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

TL;DR

This paper introduces a novel MCMC-based method for sampling the probability distributions of Type Ia supernova lightcurve parameters, improving the accuracy of cosmological constraints in precision cosmology.

Contribution

The paper develops and validates a new sampling technique for SN Ia lightcurve parameters within MCMC analysis, enhancing cosmological parameter estimation accuracy.

Findings

Sampling lightcurve parameter PDFs yields cosmological parameters closer to a flat universe with a cosmological constant.

The method improves robustness of cosmological constraints compared to using only best-fit lightcurve parameters.

Validation with simulated data confirms the effectiveness of the proposed sampling approach.

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.