the wrong values. Increasing the signal-to-noise ratio by reducing parameters, or adding additional foreground data, reduces the bias. We also find that neglecting a similar to 1?per cent polarized freefree or spinning dust component has a negligible effect on r. These tests highlight the importance of modelling the foregrounds in a way that allows for sufficient complexity while minimizing the number of free parameters.

引用

页码：1914 / 1924

页数：11

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