EFTofLSS meets simulation-based inference: ${\sigma }_8$ from biased tracers

Abstract

Cosmological inferences typically rely on explicit expressions for the likelihood and covariance of the data vector, which normally consists of a set of summary statistics. However, in the case of nonlinear large-scale structure, exact expressions for either likelihood or covariance are unknown, and even approximate expressions can become very cumbersome, depending on the scales and summary statistics considered. Simulation-based inference (SBI), in contrast, does not require an explicit form for the likelihood but only a prior and a simulator, thereby naturally circumventing these issues. In this paper, we explore how this technique can be used to infer ${\sigma }_8$ from a Lagrangian effective field theory (EFT) based forward model for biased tracers. The power spectrum and bispectrum are used as summary statistics to obtain the posterior of the cosmological, bias and noise parameters via neural density estimation. We compare full simulation-based inference with cases where the data vector is drawn from a Gaussian likelihood with sample and analytical covariances. We conclude that, for $k_{\text{max}}=0.1h{\text{Mpc}}^{-1}$ and $0.2h{\text{Mpc}}^{-1}$, the form of the covariance is more important than the non-Gaussianity of the likelihood, although this conclusion is expected to depend on the cosmological parameter inferred, the summary statistics considered and range of scales probed.

arXiv comment42 pages, 17 figures

References in corpus (99)

• Adam: A Method for Stochastic Optimization
• Auto-Encoding Variational Bayes
• Generative Adversarial Networks
• Toward a halo mass function for precision cosmology: the limits of universality
• Bayes in the sky: Bayesian inference and model selection in cosmology
• Acoustic Signatures in the Primary Microwave Background Bispectrum
• The Large Scale Bias of Dark Matter Halos: Numerical Calibration and Model Tests
• Cosmology and fundamental physics with the Euclid satellite
• Large-Scale Galaxy Bias
• Cosmological Non-Linearities as an Effective Fluid
• Linking halo mass to galaxy luminosity
• The Cosmological Analysis of the SDSS/BOSS data from the Effective Field Theory of Large-Scale Structure
• The Effective Field Theorist's Approach to Gravitational Dynamics
• Cosmological Parameters from the BOSS Galaxy Power Spectrum
• Planck 2015 results. XVI. Isotropy and statistics of the CMB
• Bayesian physical reconstruction of initial conditions from large scale structure surveys
• Non-Linear Evolution of the Bispectrum of Cosmological Perturbations
• Cosmology and the Bispectrum
• Validating Bayesian Inference Algorithms with Simulation-Based Calibration
• Fast likelihood-free cosmology with neural density estimators and active learning
• Non-linear perturbation theory extension of the Boltzmann code CLASS
• Combining Full-Shape and BAO Analyses of Galaxy Power Spectra: A 1.6% CMB-independent constraint on H0
• Bias in the Effective Field Theory of Large Scale Structures
• Renormalized Halo Bias
• The BACCO Simulation Project: Exploiting the full power of large-scale structure for cosmology
• Biased Tracers and Time Evolution
• Large-scale bias in the Universe: bispectrum method
• Blinded challenge for precision cosmology with large-scale structure: results from effective field theory for the redshift-space galaxy power spectrum
• Precision measurement of the local bias of dark matter halos
• Limits on $w$CDM from the EFTofLSS with the PyBird code
• Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology
• ELUCID - Exploring the Local Universe with reConstructed Initial Density field I: Hamiltonian Markov Chain Monte Carlo Method with Particle Mesh Dynamics
• Primordial non-Gaussianity, scale-dependent bias, and the bispectrum of galaxies
• Newtonian Cosmology in Lagrangian Formulation: Foundations and Perturbation Theory
• Modeling Biased Tracers at the Field Level
• Likelihood-free inference with neural compression of DES SV weak lensing map statistics
• Nuisance hardened data compression for fast likelihood-free inference
• A Consistent Effective Theory of Long-Wavelength Cosmological Perturbations
• BOSS Correlation Function Analysis from the Effective Field Theory of Large-Scale Structure
• Cubic Halo Bias in Eulerian and Lagrangian Space
• The cosmology dependence of galaxy clustering and lensing from a hybrid $N$-body-perturbation theory model
• Approximate Bayesian Computation for Forward Modeling in Cosmology
• Precision analysis of the redshift-space galaxy bispectrum
• Recursive Solutions of Lagrangian Perturbation Theory
• Comparing approximate methods for mock catalogues and covariance matrices II: Power spectrum multipoles
• Cosmology with the Galaxy Bispectrum Multipoles: Optimal Estimation and Application to BOSS Data
• Lagrangian perturbations and the matter bispectrum I: fourth-order model for non-linear clustering
• Constructing Regularized Cosmic Propagators
• Comparing approximate methods for mock catalogues and covariance matrices III: Bispectrum
• Bayesian optimisation for likelihood-free cosmological inference
• Systematic Renormalization of the Effective Theory of Large Scale Structure
• Time-analyticity of Lagrangian particle trajectories in ideal fluid flow
• The Three-point Function as a Probe of Models for Large-scale Structure
• Towards a self-consistent halo model for the nonlinear large-scale structure
• The recursion relation in Lagrangian perturbation theory
• Statistical methods in cosmology
• Toward a robust inference method for the galaxy bispectrum: likelihood function and model selection
• Euclid Preparation. XXVIII. Forecasts for ten different higher-order weak lensing statistics
• On the insufficiency of arbitrarily precise covariance matrices: non-Gaussian weak lensing likelihoods
• Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation
• The BACCO simulation project: biased tracers in real space
• An $n$-th order Lagrangian Forward Model for Large-Scale Structure
• Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysis
• Matter trispectrum: theoretical modelling and comparison to N-body simulations
• The skewed weak lensing likelihood: why biases arise, despite data and theory being sound
• COSMIC BIRTH: Efficient Bayesian Inference of the Evolving Cosmic Web from Galaxy Surveys
• Unbiased Cosmology Inference from Biased Tracers using the EFT Likelihood
• Robust Field-level Likelihood-free Inference with Galaxies
• The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues
• Consistency tests of field level inference with the EFT likelihood
• A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful
• Likelihood Non-Gaussianity in Large-Scale Structure Analyses
• Impacts of the physical data model on the forward inference of initial conditions from biased tracers
• A robust measurement of the first higher-derivative bias of dark matter halos
• $\mathrm{S}\mathrm{IM}\mathrm{BIG}$: Mock Challenge for a Forward Modeling Approach to Galaxy Clustering
• SICRET: Supernova Ia Cosmology with truncated marginal neural Ratio EsTimation
• On the accuracy and precision of correlation functions and field-level inference in cosmology
• Dark energy by natural evolution: Constraining dark energy using Approximate Bayesian Computation
• Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks
• Systematic-free inference of the cosmic matter density field from SDSS3-BOSS data
• Cosmology inference at the field level from biased tracers in redshift-space
• Explicit Bayesian treatment of unknown foreground contaminations in galaxy surveys
• Sampling-Based Accuracy Testing of Posterior Estimators for General Inference
• A simulation-based inference pipeline for cosmic shear with the Kilo-Degree Survey
• Data Compression and Inference in Cosmology with Self-Supervised Machine Learning
• BAO scale inference from biased tracers using the EFT likelihood
• Full forward model of galaxy clustering statistics with AbacusSummit lightcones
• Quantification of high dimensional non-Gaussianities and its implication to Fisher analysis in cosmology
• Field-level inference of cosmic shear with intrinsic alignments and baryons
• Cosmological Parameter Estimation and Inference using Deep Summaries
• Debiasing Standard Siren Inference of the Hubble Constant with Marginal Neural Ratio Estimation
• Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation
• Test of Artificial Neural Networks in Likelihood-free Cosmological Constraints: A Comparison of IMNN and DAE
• Diagnostics for Conditional Density Models and Bayesian Inference Algorithms
• Hybrid SBI or How I Learned to Stop Worrying and Learn the Likelihood
• Higher-order statistics of the large-scale structure from photometric redshifts
• Estimating Cosmological Constraints from Galaxy Cluster Abundance using Simulation-Based Inference
• Contrastive Neural Ratio Estimation for Simulation-based Inference
• Validation Diagnostics for SBI algorithms based on Normalizing Flows

Cited by in corpus (16)

• How much information can be extracted from galaxy clustering at the field level?
• A Parameter-Masked Mock Data Challenge for Beyond-Two-Point Galaxy Clustering Statistics
• Cosmology with Persistent Homology: a Fisher Forecast
• Fast likelihood-free inference in the LSS Stage IV era
• BullFrog: Multi-step perturbation theory as a time integrator for cosmological simulations
• Reducing nuisance prior sensitivity via non-linear reparameterization, with application to EFT analyses of large-scale structure
• Simulation-based inference has its own Dodelson-Schneider effect (but it knows that it does)
• Straightening the Ruler: Field-Level Inference of the BAO Scale with LEFTfield
• Field-Level Comparison and Robustness Analysis of Cosmological N-body Simulations
• Cosmology with Persistent Homology: Parameter Inference via Machine Learning
• A semi-analytic estimate for the effective sound speed counterterm in the EFTofLSS
• Perturbation Theory Remixed II: Improved Modeling of Nonlinear Bispectrum
• Quantifying Weighted Morphological Content of Large-Scale Structures via Simulation-Based Inference
• Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum
• On the Connection between Field-Level Inference and $n$-point Correlation Functions
• Incorporating curved geometry in cosmological simulations