Photometric Supernova Cosmology with BEAMS and SDSS-II

TL;DR

This paper introduces the BEAMS algorithm for photometric supernova cosmology, demonstrating its effectiveness in analyzing large datasets without spectroscopic confirmation, and applying it to SDSS-II data to improve cosmological constraints.

Contribution

The paper presents the BEAMS Bayesian framework for using probabilistic supernova classifications in cosmology, validated with simulations and applied to SDSS-II data to enhance parameter estimation.

Findings

BEAMS reduces cosmological parameter contour areas by a factor of three.

Application to SDSS-II data yields mma_m=0.194\u00b10.07.

BEAMS outperforms traditional methods with biased or limited data.

Abstract

Supernova 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 supernovae with their probabilities derived from their multi-band lightcurves. We run the BEAMS algorithm on both Gaussian and more realistic SNANA simulations with of order 10^4 supernovae, testing the algorithm against various pitfalls one might expect in the new and somewhat uncharted territory of photometric supernova cosmology. We compare the performance of BEAMS to that of both mock spectroscopic surveys and photometric samples which have been cut using typical selection criteria. The latter typically are either 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 supernovae with host spectroscopic redshifts. In this case, BEAMS reduces the area of the (\Omega_m,\Omega_\Lambda) contours by a factor of three relative to the case where only spectroscopically confirmed data are used (297 supernovae). In the case of flatness, the constraints obtained on the matter density applying BEAMS to the photometric SDSS-II data are \Omega_m(BEAMS)=0.194\pm0.07. This illustrates the potential power of BEAMS for future large photometric supernova surveys such as LSST.