English

ALAverse: A falsifiable anthropic model from the string landscape

Cosmology and Nongalactic Astrophysics 2026-07-27 v1 General Relativity and Quantum Cosmology High Energy Physics - Phenomenology High Energy Physics - Theory

Abstract

We combine the axiverse with a negative cosmological constant Λ\Lambda, both originating from the string landscape, and apply an observation-time-weighted anthropic argument. Adopting a uniform prior on Λ\Lambda and a typical string-motivated axion decay constant comparable to the reduced Planck scale, this framework predicts a 40%\sim 40\% probability of observing 0.1<Ωm<0.90.1 < \Omega_m < 0.9, thereby naturally resolving the long-standing fine-tuning and coincidence problems of dark energy. Unlike many scalar-field dark energy models, the anthropic Lambda-axion universe (ALAverse) statistically disfavors slow-roll dynamics, as slow-roll requires fine-tuning of the initial field displacement. Moreover, the negative cosmological constant renders fast-roll scenarios anthropically unfavorable, since they typically yield only a very brief observational window with positive dark energy density. Having ruled out both extremes, the ALAverse characteristically predicts a moderate-roll dynamics. We derive a two-parameter parametrization in terms of δΩ\delta_\Omega and εs|\varepsilon_s| that covers ALAverse solutions as well as a broad class of canonical and phantom field models. Current observational data yield δΩ=0.0498±0.0186\delta_\Omega = -0.0498 \pm 0.0186, corresponding to a 2.7σ2.7\sigma rejection of Λ\LambdaCDM (δΩ=0\delta_\Omega = 0) and phantom models (δΩ>0\delta_\Omega > 0). The data also show a mild preference for the ALAverse over slow-roll quintessence, a trend that can be conclusively tested with future high-precision measurements of the Hubble diagram.

Cite

@article{arxiv.2607.24100,
  title  = {ALAverse: A falsifiable anthropic model from the string landscape},
  author = {Zhiqi Huang},
  journal= {arXiv preprint arXiv:2607.24100},
  year   = {2026}
}

Comments

3 figures, 1 table