REVIEW 4 cited by
The EFT Likelihood for Large-Scale Structure
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We derive, using functional methods and the bias expansion, the conditional likelihood for observing a specific tracer field given an underlying matter field. This likelihood is necessary for Bayesian-inference methods. If we neglect all stochastic terms apart from the ones appearing in the auto two-point function of tracers, we recover the result of Schmidt et al., 2018. We then rigorously derive the corrections to this result, such as those coming from a non-Gaussian stochasticity (which include the stochastic corrections to the tracer bispectrum) and higher-derivative terms. We discuss how these corrections can affect current applications of Bayesian inference. We comment on possible extensions to our result, with a particular eye towards primordial non-Gaussianity. This work puts on solid theoretical grounds the EFT-based approach to Bayesian forward modeling.
Forward citations
Cited by 4 Pith papers
-
Two-loop renormalization and running of galaxy bias
Galaxy bias renormalization is extended to two loops for the complete fifth-order operator basis, with a single universal function controlling double-hard limits and new two-loop renormalization group equations.
-
Constraining Dynamical Dark Energy from Galaxy Clustering with Simulation-Based Priors
Adding BOSS galaxy clustering with simulation-based priors modeled as Gaussian mixtures shifts the DESI plus CMB plus supernova constraints on dark energy toward a cosmological constant and improves the w0-wa figure o...
-
Non-Gaussian Galaxy Stochasticity and the Noise-Field Formulation
Galaxy stochasticity in EFT of large-scale structure reduces to nonlinear couplings of one Gaussian noise field, yielding a samplable field-level likelihood that stabilizes the inferred noise amplitude.
-
Equivalence of the field-level inference and conventional analyses on large scales
A joint power spectrum, bispectrum and trispectrum analysis achieves the same precision on the density amplitude as field-level inference for halos on large scales.
Discussion (0). Sign in to comment.