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Unbiased Estimation of an Angular Power Spectrum

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arxiv astro-ph/0402428 v2 pith:V3WPVZJR submitted 2004-02-18 astro-ph

Unbiased Estimation of an Angular Power Spectrum

classification astro-ph
keywords noiseanalyticestimatesmapsprocedurepropertiesspectrumunbiased
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We discuss the derivation of the analytic properties of the cross-power spectrum estimator from multi-detector CMB anisotropy maps. The method is computationally convenient and it provides unbiased estimates under very broad assumptions. We also propose a new procedure for testing for the presence of residual bias due to inappropriate noise subtraction in pseudo-$C_{\ell}$ estimates. We derive the analytic behavior of this procedure under the null hypothesis, and use Monte Carlo simulations to investigate its efficiency properties, which appear very promising. For instance, for full sky maps with isotropic white noise, the test is able to identify an error of 1% on the noise amplitude estimate.

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Cited by 2 Pith papers

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  2. BROOM: a python package for model-independent analysis of microwave astronomical data

    astro-ph.CO 2026-04 unverdicted novelty 4.0

    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.