PHOTOMETRIC SUPERNOVA COSMOLOGY WITH BEAMS AND SDSS-II
- Oxford Astrophysics, Department of Physics, University of Oxford, Keble Road, Oxford, OX1 3RH (United Kingdom)
- Department de physique theorique, Universite de Geneve, 30, quai Ernest-Ansermet, CH-1211 Geneve 4 (Switzerland)
- African Institute for Mathematical Sciences, 68 Melrose Road, Muizenberg 7945 (South Africa)
- Department of Mathematics and Applied Mathematics, University of Cape Town, Rondebosch, Cape Town, 7700 (South Africa)
- The Kavli Institute for Cosmological Physics, The University of Chicago, 933 East 56th Street, Chicago, IL 60637 (United States)
- Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439 (United States)
- Institute of Cosmology and Gravitation, Dennis Sciama Building Burnaby Road Portsmouth PO1 3FX (United Kingdom)
- Las Cumbres Observatory Global Telescope Network, 6740 Cortona Drive, Suite 102, Goleta, CA 93117 (United States)
- Department of Physics and Astronomy, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218 (United States)
- Department of Physics and Astronomy, University of Pennsylvania, 203 South 33rd Street, Philadelphia, PA 19104 (United States)
Supernova (SN) cosmology without spectroscopic confirmation is an exciting new frontier, which we address here with the Bayesian Estimation Applied to Multiple Species (BEAMS) algorithm and the full three years of data from the Sloan Digital Sky Survey II Supernova Survey (SDSS-II SN). BEAMS is a Bayesian framework for using data from multiple species in statistical inference when one has the probability that each data point belongs to a given species, corresponding in this context to different types of SNe with their probabilities derived from their multi-band light curves. We run the BEAMS algorithm on both Gaussian and more realistic SNANA simulations with of order 10{sup 4} SNe, testing the algorithm against various pitfalls one might expect in the new and somewhat uncharted territory of photometric SN cosmology. We compare the performance of BEAMS to that of both mock spectroscopic surveys and photometric samples that have been cut using typical selection criteria. The latter typically either are biased due to contamination or have significantly larger contours in the cosmological parameters due to small data sets. We then apply BEAMS to the 792 SDSS-II photometric SNe with host spectroscopic redshifts. In this case, BEAMS reduces the area of the {Omega}{sub m}, {Omega}{sub {Lambda}} contours by a factor of three relative to the case where only spectroscopically confirmed data are used (297 SNe). In the case of flatness, the constraints obtained on the matter density applying BEAMS to the photometric SDSS-II data are {Omega}{sup BEAMS}{sub m} = 0.194 {+-} 0.07. This illustrates the potential power of BEAMS for future large photometric SN surveys such as Large Synoptic Survey Telescope.
- OSTI ID:
- 22037054
- Journal Information:
- Astrophysical Journal, Vol. 752, Issue 2; Other Information: Country of input: International Atomic Energy Agency (IAEA); ISSN 0004-637X
- Country of Publication:
- United States
- Language:
- English
Similar Records
The Dark Energy Survey: Cosmology Results With ~1500 New High-redshift Type Ia Supernovae Using The Full 5-year Dataset
PHOTOMETRIC ESTIMATES OF REDSHIFTS AND DISTANCE MODULI FOR TYPE Ia SUPERNOVAE