Turnover detection using the power spectrum and bispectrum

TL;DR

This paper explores the detection of the matter power spectrum turnover as a standard ruler for cosmology, using both power spectrum and bispectrum data, forecasting significant improvements with upcoming surveys.

Contribution

It introduces a method combining power spectrum and bispectrum analysis to forecast the detection of the turnover scale in future large-scale structure surveys.

Findings

Euclid-like survey detects turnover at ~6σ

MegaMapper-like survey detects turnover at ~15σ

Inclusion of bispectrum improves constraints by 10-17%

Abstract

The turnover at the peak of the Fourier matter power spectrum encodes a fundamental signature of matter-radiation equality in the early Universe. This delivers a potential standard ruler, independent of baryon acoustic oscillations and therefore able to break parameter degeneracies and improve precision. Furthermore, the turnover scale is independent of redshift and clustering bias, allowing for stacking of the signals from redshift bins. In practice, the very large scale of the turnover means that sample variance and systematics are serious impediments to its detection. Detections of the turnover and measurements of its scale have been made in the WiggleZ, eBOSS, Quaia, and DESI surveys. Upcoming surveys should improve the detection significance and reduce errors on the turnover scale. We use MCMC forecasts for turnover detection in a spectroscopic Euclid-like survey and a futuristic MegaMapper-like survey. In addition to the power spectrum, we include the signal from the bispectrum in equilateral configurations. These surveys are forecast to detect the turnover at $\sim\! 6\sigma$ (Euclid-like) and $\sim\! 15\sigma$ (MegaMapper-like), with precision on the turnover scale of $\sim\! 4\%$ and $\sim\! 2\%$. The inclusion of the bispectrum delivers a modest improvement of $\sim\! 10-17\%$ in the constraints on the turnover scale.