Characterizing the Contaminating Distance Distribution for Bayesian Supernova Cosmology

Details Characterizing the Contaminating Distance Distribution for Bayesian Supernova Cosmology Characterizing the Contaminating Distance Distribution for Bayesian Supernova Cosmology

Jan 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aas...21543701f&link_type=abstract

American Astronomical Society, AAS Meeting #215, #437.01; Bulletin of the American Astronomical Society, Vol. 42, p.390

Astronomy and Astrophysics

Astronomy

Scientific paper

New large-scale surveys such as Pan-STARRS and LSST will obtain light curves of thousands to millions of supernovae, but spectroscopic follow-up will only be possible for a few percent of candidates. Measurements of the equation of state of dark energy with Type Ia Supernovae (SNe Ia) 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 that account for and marginalize over the presence of contaminants 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, but only after substantially changing the intrinsic properties of CC SNe input to the simulations, and that the details of the SDSS sample selection process play an important role in shaping the contaminating distance distribution. 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.

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