Vol 16, No 11

On the non-Gaussian errors in high-z supernovae type Ia data

Meghendra Singh, Ashwini Pandey, Amit Sharma, Shashikant Gupta, Satendra Sharma


Abstract The nature of random errors in any data set is Gaussian, which is a well established fact according to the Central Limit Theorem. Supernovae type Ia data have played a crucial role in major discoveries in cosmology. Unlike in laboratory experiments, astronomical measurements cannot be performed in controlled situations. Thus, errors in astronomical data can be more severe in terms of systematics and non-Gaussianity compared to those of laboratory experiments. In this paper, we use the Kolmogorov-Smirnov statistic to test non-Gaussianity in high-z supernovae data. We apply this statistic to four data sets, i.e., Gold data (2004), Gold data (2007), the Union2 catalog and the Union2.1 data set for our analysis. Our results show that in all four data sets the errors are consistent with a Gaussian distribution.


Keywords cosmology — data analysis — statistics — probability

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