Nonparametric Dark Energy Reconstruction from Supernova Data

Details Nonparametric Dark Energy Reconstruction from Supernova Data Nonparametric Dark Energy Reconstruction from Supernova Data

2010-11-13

arxiv.org/abs/1011.3079v1

Phys.Rev.Lett.105:241302,2010

Astronomy and Astrophysics

Astrophysics

Cosmology and Extragalactic Astrophysics

4 pages, 2 figures, accepted for publication in Physical Review Letters

Scientific paper

10.1103/PhysRevLett.105.241302

Understanding the origin of the accelerated expansion of the Universe poses one of the greatest challenges in physics today. Lacking a compelling fundamental theory to test, observational efforts are targeted at a better characterization of the underlying cause. If a new form of mass-energy, dark energy, is driving the acceleration, the redshift evolution of the equation of state parameter w(z) will hold essential clues as to its origin. To best exploit data from observations it is necessary to develop a robust and accurate reconstruction approach, with controlled errors, for w(z). We introduce a new, nonparametric method for solving the associated statistical inverse problem based on Gaussian Process modeling and Markov chain Monte Carlo sampling. Applying this method to recent supernova measurements, we reconstruct the continuous history of w out to redshift z=1.5.

Affiliated with

Also associated with

No associations

Rating

Nonparametric Dark Energy Reconstruction from Supernova Data does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Nonparametric Dark Energy Reconstruction from Supernova Data, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Nonparametric Dark Energy Reconstruction from Supernova Data will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-445103