Extracting high-order cosmological information in galaxy surveys with power spectra
Abstract: The "reconstruction" method was proposed more than a decade ago to boost the signal of baryonic acoustic oscillations measured in galaxy redshift surveys, which is one of key probes for dark energy. After moving observed galaxies in galaxy surveys back to their initial position, the reconstructed density field is closer to a linear Gaussian field, with higher-order information moved back into the power spectrum. By combining power spectra measured from the pre- and post-reconstructed galaxy samples, higher-ord… Show more
Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Cited by 5 publications
References 18 publications
A forward modeling approach to analyzing galaxy clustering with S
im
BIG
We present cosmological constraints from a simulation-based inference (SBI) analysis of galaxy clustering from the S im BIG forward modeling framework. S im BIG leverages the predictive power of high-fidelity simulations and provides an inference framework that can extract cosmological information on small nonlinear scales. In this work, we apply S im BIG to the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample and analyze the power spectrum, P ℓ ( k ) , to k max = 0.5 h / Mpc . We construct 20,000 simulated galaxy samples using our forward model, which is based on 2,000 high-resolution Q uijote N -body simulations and includes detailed survey realism for a more complete treatment of observational systematics. We then conduct SBI by training normalizing flows using the simulated samples and infer the posterior distribution of Λ CDM cosmological parameters: Ω m , Ω b , h , n s , σ 8 . We derive significant constraints on Ω m and σ 8 , which are consistent with previous works. Our constraint on σ 8 is 27% more precise than standard P ℓ analyses because we exploit additional cosmological information on nonlinear scales beyond the limit of current analytic models, k > 0.25 h / Mpc . This improvement is equivalent to the statistical gain expected from a standard P ℓ analysis of galaxy sample ∼ 60% larger than CMASS. While we focus on P ℓ in this work for validation and comparison to the literature, S im BIG provides a framework for analyzing galaxy clustering using any summary statistic. We expect further improvements on cosmological constraints from subsequent S im BIG analyses of summary statistics beyond P ℓ .
A forward modeling approach to analyzing galaxy clustering with S
im
BIG
We present cosmological constraints from a simulation-based inference (SBI) analysis of galaxy clustering from the S im BIG forward modeling framework. S im BIG leverages the predictive power of high-fidelity simulations and provides an inference framework that can extract cosmological information on small nonlinear scales. In this work, we apply S im BIG to the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample and analyze the power spectrum, P ℓ ( k ) , to k max = 0.5 h / Mpc . We construct 20,000 simulated galaxy samples using our forward model, which is based on 2,000 high-resolution Q uijote N -body simulations and includes detailed survey realism for a more complete treatment of observational systematics. We then conduct SBI by training normalizing flows using the simulated samples and infer the posterior distribution of Λ CDM cosmological parameters: Ω m , Ω b , h , n s , σ 8 . We derive significant constraints on Ω m and σ 8 , which are consistent with previous works. Our constraint on σ 8 is 27% more precise than standard P ℓ analyses because we exploit additional cosmological information on nonlinear scales beyond the limit of current analytic models, k > 0.25 h / Mpc . This improvement is equivalent to the statistical gain expected from a standard P ℓ analysis of galaxy sample ∼ 60% larger than CMASS. While we focus on P ℓ in this work for validation and comparison to the literature, S im BIG provides a framework for analyzing galaxy clustering using any summary statistic. We expect further improvements on cosmological constraints from subsequent S im BIG analyses of summary statistics beyond P ℓ .
We review the different approaches for combining the cosmological information from the full shape of the pre-reconstructed power spectrum — usually referred as redshift-space distortion (RSD) analysis — and from the baryon acoustic oscillation (BAO) peak position in the post-reconstructed power spectrum with the aim of finding the optimal procedure. We focus on combining the pre- and post-reconstructed derived quantities at different compression levels: 1) the two-point summary statistics, the power spectrum multipoles, P(ℓ)(k); 2) the compressed BAO variables,α∥,⊥; and 3) an hybrid approach between 1) and 2). We apply these methods to the publicly available eBOSS Luminous Red Galaxy catalogues, for both data and synthetic EZ-mocks. We find that the three approaches result in very consistent posteriors when the appropriate covariance matrix estimator is used. On average, the combination at P(ℓ)(k) level retrieves 5-10% tighter constraints than the other two approaches, demonstrating that the standard approach of combining at the level of the BAO variables is nearly optimal. We conclude that combining both BAO post-reconstructed and full shape pre-reconstructed signals for the one single data realization at the level of the summary statistics is faster, as it does not require running the whole pipeline on the individual mocks, and brings a moderate 10% improvement, with respect to the other two studied methods. Moreover, we check for potential systematics, such as, the way the matrix is built and the effect of the finite number of mocks on the likelihood estimator and find none of these have a significant impact in the final results. Combining the pre- and post-reconstruction signals at the level of the summary statistics is an attractive, faster and accurate method to be used in future and on-going spectroscopic surveys.
SimBIG: mock challenge for a forward modeling approach to galaxy clustering
Simulation-Based Inference of Galaxies (SimBIG) is a forward modeling framework for analyzing galaxy clustering using simulation-based inference. In this work, we present the SimBIG forward model, which is designed to match the observed SDSS-III BOSS CMASS galaxy sample. The forward model is based on high-resolution Quijote N-body simulations and a flexible halo occupation model. It includes full survey realism and models observational systematics such as angular masking and fiber collisions. We present the “mock challenge” for validating the accuracy of posteriors inferred from SimBIG using a suite of 1,500 test simulations constructed using forward models with a different N-body simulation, halo finder, and halo occupation prescription. As a demonstration of SimBIG, we analyze the power spectrum multipoles out to k max = 0.5 h/Mpc and infer the posterior of ΛCDM cosmological and halo occupation parameters. Based on the mock challenge, we find that our constraints on Ω m and σ 8 are unbiased, but conservative. Hence, the mock challenge demonstrates that SimBIG provides a robust framework for inferring cosmological parameters from galaxy clustering on non-linear scales and a complete framework for handling observational systematics. In subsequent work, we will use SimBIG to analyze summary statistics beyond the power spectrum including the bispectrum, marked power spectrum, skew spectrum, wavelet statistics, and field-level statistics.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
Contact Info
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Product
Resources
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.
