REVIEW 2 cited by
The Renormalization Group for Large-Scale Structure: Origin of Galaxy Stochasticity
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
Signed reviews
abstract
The renormalization group equations for large-scale structure (RG-LSS) describe how the bias and stochastic (noise) parameters -- both of matter and biased tracers such as galaxies -- evolve as a function of the cutoff $\Lambda$ of the effective field theory. In previous work, we derived the RG-LSS equations for the bias parameters using the Wilson-Polchinski framework. Here, we extend these results to include stochastic contributions, corresponding to terms in the effective action that are higher order in the current $J$. We derive the general local interaction terms that describe stochasticity at all orders in perturbations, and a closed set of nonlinear RG equations for their coefficients. These imply that a single nonlinear bias term generates all stochastic moments through RG evolution. Further, the evolution is controlled by a different, lower scale than the nonlinear scale. This has implications for the optimal choice of the renormalization scale when comparing the theory with data to obtain cosmological constraints.
Forward citations
Cited by 2 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.
-
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.
Discussion (0). Continue with ORCID to comment.