A Dynamical Equilibrium Linking Nanohertz Stochastic Gravitational Wave Background to Cosmic Structure Formation
Abstract
The stochastic gravitational wave background (SGWB) is conventionally treated as a passive relic of its astrophysical and cosmological sources, with negligible back-reaction on the matter content of the Universe. Here we show that this assumption needs to be modified once the SGWB and matter are treated as a dynamically coupled non-equilibrium system. Combining linearized general relativity with the fluctuation-dissipation theorem, we derive a generalized Langevin framework that drives the coupled system toward a dynamical equilibrium, which is characterized by a distinctive strain spectrum with a high-frequency cutoff , and a scale-dependent coupling parameter that screens gravity progressively for the most massive structures. Three findings support this framework. Fitting the equilibrium spectrum to the NANOGrav 15-year dataset yields a Bayes factor of over the supermassive black hole binary baseline, achieved entirely within general relativity and the Standard Model. The PTA-calibrated screening mass scale overlaps, with no free cosmological parameter, the CDM-derived linear-to-nonlinear transition mass of cosmic structure at . Most strikingly, promoting this concordance to a structural identification expresses entirely in terms of , and its inverse acquires a transparent physical reading as a coherence threshold for SGWB-matter coupling. is thereby a derived quantity linking nanohertz gravitational-wave observables to the late-time cosmological sector. The framework makes distinctive scale-dependent predictions testable by forthcoming large-scale structure surveys and space-borne gravitational-wave observatories.
Keywords
Cite
@article{arxiv.2604.08317,
title = {A Dynamical Equilibrium Linking Nanohertz Stochastic Gravitational Wave Background to Cosmic Structure Formation},
author = {Manjia Liang and Peng Xu and Ruijun Shi and Zhoujian Cao and Ziren Luo and Minghui Du and Qiong Deng and Bo Liang and Jiaxiang Liang},
journal= {arXiv preprint arXiv:2604.08317},
year = {2026}
}
Comments
33 pages, 3 figures, 3 Tables