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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The ANN-reconstructed Hubble parameter H(z) from cosmic chronometers aligns with Lambda CDM predictions within uncertainties.
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.
EMCEE outperforms GP and MAF in recovering true H0 from mock cosmic chronometer datasets, with GP most sensitive to data points via delete-d jackknife analysis.
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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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.
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Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.
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Comparative Analysis of EMCEE, Gaussian Process, and Masked Autoregressive Flow in Constraining the Hubble Constant Using Cosmic Chronometers Dataset
EMCEE outperforms GP and MAF in recovering true H0 from mock cosmic chronometer datasets, with GP most sensitive to data points via delete-d jackknife analysis.