Astronomy and Astrophysics – Astrophysics – Cosmology and Extragalactic Astrophysics
Scientific paper
2010-09-09
Astronomy and Astrophysics
Astrophysics
Cosmology and Extragalactic Astrophysics
17 pages, 5 figures; accepted for publication in the Astrophysical Journal
Scientific paper
Measurements of the equation of state of dark energy from surveys of thousands of Type Ia Supernovae (SNe Ia) will be limited by spectroscopic follow-up and must therefore rely on photometric identification, increasing the chance that the sample is contaminated by Core Collapse Supernovae (CC SNe). Bayesian methods for supernova cosmology can remove contamination bias while maintaining high statistical precision but are sensitive to the choice of parameterization of the contaminating distance distribution. We use simulations to investigate the form of the contaminating distribution and its dependence on the absolute magnitudes, light curve shapes, colors, extinction, and redshifts of core collapse supernovae. We find that the CC luminosity function dominates the distance distribution function, but its shape is increasingly distorted as the redshift increases and more CC SNe fall below the survey magnitude limit. The shapes and colors of the CC light curves generally shift the distance distribution, and their effect on the CC distances is correlated. We compare the simulated distances to the first year results of the SDSS-II SN survey and find that the SDSS distance distributions can be reproduced with simulated CC SNe that are ~1 mag fainter than the standard Richardson et al. (2002) luminosity functions, which do not produce a good fit. To exploit the full power of the Bayesian parameter estimation method, parameterization of the contaminating distribution should be guided by the current knowledge of the CC luminosity functions, coupled with the effects of the survey selection and magnitude-limit, and allow for systematic shifts caused by the parameters of the distance fit.
Falck Bridget L.
Hlozek Renee
Riess Adam G.
No associations
LandOfFree
Characterizing the contaminating distance distribution for Bayesian supernova cosmology 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 Characterizing the contaminating distance distribution for Bayesian supernova cosmology, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Characterizing the contaminating distance distribution for Bayesian supernova cosmology will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-671618