Straightening the Ruler: Field-Level Inference of the BAO Scale with LEFTfield

Abstract

Current inferences of the BAO scale from galaxy clustering employ a reconstruction technique at fixed cosmology and bias parameters. Here, we present the first consistent joint Bayesian inference of the isotropic BAO scale, jointly varying the initial conditions as well as all bias coefficients, based on the EFT-based field-level forward model $\text{LEFTfield}$. We apply this analysis to mock data generated at a much higher cutoff, or resolution, resulting in a significant model mismatch between mock data and the model used in the inference. We demonstrate that the remaining systematic bias in the BAO scale is below 2% for all data considered and below 1% when Eulerian bias is used for inference. Furthermore, we find that the inferred error on the BAO scale is typically 30%, and up to 50%, smaller compared to that from a replication of the standard post-reconstruction power-spectrum approach, using the same scales as in the field-level inference. The improvement in BAO scale precision grows towards smaller scales (higher $k$). As a validation test, we repeat this comparison on a mock dataset that is linearly biased with respect to a 1LPT (Zel'dovich) density field, following the assumption made in standard reconstruction approaches. We find that field-level inference indeed yields the same error bar as the post-reconstruction power spectrum, which is expectd to be optimal in this case. In summary, a field-level approach to BAO not only allows for a consistent inference of the BAO scale, but promises to achieve more precise measurements on realistic, nonlinearly biased tracers as well.

arXiv comment30 pages, 15 figures

References in corpus (36)

• emcee: The MCMC Hammer
• Large-Scale Galaxy Bias
• Cosmological Non-Linearities as an Effective Fluid
• The Effective Field Theory of Cosmological Large Scale Structures
• Improving Cosmological Distance Measurements by Reconstruction of the Baryon Acoustic Peak
• Resumming Cosmological Perturbations via the Lagrangian Picture: One-loop Results in Real Space and in Redshift Space
• On the Robustness of the Acoustic Scale in the Low-Redshift Clustering of Matter
• Improved forecasts for the baryon acoustic oscillations and cosmological distance scale
• Biased Tracers and Time Evolution
• Reconstructing Baryon Oscillations: A Lagrangian Theory Perspective
• The Shift of the Baryon Acoustic Oscillation Scale: A Simple Physical Picture
• Fast Hamiltonian sampling for large scale structure inference
• Reconstructing baryon oscillations
• Iterative initial condition reconstruction
• Eulerian BAO Reconstructions and N-Point Statistics
• Recursive Solutions of Lagrangian Perturbation Theory
• Reconstruction in Fourier space
• A rigorous EFT-based forward model for large-scale structure
• Time-analyticity of Lagrangian particle trajectories in ideal fluid flow
• Towards an Optimal Reconstruction of Baryon Oscillations
• The recursion relation in Lagrangian perturbation theory
• Cosmological inference from Bayesian forward modelling of deep galaxy redshift surveys
• An $n$-th order Lagrangian Forward Model for Large-Scale Structure
• How much information can be extracted from galaxy clustering at the field level?
• Large-Scale Structure Observables in General Relativity
• Unbiased Cosmology Inference from Biased Tracers using the EFT Likelihood
• Cosmology Inference from Biased Tracers using the EFT-based Likelihood
• The EFT Likelihood for Large-Scale Structure
• New method for initial density reconstruction
• Consistency tests of field level inference with the EFT likelihood
• Isobaric Reconstruction of the Baryonic Acoustic Oscillation
• Sigma-Eight at the Percent Level: The EFT Likelihood in Real Space
• A Parameter-Masked Mock Data Challenge for Beyond-Two-Point Galaxy Clustering Statistics
• Cosmology inference at the field level from biased tracers in redshift-space
• EFTofLSS meets simulation-based inference: ${\sigma }_8$ from biased tracers
• BAO scale inference from biased tracers using the EFT likelihood

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