An iterative AI reasoning process proposes new dynamical dark energy equations of state that are competitive with traditional forms on supernova, BAO, and Planck data.
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Bayesian joint constraints show that elevated star formation efficiency accounts for JWST high-z galaxy excess in flat Lambda CDM, without requiring deviations in dark energy equation of state or curvature.
DESI data hint that dark energy evolves (w0>−1, wa<0, phantom crossing) at 2.5–4.2σ, but the same expansion history can also be produced by interacting dark sectors, modified gravity, or non-cold dark matter.
A review of string-inspired dynamical dark energy models claiming compatibility with DESI's hints of evolving dark energy under swampland constraints.
citing papers explorer
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Physics-guided discovery of dynamical dark-energy equations of state through iterative AI reasoning
An iterative AI reasoning process proposes new dynamical dark energy equations of state that are competitive with traditional forms on supernova, BAO, and Planck data.
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Dark energy, spatial curvature, and star formation efficiency from JWST photometric and spectroscopic high-redshift galaxies
Bayesian joint constraints show that elevated star formation efficiency accounts for JWST high-z galaxy excess in flat Lambda CDM, without requiring deviations in dark energy equation of state or curvature.
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Dark Energy in the DESI Era: A Brief Review of Evidence, Beyond-$\Lambda$CDM Interpretations, and Tensions
DESI data hint that dark energy evolves (w0>−1, wa<0, phantom crossing) at 2.5–4.2σ, but the same expansion history can also be produced by interacting dark sectors, modified gravity, or non-cold dark matter.
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Breaking Free from the Swampland of Impossible Universes through the DESI Portal
A review of string-inspired dynamical dark energy models claiming compatibility with DESI's hints of evolving dark energy under swampland constraints.