Power spectrum multipoles and clustering wedges during the Epoch of Reionization

TL;DR

This paper evaluates the effectiveness of power spectrum clustering wedges as summary statistics for 21cm EoR surveys, demonstrating they improve parameter inference precision over monopole analysis, especially with SKA-Low data.

Contribution

It introduces the use of clustering wedges for 21cm EoR analysis, showing they better handle anisotropic effects and sampling issues, leading to more accurate reionization parameter constraints.

Findings

Clustering wedges improve parameter constraints by a factor of ~3.

Power spectrum multipoles can disentangle ionization bubble effects.

SKA-Low can achieve percent-level reionization inference with 120 hours.

Abstract

We study the viability of using power spectrum clustering wedges as summary statistics of 21cm surveys during the Epoch of Reionization (EoR). For observations in a wide redshift range $z\sim 7-9$ corresponding to a line-of-sight scale of $\sim 500$Mpc, the power spectrum is subject to anisotropic effects due to the evolution along the light-of-sight. Information on the physics of reionization can be extracted from the anisotropy using the power spectrum multipoles. Signals of the power spectrum monopole are highly correlated at scales smaller than the typical ionization bubble, which can be disentangled by including higher-order multipoles. By simulating observations of the low frequency part of the Square Kilometre Array (SKA) Observatory, we find that the sampling of the cylindrical wavenumber $k$-space is highly non-uniform due to the baseline distribution, i.e. the distribution of antenna pairs sampling different transverse ${k}_\perp$ scales. Measurements in clustering wedges partition the cylindrical $k$-space into different radial $k_\parallel$ scales, and can be used for isolating parts of $k$-space with relatively uniform sampling, allowing for more precise parameter inference. Using Fisher Matrix forecasts, we find that the reionization model can be inferred with per-cent level precision with $\sim 120$hrs of integration time using SKA-Low. Compared to model inference using only the power spectrum monopole above the foreground wedge, model inference using multipole power spectra in clustering wedges yields a factor of $\sim 3$ improvement on the marginalised 1D parameter constraints.