On The Non-Gaussian Errors in High-z Supernovae Type Ia Data

TL;DR

This paper investigates whether the errors in high-redshift supernovae Type Ia data are Gaussian or non-Gaussian, using the Kolmogorov-Smirnov test across four major data sets, and finds consistency with Gaussian errors.

Contribution

It applies the Kolmogorov-Smirnov test to assess the Gaussianity of errors in multiple supernova datasets, providing a systematic analysis of error distribution.

Findings

Errors are consistent with Gaussian distribution in all datasets

No significant non-Gaussian errors detected

Supports the assumption of Gaussian errors in supernova cosmology

Abstract

The nature of random errors in any data set that is Gaussian 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 can not 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), Union2 catalogue and the Union2.1 data set for our analysis. Our results shows that in all four data sets the errors are consistent with the Gaussian distribution.