Arc Statistics in Cosmological Models with Dark Energy

Astronomy and Astrophysics – Astrophysics

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

8 pages, accepted by A&A

Scientific paper

10.1051/0004-6361:20031158

We investigate how the probability of the formation of giant arcs in galaxy clusters is expected to change in cosmological models dominated by dark energy with an equation of state p=w rho c^2 compared to cosmological-constant or open models. To do so, we use a simple analytic model for arc cross sections based on the Navarro-Frenk-White density profile which we demonstrate reproduces essential features of numerically determined arc cross sections. Since analytic lens models are known to be inadequate for accurate absolute quantifications of arc probabilities, we use them only for studying changes relative to cosmological-constant models. Our main results are (1) the order of magnitude difference between the arc probabilities in low density, spatially flat and open CDM models found numerically is reproduced by our analytic model, and (2) dark-energy cosmologies with w>-1 increase the arc optical depth by at most a factor of two and are thus unlikely to reconcile arc statistics with spatially flat cosmological models with low matter density.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Arc Statistics in Cosmological Models with Dark Energy 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 Arc Statistics in Cosmological Models with Dark Energy, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Arc Statistics in Cosmological Models with Dark Energy will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-164680

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.