New dark energy constraints from supernovae, microwave background and
galaxy clustering
Fig 1 from Wang & Tegmark,
astro-ph/0403292:
Using the spectacular new high redshift supernova observations from the HST/GOODS
program and previous supernova, CMB and galaxy clustering data, we make the most
accurate measurements to date of the dark energy density rho_X as a function of
cosmic time, constraining it in a rather model-independent way. We find that
Einstein's vanilla scenario where rho_X(z) is constant remains consistent with
these new tight constraints, and that a big crunch or
big rip is more than 50 gigayears away for a broader class of models
allowing such cataclysmic events.
This figure shows the
68 % confidence level constraints on the density of matter and dark energy
from SN Ia, CMB and LSS data, all in units of the current
dark energy density.
From inside out, the four nested dark energy constraints are
for models maling increasingly strong assumptions,
corresponding, respectively, to the 4-parameter spline,
the 3-parameter spline, the 2-parameter (f_{infty},w_i) case
and the 1-parameter constant w case (hatched).
The Universe starts accelerating when
the total density slope d{ln(rho)}/d{ln(1+z)}>-2, which roughly
corresponds to when dark energy begins to
dominate, i.e., to where the matter and dark energy bands cross.
In the distant future, the Universe recollapses if the dark energy density rho_X
goes negative and ends in a ``big rip'' if it keeps growing
(d{ln(rho_X)/d{ln(1+z)}<0).
Flux-averaging statistics of supernova data was used in our analysis.
Yun Wang's
Supernova Flux-averaging Likelihood Code
Flux-averaging justifies the use
of the distance-redshift relation for a smooth universe
in the analysis of type Ia supernova (SN Ia) data.
Flux-averaging of SN Ia data is required
to yield cosmological parameter constraints that are free
of the bias induced by weak gravitational lensing.
Click here for
the paper by Wang (1999) for more details.
SN Ia data are converted into flux. For a given cosmological model, the distance
dependence of the data is removed, then the data are binned in redshift, and placed
at the average redshift in each redshift bin. The likelihood of the given cosmological model
is then computed using ``flux statistics''.
Click here for
the paper by Wang & Mukherjee (2003) in which a consistent framework for
flux-averaging analysis of supernova data is presented.
Fortran Code:
Click here to download the Fortran code
that computes the likelihood of an arbitrary cosmological model
[with given AngularDiameterDistance(z)] using flux-averaged type Ia supernova data.
The Riess 2007 sample is included at present.
To use this code, replace the function
AngularDiameterDistance(z) with the AngularDiameterDistance(z)
from your model. Report any problems to wang at nhn dot ou dot edu.
No questions about fortran programming please.
The code contains informative details to assist you
in using the code.
This code will run as either Fortran 90 or Fortran 77.
To unpack the files, type:
gunzip SNcode.tar.gz
tar -xvf SNcode.tar
New Features of Current Version (v2):
(1) marginalization over H_0 added;
(2) compatible with cosmomc.
If you use this code, please reference these three papers:
(1) Yun Wang, "Flux-averaging Analysis of Type Ia Supernova Data",
ApJ, 536, 531 (2000), astro-ph/9907405
(This paper introduces the concept of flux-averaging.)
(2) Yun Wang, and Pia Mukherjee, "Model-Independent Constraints on
Dark Energy Density from Flux-averaging Analysis of Type Ia
Supernova Data", ApJ, 606, 654 (2004),
astro-ph/0312192
(This paper presents a consistent framework for flux-averaging
and shows that flux-avearging leads to less biased values for
estimated parameters.)
(3) Yun Wang, and Pia Mukherjee, "Observational Constraints on
Dark Energy and Cosmic Curvature, PRD submitted,
astro-ph/0703780
(This paper gives the formulae for marginalization over H_0.)
Click here if
you are interested in other research of mine.
This work was funded in part by a NSF CAREER award.
This page was last modified March 30, 2007.
Send comments to wang at nhn dot ou dot edu.