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Abstract: We discuss the extent to which photometric measurements alone can be used toidentify Type Ia supernovae SNIa and to determine redshift and otherparameters of interest for cosmological studies. We fit the light curve data ofthe type expected from a survey such as the one planned with Large SynopticSurvey Telescope LSST and also to remove the contamination from thecore-collapse supernovae to SNIa samples. We generate 1000 SNIa mock flux datafor each of the LSST filters based on existing design parameters, then use aMarkov Chain Monte-Carlo MCMC analysis to fit for the redshift, apparentmagnitude, stretch factor and the phase of the SNIa. We find that the modelfitting works adequately well when the true SNe redshift is below 0.5, while at$z < 0.2$ the accuracy of the photometric data is almost comparable withspectroscopic measurements of the same sample. We discuss the contamination ofType Ib-c SNIb-c and Type II supernova SNII on the SNIa data set. We findit is easy to distinguish the SNII through the large $\chi^2$ mismatch whenfitting to photometric data with Ia light curves. This is not the case forSNIb-c. We implement a statistical method based on the Bayesian estimation inorder to statistically reduce the contamination from SNIb-c for cosmologicalparameter measurements from the whole SNe sample. The proposed statisticalmethod also evaluate the fraction of the SNIa in the total SNe data set, whichprovides a valuable guide to establish the degree of contamination.



Author: Yan Gong, Asantha Cooray, Xuelei Chen

Source: https://arxiv.org/







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