Name
Abstract | ||
|---|---|---|
The statefinder parameters characterize the expansion history of the Universe in a model independent way. The standard method to estimate them is named Standard Cosmography (SC). In this paper we show how these estimations turn out to be highly biased and the standard deviations of their probability distributions very large. The Eis method was tailored to minimize these drawbacks. Here, with the aid of mock supernovae catalogs, we show how our new method works, and that it surpasses the performance of SC for both the bias and dispersion of the estimated statefinders. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-57972-6_30 | Communications in Computer and Information Science |
Field | DocType | Volume |
Statistical physics,Dark matter,Markov chain Monte Carlo,Cosmography,Computer science,Dark energy,Parallel computing,Probability distribution,Universe,Cold dark matter,Standard deviation | Conference | 697 |
ISSN | Citations | PageRank |
1865-0929 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaime Klapp | 1 | 11 | 5.72 |
| Alejandro Aviles | 2 | 0 | 0.34 |
| Orlando Luongo | 3 | 0 | 0.34 |