The Simons Observatory: Validation of reconstructed power spectra from simulated filtered maps for the Small Aperture Telescope survey

TL;DR

This paper introduces a transfer function-based method to accurately estimate CMB angular power spectra from filtered maps, validated with simulated data for the Simons Observatory's small aperture telescopes, enabling efficient analysis of large datasets.

Contribution

It presents a practical, faster transfer function-based approach for estimating power spectra from filtered CMB maps, suitable for large sky surveys like SO.

Findings

Method recovers unbiased cosmological parameters in simulations.

Validation shows effective mitigation of filtering effects on power spectra.

Approach is applicable to large data volumes and multiple frequency bands.

Abstract

We present a transfer function-based method to estimate angular power spectra from filtered maps for cosmic microwave background (CMB) surveys. This is especially relevant for experiments targeting the faint primordial gravitational wave signatures in CMB polarisation at large scales, such as the Simons Observatory (SO) small aperture telescopes. While timestreams can be filtered to mitigate the contamination from low-frequency noise, usual methods that calculate the mode coupling at individual multipoles can be challenging for experiments covering large sky areas or reaching few-arcminute resolution. The method we present here, although approximate, is more practical and faster for larger data volumes. We validate it through the use of simulated observations approximating the first year of SO data, going from half-wave plate-modulated timestreams to maps, and using simulations to estimate the mixing of polarisation modes induced by an example of time-domain filtering. We show its performance through an example null test and with an end-to-end pipeline that performs inference on cosmological parameters, including the tensor-to-scalar ratio $r$. The performance demonstration uses simulated observations at multiple frequency bands. We find that the method can recover unbiased parameters for our simulated noise levels.