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-fitting values of the SN Ia lightcurve parameters. Our method will be useful in the use of SN Ia data for precision cosmology.
Additional Information
© 2016 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. Accepted 2016 March 29. Received 2016 March 29; in original form 2015 May 19. First published online April 5, 2016. We are grateful to Rick Kessler and Alex Conley for very helpful discussions. The computing for this project was performed at the OU Supercomputing Center for Education & Research (OSCER) at the University of Oklahoma (OU).Attached Files
Published - MNRAS-2016-Dai-1819-26.pdf
Submitted - 1505.05086v2.pdf
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Additional details
- Eprint ID
- 68882
- Resolver ID
- CaltechAUTHORS:20160707-090752668
- Created
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2016-07-07Created from EPrint's datestamp field
- Updated
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2021-11-11Created from EPrint's last_modified field
- Caltech groups
- Infrared Processing and Analysis Center (IPAC)