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.
Nature Reviews Physics2(1), 10–12 (2020) https://arxiv.org/abs/2001
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Bayesian photometric cosmic chronometer analysis on VIPERS PDR2 data yields H(z=0.65)=93.68±28.27(stat)±10.67(syst) km/s/Mpc, consistent with spectroscopic CC results and Planck ΛCDM, as a proof of concept for photometric surveys.
Joint analysis of DESI DR2, Pantheon+, and cosmic chronometers yields a mild statistical preference for time-varying dark energy over LambdaCDM, though constraints on the evolution remain moderate.
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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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Towards Bayesian Photometric Cosmic Chronometers: Application to VIPERS
Bayesian photometric cosmic chronometer analysis on VIPERS PDR2 data yields H(z=0.65)=93.68±28.27(stat)±10.67(syst) km/s/Mpc, consistent with spectroscopic CC results and Planck ΛCDM, as a proof of concept for photometric surveys.
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Probing the Evolution of Dark Energy: A Joint Analysis of DESI DR2, Pantheon+, and Cosmic Chronometers
Joint analysis of DESI DR2, Pantheon+, and cosmic chronometers yields a mild statistical preference for time-varying dark energy over LambdaCDM, though constraints on the evolution remain moderate.