Constraining Cosmological Parameters with Needlet Internal Linear Combination Maps I: Analytic Power Spectrum Formalism

The internal linear combination (ILC) method is a popular approach for constructing component-separated maps in cosmic microwave background (CMB) analyses. It optimally combines observed maps at different frequencies to produce an unbiased minimum-variance map of a component. When performed in harmonic space, it is straightforward to analytically compute the contributions of individual sky components to the power spectrum of the resulting ILC map. ILC can also be performed on a basis of needlets, spherical wavelets that have compact support in both pixel and harmonic space, capturing both scale-dependent and spatially varying information. However, an analytic understanding of the power spectra of needlet ILC (NILC) component-separated maps, as needed to enable their use in cosmological parameter inference, has remained an outstanding problem. In this paper, we derive the first analytic expression for the power spectra of NILC maps, as well as an expression for the cross-spectrum of a NILC map with an arbitrary second map, in terms of contributions from individual sky components. We validate our result using simulations, finding that it is exact. These results contain useful insights: we explicitly see how NILC power spectra contain information from contaminant fields beyond the two-point level, and we obtain a formalism with which to parameterize NILC power spectra. However, because this parameter dependence is complicated by correlations and higher-point functions of the component maps and weight maps, we find that it is intractable to perform parameter inference using these analytic expressions. Instead, numerical techniques are needed to estimate parameters using NILC maps -- we explore the use of likelihood-free inference with neural posterior estimation in a companion paper. Our code to produce the results in this paper is available in https://github.com/kmsurrao/NILC-PS-Model.
Comment: 19 pages, 3 figures; code available at https://github.com/kmsurrao/NILC-PS-Model