Cosmological constraints from the angular power spectrum and bispectrum of luminous red galaxies and CMB lensing

TL;DR

This paper combines galaxy clustering and CMB lensing data to improve cosmological parameter constraints, validating a perturbative bias model up to certain scales, and finds mild tension with Planck results.

Contribution

It introduces a perturbative bias model for galaxy clustering that is validated against simulations and applied to combined data for enhanced cosmological constraints.

Findings

Constraints on $\sigma_8$, $\Omega_m$, and $S_8$ consistent with previous analyses.

Including the bispectrum improves parameter constraints by 10-20%.

Results show mild tension with Planck, slightly shifting parameters.

Abstract

We study the projected clustering of photometric luminous red galaxies from the DESI Legacy Survey, combining their angular power spectrum, bispectrum, and cross-correlation with maps of the CMB lensing convergence from the Planck satellite. We employ a perturbative bias expansion in Eulerian space to describe the clustering of galaxies, modelling the power spectrum and bispectrum at one-loop and tree level, respectively. This allows us to use the power spectrum to self-consistently calibrate the perturbative bias parameters. We validate this model against an $N$-body simulation, and show that it can be used up to scales of at least $k_{\rm max}^P\simeq 0.2\,h{\rm Mpc}^{-1}$ and $k_{\rm max}^B\simeq 0.08\,h{\rm Mpc}^{-1}$, saturating the information recovered from the data. We obtain constraints on the amplitude of matter fluctuations $\sigma_8=0.761\pm 0.020$ and the non-relativistic matter fraction $\Omega_m=0.307\pm 0.015$, as well as the combination $S_8\equiv\sigma_8\sqrt{\Omega_m/0.3}=0.769 \pm 0.020$. Including the galaxy bispectrum leads to a $10$-$20\%$ improvement on the cosmological constraints, which are also in good agreement with previous analyses of the same data, and in mild tension with Planck at the $\sim2.5\sigma$ level. This tension is largely present in the standard two-point function dataset, and the addition of the bispectrum increases it slightly, marginally shifting $\sigma_8$ downwards and $\Omega_m$ upwards. Finally, using the bispectrum allows for a substantially more precise measurement of the bias parameters of this sample, which are in reasonable agreement with existing coevolution relations.