4.6 Article

Influence of the correlation prior on reconstruction of the dark energy equation of state

出版社

IOP PUBLISHING LTD
DOI: 10.1088/1475-7516/2019/10/006

关键词

cosmological parameters from CMBR; cosmological parameters from LSS; dark energy experiments; dark energy theory

资金

  1. National Science Foundation of China [11721303]
  2. National Key R&D Program of China [2016YFB1000605]
  3. China Manned Space Program through the Space Application System
  4. NSF China [11775110, 11690034]
  5. European Union's Horizon 2020 research and innovation programme (RISE) under the Marie Sklodowska-Curie grant [644121]
  6. Priority Academic Program Development for Jiangsu Higher Education Institutions (PAPD)

向作者/读者索取更多资源

Non-parametric reconstruction of the dark energy equation of state (EoS) aims to determine the EoS as a function of redshift without invoking any particular dark energy model, so that the resulting EoS can be free of model-induced biases or artifacts. Without proper regularization, however, such reconstruction is often overwhelmed by the noise of poorly constrained modes. An intuitive regularization scheme is to assume a priori the dark energy EoS to evolve at most slowly with time, which may be enforced by a correlation between the EoS at different epochs. Indeed, studies that impose the correlation prior are able to significantly reduce the uncertainties of the reconstructed EoS and even show hints for dynamical dark energy. In this work, we examine the correlation prior using mock datasets of type Ia supernovae (SNe Ia), baryonic acoustic oscillations (BAOs), age-derived Hubble parameter, Hubble constant, and cosmic microwave background. We find that even though the prior is designed to disfavor evolving equations of state, it can still accommodate spurious oscillating features at high significance. Within the 1000 mock datasets of existing observations that are generated for the concordance cosmological model, i.e., the input dark energy EoS w = -1, there are 688 (69) cases recovering an EoS that departs from -1 by more than 1 sigma (2 sigma) in one or more redshift bins. The reconstructed EoS turns up and down markedly in many cases. Moreover, inverting the signs of the randomly assigned errors of the mock data more or less reverses the behavior of the EoS. Spurious results occur even more frequently when idealized SN Ia and BAO data from future surveys are included. Our tests suggest that further studies are needed to ensure accurate reconstruction of the EoS with the correlation prior.

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