Bayesian field-level inference of primordial non-Gaussianity using next-generation galaxy surveys

Abstract

Detecting and measuring a non-Gaussian signature of primordial origin in the density field is a major science goal of nextgeneration galaxy surveys. The signal will permit us to determine primordial physics processes and constrain models of cosmic inflation. While traditional approaches utilise a limited set of statistical summaries of the galaxy distribution to constrain primordial non-Gaussianity, we present a field-level approach by Bayesian forward-modelling the entire three-dimensional galaxy survey. Our method naturally and fully self-consistently exploits the entirety of the large-scale structure, e.g., higherorder statistics, peculiar velocity fields, and scale-dependent galaxy bias, to extract information on the local non-Gaussianity parameter, 𝑓 nl . We demonstrate the performance of our approach through various tests with mock galaxy data emulating relevant features of the SDSS-III/BOSS-like survey, and additional tests with a Stage IV mock data set. These tests reveal that the method infers unbiased values of 𝑓 nl by accurately handling survey geometries, noise, and unknown galaxy biases. We demonstrate that our method can achieve constraints of 𝜎 𝑓 nl ≈ 8.78 for SDSS-III/BOSS-like data, an improvement of a factor ∼ 2.5 over currently published constraints. Tests with next-generation mock data show that significant further improvements are feasible with sufficiently high resolution. Furthermore, the results demonstrate that our method can consistently marginalise all nuisance parameters of the data model. The method further provides an inference of the three-dimensional primordial density field, providing opportunities to explore additional signatures of primordial physics.

Quijote-PNG: Simulations of Primordial Non-Gaussianity and the Information Content of the Matter Field Power Spectrum and Bispectrum

“…A comparison between the information captured by our bispectrum measurements and the information available in the primordial field, which bounds how much we could possibly know about PNG, shows that the matter power spectrum and bispectrum estimators only capture ∼10% of the total available information. This highlights the need to explore alternative approaches, such as topological measures (Biagetti et al 2021), the matter pdf (Friedrich et al 2020), machine-learning approaches (Giusarma et al 2019;Villaescusa-Navarro et al 2020), or field level approaches (Andrews et al 2022), to fully capture the information available in the matter field. The simulations presented here are designed to facilitate future investigations along these lines.…”

“…By making this choice, our PNG simulations can be used within a consistent framework to study how uncertainties in the standard cosmological parameters impact inferences of PNG. For example, while previous work has shown that the halo mass function (Lucchin & Matarrese 1988;LoVerde & Smith 2011), the matter probability density function (Valageas 2002;Uhlemann et al 2018;Friedrich et al 2020), topological measures (Biagetti et al 2021, and field level analyses (Andrews et al 2022) are potentially powerful probes of PNG, simulations are required to validate theoretical predictions or model these novel probes. An aim of this work is to help facilitate such analyses.…”

Quijote-PNG: Quasi-maximum Likelihood Estimation of Primordial Non-Gaussianity in the Nonlinear Dark Matter Density Field

“…This approach has even recently led to the first constraints on PNG using both the galaxy power spectrum and bispectrum, by analyzing data from the BOSS survey (Dawson et al 2013;Cabass et al 2022aCabass et al , 2022bD'Amico et al 2022). 13 Recent works based on field-level inference (Baumann & Green 2021;Andrews et al 2022), neural networks (Giri et al 2022), or reconstruction methods (Shirasaki et al 2021), have however shown that much more information should be present in the data. To extract this extra information, alternative promising observables, such as the density probability density function (Mao et al 2014;Uhlemann et al 2018;Friedrich et al 2020), persistent homology (Biagetti et al 2021(Biagetti et al , 2022a, or higher-order correlation functions in Fourier space (Gualdi et al 2021a(Gualdi et al , 2021b, have been considered.…”

Can we actually constrain $f_{\rm NL}$ using the scale-dependent bias effect? An illustration of the impact of galaxy bias uncertainties using the BOSS DR12 galaxy power spectrum

“…In the future, it would be interesting to check the extent to which the situation changes in analyses with the 1-loop galaxy bispectrum [32], or by probing higher-order statistics with the aid of field-level galaxy forward models [80,81] (though in Ref. [81] the authors still assume perfect knowledge of the b φ (b 1 ) relation).…”

“…In the future, it would be interesting to check the extent to which the situation changes in analyses with the 1-loop galaxy bispectrum [32], or by probing higher-order statistics with the aid of field-level galaxy forward models [80,81] (though in Ref. [81] the authors still assume perfect knowledge of the b φ (b 1 ) relation). We note however that if competitive constraints on f nl end up being possible with these higher-order analyses, these will come from our ability to probe the primordial signal, and not through the scale-dependent bias effect.…”