Comparison of cosmological parameter inference methods applied to supernovae light curves fitted with salt-II
2014 (English)In: Monthly notices of the Royal Astronomical Society, ISSN 0035-8711, E-ISSN 1365-2966, Vol. 437, no 4, 3298-3311 p.Article in journal (Refereed) Published
We present a comparison of two methods for cosmological parameter inference from Type Ia supernovae (SNeIa) light curves fitted with the salt-ii technique, in which we treat the statistical errors but not the systematic errors. The standard chi(2) methodology and the recently proposed SNeIa Bayesian hierarchical method (SNBHM) are each applied to identical sets of simulations based on the 3-yr data release from the Supernova Legacy Survey (SNLS3), and also data from the Sloan Digital Sky Survey, the low-redshift sample and the Hubble Space Telescope, assuming a concordance Lambda cold dark matter cosmology. For both methods, we find that the recovered values of the cosmological parameters, and the global nuisance parameters controlling the stretch and colour corrections to the supernovae light curves, suffer from small biases. The magnitude of the biases is similar in both cases, with the SNBHM yielding slightly more accurate results for cosmological parameters when applied to just the SNLS3 single survey data sets. Most notably, in this case, the biases in the recovered matter density (m,0) are in opposite directions for the two methods. For any given realization of the SNLS3-type data, this can result in a similar to 2 Sigma discrepancy in the estimated value of (m,0) between the two methods, which we find to be the case for real SNLS3 data. As more higher and lower redshift SNIa samples are included, however, the cosmological parameter estimates of the two methods converge.
Place, publisher, year, edition, pages
2014. Vol. 437, no 4, 3298-3311 p.
methods: data analysis, methods: statistical, supernovae: general, cosmology: miscellaneous
Astronomy, Astrophysics and Cosmology
Research subject Physics
IdentifiersURN: urn:nbn:se:su:diva-100372DOI: 10.1093/mnras/stt2114ISI: 000329177100025OAI: oai:DiVA.org:su-100372DiVA: diva2:693487