Biconnection gravity produces extra geometric contributions to the Friedmann equations that act as effective dark energy; when parametrized in five common ways and fit to recent data, the Barboza-Alcaniz and logarithmic models are statistically competitive with LambdaCDM.
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Neural networks calibrate 2D and 3D Dainotti relations on the Platinum GRB sample via ANN-driven MCMC to produce a model-independent Hubble diagram with reduced scatter.
In f(R,Σ,T) gravity with logarithmic Om(z) parameterization, the model produces quintessence dark energy that drives acceleration without phantom crossing but exhibits classical instability.
Extended analysis of DESI DR2 data confirms robust evidence for dynamical dark energy with phantom crossing preference, stable under parametric and non-parametric modeling.
The Viaggiu holographic dark energy model fits late-time data similarly to Lambda CDM, yielding Omega_m0 around 0.24 and a model parameter of 0.27-0.33, with AIC showing statistical indistinguishability.
citing papers explorer
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Cosmological test of a length-preserving biconnection gravity
Biconnection gravity produces extra geometric contributions to the Friedmann equations that act as effective dark energy; when parametrized in five common ways and fit to recent data, the Barboza-Alcaniz and logarithmic models are statistically competitive with LambdaCDM.
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Model-independent calibration of Gamma-Ray Bursts with neural networks
Neural networks calibrate 2D and 3D Dainotti relations on the Platinum GRB sample via ANN-driven MCMC to produce a model-independent Hubble diagram with reduced scatter.
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Extended Dark Energy analysis using DESI DR2 BAO measurements
Extended analysis of DESI DR2 data confirms robust evidence for dynamical dark energy with phantom crossing preference, stable under parametric and non-parametric modeling.