Constraints on primordial non-Gaussianity from the cross-correlation of DESI Luminous Red Galaxies and $Planck$ CMB lensing

TL;DR

This paper uses cross-correlation of DESI LRGs and Planck CMB lensing to constrain primordial non-Gaussianity, demonstrating the method's robustness and potential for future cosmological studies.

Contribution

It introduces a nonlinear neural network-based systematics correction and provides new constraints on $f_{NL}$ from cross-correlation data.

Findings

Measured $f_{NL} = 39_{-38}^{+40}$ from cross-correlation alone.

Combined analysis yields $f_{NL} = 24_{-21}^{+20}$, consistent with Gaussianity.

Highlights the stability of CMB lensing cross-correlation for primordial non-Gaussianity constraints.

Abstract

We use the angular cross-correlation between a luminous red galaxy (LRG) sample from the Dark Energy Spectroscopic Instrument (DESI) Legacy Survey data release DR9 and the $Planck$ cosmic microwave background (CMB) lensing maps to constrain the local primordial non-Gaussianity parameter, $f_{\rm NL}$, using the scale-dependent galaxy bias effect. The galaxy sample covers approximately 40\% of the sky, contains galaxies up to redshift $z \sim 1.4$, and is calibrated with the LRG spectra that have been observed for DESI Year 1 (Y1). We apply a nonlinear imaging systematics treatment based on neural networks to remove observational effects that could potentially bias the $f_{\rm NL}$ measurement. Our measurement is performed without blinding, but the full analysis pipeline is tested with simulations including systematics. Using the two-point angular cross-correlation between LRG and CMB lensing only ($C_\ell^{\kappa G}$) we find $f_{\rm NL} = 39_{-38}^{+40}$ at 68% confidence level, and our result is robust in terms of systematics and cosmology assumptions. If we combine this information with the autocorrelation of LRG ($C_\ell^{GG}$) applying a $\ell_{\rm min}$ scale cut to limit the impact of systematics, we find $f_{\rm NL} = 24_{-21}^{+20}$ at 68% confidence level. Our results motivate the use of CMB lensing cross-correlations for measuring $f_{\rm NL}$ with future datasets given its stability in terms of observational systematics compared to the angular auto-correlation. Furthermore, performing accurate systematics mitigation is crucially important in order to achieve competitive constraints on $f_{\rm NL}$ from CMB lensing cross-correlation in combination with the tracers' autocorrelation.