@Article{BachegaCostAbdaForn:2020:FoInDa,
author = "Bachega, Riis R. A. and Costa, Andr{\'e} A. and Abdalla, E. and
Fornazier, Karin Silva Franzoni",
affiliation = "{Universidade de S{\~a}o Paulo (USP)} and {Yangzhou University}
and {Universidade de S{\~a}o Paulo (USP)} and {Instituto Nacional
de Pesquisas Espaciais (INPE)}",
title = "Forecasting the interaction in dark matter-dark energy models with
standard sirens from the Einstein telescope",
journal = "Journal of Cosmology and Astroparticle Physics",
year = "2020",
volume = "5",
pages = "e021",
month = "May",
keywords = "dark energy theory, gravitational wave detectors, gravitational
waves / sources, gravitational waves / theory.",
abstract = "Gravitational Waves (GW's) can determine the luminosity distance
of the progenitor directly from the amplitude of the wave, without
assuming any specific cosmological model. Thus, it can be
considered as a standard siren. The coalescence of binary neutron
stars (BNS) or neutron star-black hole pair (NSBH) can generate
GW's as well as the electromagnetic counterpart, which can be
detected in a form of Gamma-Ray Bursts (GRB) and can be used to
determine the redshift of the source. Consequently, such a
standard siren can be a very useful probe to constrain the
cosmological parameters. In this work, we consider an interacting
Dark Matter-Dark Energy (DM-DE) model. Assuming some fiducial
values for the parameters of our model, we simulate the luminosity
distance for a {"}realistic{"} and {"}optimistic{"} GW+GRB events
, which can be detected by the third-generation GW detector
Einstein Telescope (ET). Using these simulated events, we perform
a Monte Carlo Markov Chain (MCMC) to constrain the DM-DE coupling
constant and other model parameters in 1 sigma and 2 sigma
confidence levels. We also investigate how GW's can improve the
constraints obtained by current cosmological probes.",
doi = "10.1088/1475-7516/2020/05/021",
url = "http://dx.doi.org/10.1088/1475-7516/2020/05/021",
issn = "1475-7516",
language = "en",
targetfile = "bachega_forecasting.pdf",
urlaccessdate = "17 abr. 2021"
}