The Fix and Pair techniques were designed to generate simulations with reduced variance in the 2-point statistics by modifying the Initial Conditions (ICs). In this paper we show that this technique is also valid when the initial conditions have local non-Gaussianities (PNG), parametrised by 𝑓 NL , without biasing the 2-point statistics but reducing significantly their variance. We show how to quantitatively use these techniques to test the accuracy of galaxy/halo clustering models down to a much reduced uncertainty and we apply them to test the standard model for halo clustering in the presence of PNG. Additionally, we show that by Matching the stochastic part of the ICs for two different cosmologies (Gaussian and non-Gaussian) we obtain a large correlation between the (2-point) statistics that can explicitly be used to further reduce the uncertainty of the model testing. For our reference analysis (we obtain an uncertainty of 𝜎( 𝑓 NL ) = 60 with a standard simulation, whereas using Fixed [Fixed-Paired] initial conditions it reduces to 𝜎( 𝑓 NL ) = 12 [𝜎( 𝑓 NL ) = 12]. When also Matching the ICs we obtain 𝜎( 𝑓 NL ) = 18 for the standard case, and 𝜎( 𝑓 NL ) = 8 [𝜎( 𝑓 NL ) = 7] for Fixed [Fixed-Paired]. The combination of the Fix, Pair and Match techniques can be used in the context of PNG to create simulations with an effective volume incremented by a factor ∼ 70 at given computational resources.

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: ∼246 goliat-png N-body small area simulations with fNL equal to -100 and 100, and 1952 Gaussian ICE-COLA mocks with fNL = 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 fNL within the 1σ when including the integral constraint. In contrast, we found a bias of ΔfNL ∼ 100 when not including it. For a DES-like scenario, we forecast a bias of ΔfNL ∼ 23, equivalent to 1.8σ, when not using the IC for a fiducial value of fNL = 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 fNL within σ(fNL) = 31 when using the DES-Y3 BAO sample, with the ACF in the 1 deg < θ < 20 deg range.

The Fix and Pair techniques were designed to generate simulations with reduced variance in the 2-point statistics by modifying the Initial Conditions (ICs). In this paper we show that this technique is also valid when the initial conditions have local Primordial non-Gaussianities (PNG), parametrised by fNL, without biasing the 2-point statistics but reducing significantly their variance. We show how to quantitatively use these techniques to test the accuracy of galaxy/halo clustering models down to a much reduced uncertainty and we apply them to test the standard model for halo clustering in the presence of PNG. Additionally, we show that by Matching the stochastic part of the ICs for two different cosmologies (Gaussian and non-Gaussian) we obtain a large correlation between the (2-point) statistics that can explicitly be used to further reduce the uncertainty of the model testing. For our reference analysis (fNL = 100, V = 1[h−1Gpc]3, n = 2.5 × 10−4[h−1Mpc]−3, b = 2.32), we obtain an uncertainty of σ(fNL) = 60 with a standard simulation, whereas using Fixed [Fixed-Paired] initial conditions it reduces to σ(fNL) = 12 [σ(fNL) = 12]. When also Matching the ICs we obtain σ(fNL) = 18 for the standard case, and σ(fNL) = 8 [σ(fNL) = 7] for Fixed [Fixed-Paired]. The combination of the Fix, Pair and Match techniques can be used in the context of PNG to create simulations with an effective volume incremented by a factor ∼70 at given computational resources.