A Fast Method for Power Spectrum and Foreground Analysis for 21 cm Cosmology

TL;DR

This paper introduces a fast, scalable method for analyzing the 21 cm power spectrum and foregrounds in cosmology, enabling efficient processing of large datasets from radio interferometers.

Contribution

It develops an accelerated quadratic estimator formalism reducing computational complexity from O(N^3) to O(N log N), facilitating megavoxel-scale analysis in 21 cm cosmology.

Findings

Achieved a computational speed-up enabling analysis of large datasets.

Forecasted that 1000 hours of MWA observations can detect the Epoch of Reionization signal.

Extended foreground models to include bright point sources efficiently.

Abstract

We develop and demonstrate an acceleration of the Liu & Tegmark quadratic estimator formalism for inverse variance foreground subtraction and power spectrum estimation in 21 cm tomography from O(N^3) to O(N log N), where N is the number of voxels of data. This technique makes feasible the megavoxel scale analysis necessary for current and upcoming radio interferometers by making only moderately restrictive assumptions about foreground models and survey geometry. We exploit iterative and Monte Carlo techniques and the symmetries of the foreground covariance matrices to quickly estimate the 21 cm brightness temperature power spectrum, P(k_parallel, k_perpendicular), the Fisher information matrix, the error bars, the window functions, and the bias. We also extend the Liu & Tegmark foreground model to include bright point sources with known positions in a way that scales as O[(N log N)(N point sources)] < O(N^5/3). As a first application of our method, we forecast error bars and window functions for the upcoming 128-tile deployment of the Murchinson Widefield Array, showing that 1000 hours of observation should prove sufficiently sensitive to detect the power spectrum signal from the Epoch of Reionization.