KLT-Net reconstructs the SN Ia distance modulus non-parametrically; with MFV M_B and flat-ΛCDM Bayesian/Hessian inference it yields H0 ≈ 69.6 km s⁻¹ Mpc⁻¹ and Ωm ≈ 0.30.
PRD111(4), 043503 (2025) https://arxiv.org/abs/2406
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Bayesian constraints on seven w(a) parameterizations with CMB+BAO+SNIa datasets favor the logarithmic model over LambdaCDM in several combinations and show modest sigma8 relief.
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KAN-LSTM-Transformer Neural Networks, MFV and Cosmological Parameters
KLT-Net reconstructs the SN Ia distance modulus non-parametrically; with MFV M_B and flat-ΛCDM Bayesian/Hessian inference it yields H0 ≈ 69.6 km s⁻¹ Mpc⁻¹ and Ωm ≈ 0.30.
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Constraints of dynamical dark energy models from different observational datasets
Bayesian constraints on seven w(a) parameterizations with CMB+BAO+SNIa datasets favor the logarithmic model over LambdaCDM in several combinations and show modest sigma8 relief.