Computer Science
Scientific paper
Jun 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aipc.1241..200b&link_type=abstract
INVISIBLE UNIVERSE: Proceedings of the Conference. AIP Conference Proceedings, Volume 1241, pp. 200-208 (2010).
Computer Science
1
Dark Matter, Supernovae, Genetic Algorithms, Cosmology, Dark Energy, Supernovae, Data Analysis: Algorithms And Implementation, Data Management, Origin And Formation Of The Universe
Scientific paper
We introduce genetic algorithms as a means to analyze supernovae type Ia data and extract model-independent constraints on the evolution of the Dark Energy equation of state w(z) ≡ PDEρDE Specifically, we will give a brief introduction DE to the genetic algorithms along with some simple examples to illustrate their advantages and finally we will apply them to the supernovae type Ia data. We find that genetic algorithms can lead to results in line with already established parametric and non-parametric reconstruction methods and could be used as a complementary way of treating SNIa data. As a non-parametric method, genetic algorithms provide a model-independent way to analyze data and can minimize bias due to premature choice of a dark energy model.
Bogdanos Charalampos
Nesseris Savvas
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