Skip to main navigation Skip to search Skip to main content

Two-loop renormalization and running of galaxy bias

  • Thomas Bakx
  • , Mathias Garny
  • , Henrique Rubira
  • , Zvonimir Vlah
  • Technical University of Munich
  • Ludwig Maximilian University of Munich
  • University of Cambridge
  • Ruder Boskovic Institute

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

We systematically extend the framework of galaxy bias renormalization to two-loop order. For the minimal complete basis of 29 deterministic bias operators up to fifth order in the density field and at leading order in gradient expansion we explicitly work out one- and two-loop renormalization. The latter is provided in terms of double-hard limits of bias kernels, which we find to depend on only one function of the ratio of the loop momenta. After including stochasticity in terms of composite operator renormalization, we apply the framework to the two-loop power spectrum of biased tracers and provide a simple result suitable for numerical evaluation. In addition, we work out one- and two-loop renormalization group equations (RGE) for deterministic bias coefficients related to bias operators constructed from a smoothed density field, generalizing previous works. We identify a linear combination of bias operators with enhanced UV sensitivity, related to a positive eigenvalue of the RGE. Finally, we present an analogy with the RGE as used in quantum field theory, suggesting that a resummation of large logarithms as employed in the latter may also yield useful applications in the study of large-scale galaxy bias.

Original languageEnglish
Article number056
JournalJournal of Cosmology and Astroparticle Physics
Volume2026
Issue number3
DOIs
Publication statusPublished - 1 Mar 2026

Bibliographical note

Publisher Copyright:
© 2026 The Author(s)

Keywords

  • cosmological parameters from LSS
  • galaxy surveys
  • power spectrum

Fingerprint

Dive into the research topics of 'Two-loop renormalization and running of galaxy bias'. Together they form a unique fingerprint.

Cite this