4.6 Article

Weighing neutrinos in the scenario of vacuum energy interacting with cold dark matter: application of the parameterized post-Friedmann approach

出版社

IOP PUBLISHING LTD
DOI: 10.1088/1475-7516/2017/05/040

关键词

cosmological neutrinos; dark energy experiments; dark energy theory; neutrino masses from cosmology

资金

  1. National Natural Science Foundation of China [11522540, 11690021]
  2. Top-Notch Young Talents Program of China
  3. Provincial Department of Education of Liaoning [L2012087]

向作者/读者索取更多资源

We constrain the neutrino mass in the scenario of vacuum energy interacting with cold dark matter by using current cosmological observations. To avoid the large-scale instability problem in interacting dark energy models, we employ the parameterized post-Friedmann (PPF) approach to do the calculation of perturbation evolution, for the Q = beta H rho(c) and Q = beta H rho(Lambda) models. The current observational data sets used in this work include Planck (cosmic microwave background), BSH (baryon acoustic oscillations, type Ia supernovae, and Hubble constant), and LSS (redshift space distortions and weak lensing). According to the constraint results, we find that beta > 0 at more than 1 sigma level for the Q = beta H rho(c) model, which indicates that cold dark matter decays into vacuum energy; while beta = 0 is consistent with the current data at 1 sigma level for the Q = beta H rho(Lambda) model. Taking the ACDM model as a baseline model, we find that a smaller upper limit, Sigma m(nu) < 0.11 eV (2 sigma), is induced by the latest BAO BOSS DR12 data and the Hubble constant measurement H-0 = 73.00 +/- 1.75 km s(-1) Mpc(-1). For the Q = beta H rho(c) model, we obtain Sigma m(nu) < 0.20 eV (2 sigma) from Planck+BSH. For the Q = beta H rho(Lambda) model, Sigma m(nu) < 0.10 eV (2 sigma) and Sigma m(nu) < 0.14 eV (2 sigma) are derived from Planck+BSH and Planck+BSH+LSS, respectively. We show that these smaller upper limits on Sigma m(nu) are affected more or less by the tension between H-0 and other observational data.

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