Sampling the Probability Distribution of Type Ia Supernova Lightcurve Parameters in Cosmological Analysis

Abstract

In order to obtain robust cosmological constraints from Type Ia supernova (SN Ia) data, we have applied Markov Chain Monte Carlo (MCMC) to SN Ia lightcurve fitting. We develop a method for sampling the resultant probability density distributions (pdf) of the SN Ia lightcuve parameters in the MCMC likelihood analysis to constrain cosmological parameters, and validate it using simulated data sets. Applying this method to the Joint Lightcurve Analysis (JLA) data set of SNe Ia, we find that sampling the SN Ia lightcurve parameter pdf's leads to cosmological parameters closer to that of a flat Universe with a cosmological constant, compared to the usual practice of using only the best fit values of the SN Ia lightcurve parameters. Our method will be useful in the use of SN Ia data for precision cosmology.

9 pages, 6 figures, 4 tables. Revised version accepted by MNRAS

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• First Cosmology Results Using Type Ia Supernovae From the Dark Energy Survey: Analysis, Systematic Uncertainties, and Validation
• The Foundation Supernova Survey: Measuring Cosmological Parameters with Supernovae from a Single Telescope
• A comprehensive investigation on the slowing down of cosmic acceleration
• Photometric classification and redshift estimation of LSST Supernovae
• Cosmological constraints from low-redshift data