Primordial non-Gaussianity with Angular correlation function: Integral constraint and validation for DES

TL;DR

This paper develops a methodology to measure primordial non-Gaussianity using the angular correlation function in DES data, emphasizing the importance of the integral constraint for unbiased results and validating the approach with simulations.

Contribution

It introduces a robust method for estimating local primordial non-Gaussianity from DES data, highlighting the critical role of the integral constraint and validating the approach with simulations.

Findings

Including the integral constraint reduces bias in $f_{NL}$ measurement.

Forecasted uncertainty of $f_{NL}$ measurement is approximately 31.

Validated the methodology's robustness across different analysis choices.

Abstract

Local primordial non-Gaussianity (PNG) is a promising observable of the underlying physics of inflation, characterised by $f_{\rm NL}^{\rm loc}$. We present the methodology to measure $f_{\rm NL}^{\rm loc}$ from the Dark Energy Survey (DES) data using the 2-point angular correlation function (ACF) with scale-dependent bias. One of the focuses of the work is the integral constraint. This condition appears when estimating the mean number density of galaxies from the data and is key in obtaining unbiased $f_{\rm NL}^{\rm loc}$ constraints. The methods are analysed for two types of simulations: $\sim 246$ GOLIAT-PNG N-body small area simulations with $f_{\rm NL}$ equal to -100 and 100, and 1952 Gaussian ICE-COLA mocks with $f_{\rm NL}=0$ that follow the DES angular and redshift distribution. We use the ensemble of GOLIAT-PNG mocks to show the importance of the integral constraint when measuring PNG, where we recover the fiducial values of $f_{\rm NL}$ within the $1\sigma$ when including the integral constraint. In contrast, we found a bias of $\Delta f_{\rm NL}\sim 100$ when not including it. For a DES-like scenario, we forecast a bias of $\Delta f_{\rm NL} \sim 23$, equivalent to $1.8\sigma$, when not using the IC for a fiducial value of $f_{\rm NL}=100$. We use the ICE-COLA mocks to validate our analysis in a realistic DES-like setup finding it robust to different analysis choices: best-fit estimator, the effect of IC, BAO damping, covariance, and scale choices. We forecast a measurement of $f_{\rm NL}$ within $\sigma(f_{\rm NL})=31$ when using the DES-Y3 BAO sample, with the ACF in the $1\ {\rm deg}<\theta<20\ {\rm deg}$ range.