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Non minimally coupled condensate cosmologies: matching observational data with phase space
by
Carloni, S
, Vignolo, S
, Cianci, R
, Piedipalumbo, E
, Feola, P
in
Astronomical models
/ Computer simulation
/ Condensates
/ Cosmology
/ Dark energy
/ Gravitational fields
/ Markov analysis
/ Markov chains
/ Monte Carlo simulation
/ Parameters
/ Phase matching
2019
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Non minimally coupled condensate cosmologies: matching observational data with phase space
by
Carloni, S
, Vignolo, S
, Cianci, R
, Piedipalumbo, E
, Feola, P
in
Astronomical models
/ Computer simulation
/ Condensates
/ Cosmology
/ Dark energy
/ Gravitational fields
/ Markov analysis
/ Markov chains
/ Monte Carlo simulation
/ Parameters
/ Phase matching
2019
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Do you wish to request the book?
Non minimally coupled condensate cosmologies: matching observational data with phase space
by
Carloni, S
, Vignolo, S
, Cianci, R
, Piedipalumbo, E
, Feola, P
in
Astronomical models
/ Computer simulation
/ Condensates
/ Cosmology
/ Dark energy
/ Gravitational fields
/ Markov analysis
/ Markov chains
/ Monte Carlo simulation
/ Parameters
/ Phase matching
2019
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Non minimally coupled condensate cosmologies: matching observational data with phase space
Paper
Non minimally coupled condensate cosmologies: matching observational data with phase space
2019
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Overview
We compare theoretical predictions with observations for a class of cosmological models in which the dark energy component is modeled as a fermionic condensate, non-minimally coupled with the gravitational field and characterized by some specific self-interaction potentials. Our analysis is based on the Markov Chain Monte Carlo Method (MCMC) and employs different data sets. It turns out that with an appropriate choice of parameters our models are fully compatible with several observed data. We combine these parameter values with phase space analysis to deduce the features of the entire cosmic history of the considered models.
Publisher
Cornell University Library, arXiv.org
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