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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Quasar X-ray/UV luminosity relation shows non-linear redshift dependence that cannot be fixed by linear correction and requires further modeling or data screening for cosmological use.
The ANN-reconstructed Hubble parameter H(z) from cosmic chronometers aligns with Lambda CDM predictions within uncertainties.
citing papers explorer
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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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Deep Learning Calibration of the Quasar X-ray/UV Luminosity Relation for Cosmological Applications
Quasar X-ray/UV luminosity relation shows non-linear redshift dependence that cannot be fixed by linear correction and requires further modeling or data screening for cosmological use.
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Testing $\Lambda$CDM with ANN-Reconstructed Expansion History from Cosmic Chronometers
The ANN-reconstructed Hubble parameter H(z) from cosmic chronometers aligns with Lambda CDM predictions within uncertainties.