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The Redshift-Space Galaxy Power Spectrum at Two-Loop Order

Published 9 Oct 2026 in astro-ph.CO, gr-qc, hep-ex, hep-ph, and hep-th | (2610.13867v1)

Abstract: Contemporary large-scale structure analyses center around the galaxy power spectrum, modeled with one-loop perturbation theory. This description naturally breaks down at large wavenumbers, prohibiting the extraction of cosmological information from small-scale clustering. In this work, we present a complete model for the redshift-space galaxy power spectrum up to two-loop order, derived in the framework of the Effective Field Theory of Large Scale Structure. This accounts for a wide variety of effects, including fifth-order galaxy bias, redshift-space distortions, small-scale non-linearities, spatial non-locality, long-wavelength bulk flows, coordinate distortions, and stochasticity, involving a total of 49 free coefficients. Our model additionally encodes all deterministic contributions to the one-loop bispectrum and trispectrum (as well as the tree-level quadspectrum and pentaspectrum), which depend on many of the same parameters. We implement the model numerically, using the \textsc{vegas} algorithm to precompute the loop integrals; this allows the full likelihood to be evaluated in milliseconds. The approach is validated using power spectra measured from high-resolution simulations, considering both dark matter and realistic galaxy samples. The extension to two-loop order greatly increases the range of scales over which the power spectrum can be modeled, with kmaxk_{\rm max} rising from 0.2h Mpc<sup>−10.2h\,\mathrm{Mpc}<sup>{-1} to 0.34h Mpc<sup>−10.34h\,\mathrm{Mpc}<sup>{-1} for a luminous red galaxy sample at z=0.5z=0.5. Much of the additional information is absorbed by the numerous fifth-order biases and counterterms; for this reason, constraints on σ8σ_8 are not significantly enhanced by the inclusion of two-loop terms. Future work will assess the full implications of the two-loop model for cosmological parameter inference, both alone and in combination with higher-order statistics.

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