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The Value of $H_0$ from Gaussian Processes
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abstract
A new non-parametric method based on Gaussian Processes was proposed recently to measure the Hubble constant $H_0$. The freedom in this approach comes in the chosen covariance function, which determines how smooth the process is and how nearby points are correlated. We perform coverage tests with a thousand mock samples within the LCDM model in order to determine what covariance function provides the least biased results. The function Matern(5/2) is the best with sligthly higher errors than other covariance functions, although much more stable when compared to standard parametric analyses.
Forward citations
Cited by 2 Pith papers
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Cosmo-Learn: code for learning cosmology using different methods and mock data
An open-source toolkit that simulates late-universe cosmological observations and benchmarks MCMC, genetic algorithms, Gaussian processes, Bayesian ridge regression, and neural networks in one pipeline.
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