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The Renormalization Group for Large-Scale Structure: Origin of Galaxy Stochasticity

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arxiv 2404.16929 v2 pith:YSKXR2I7 submitted 2024-04-25 astro-ph.CO hep-th

classification astro-ph.COhep-th
keywords biasequationsnonlinearrenormalizationscalestochasticdescribeeffective
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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.

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Two-loop renormalization and running of galaxy bias

    astro-ph.CO 2025-07 accept novelty 7.0 of 10

    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.

  2. Non-Gaussian Galaxy Stochasticity and the Noise-Field Formulation

    astro-ph.CO 2025-11 conditional novelty 6.0 of 10

    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.

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