Spherical Bispectrum: A Novel Visualization Scheme For Facilitating Comparisons

TL;DR

This paper introduces a spherical-bispectrum visualization scheme that simplifies the comparison of bispectra from simulations and perturbation theory, enabling a quantitative assessment of PT accuracy across different scales and redshifts.

Contribution

The paper presents a novel spherical-bispectrum visualization method that reduces configuration dependence, facilitating easier comparison and analysis of bispectra from simulations and theoretical models.

Findings

The new scheme effectively compares bispectra from different sources.

PT predictions match N-body results below a certain scale $k_{NL}$.

Binning effects significantly impact Fourier space comparisons.

Abstract

Recent developments of Perturbation Theory (PT), specifically the Effective Field Theory of Large Scale Structure (EFTofLSS) and its equivalents, have proven powerful in analyzing galaxy clustering statistics such as the galaxy power spectrum and bispectrum. To further this pursuit, we have devised a novel spherical-bispectrum visualization scheme that collapses configuration dependencies to highlight the scale dependence of the bispectrum. The resulting one-dimensional curves facilitate the comparison between different bispectra, for example, from simulation and PT calculation. Using the new scheme, we present a quantitative analysis of the accuracy of PT modeling by comparing PT's analytical prediction to the result from a suite of Quijote simulations. Specifically, we determine $k_{\rm NL}$, the wavenunmber below which the analytical prediction matches well with the N-body result by inspecting both leading order (LO) and next-to-leading order (NLO) power spectrum and bispectrum at redshifts $z=0$, $0.5$, $1$, $2$, $3$. We also quantify the binning effect in Fourier space and show that an appropriate correction must be applied to the analytic predictions in order to compare them with the discrete Fourier transform results obtained from N-body-simulation or real data.