| Name: | Ramanpreet Singh |
| Affiliation: | IISER Mohali |
| Conference ID : | ASI2024_623 |
| Title : | Revisiting Supernova type Ia data |
| Authors : | Ramanpreet Singh, Athul C N, Harvinder kaur jassal |
| Authors Affiliation: | IISER Mohali, Knowledge city, Sector 81, SAS Nagar, Manauli PO 140306 |
| Mode of Presentation: | Poster |
| Abstract Category : | Galaxies and Cosmology |
| Abstract : | Cosmological parameter fitting continues to be important given that extensive data is available. While the cosmological parameters have been fixed to unprecedented precision, there are tensions in cosmological quantities obtained from different data. In this work, we explore different distributions for the residuals, mainly Gaussian and Logistic distributions. We analyse the PANTHEON supernova data set with different dark energy scenarios, including standard $\Lambda$CDM and with different parameterizations. We also work with a new parameterization called 'linear' due to linear dependence of the equation of state parameter with redshift. We will establish using different tests (AIC test, likelihood ratio test etc.) that the Logistic likelihood is the better fit than Gaussian likelihood. And once it is established that Logistic likelihood is better among different parameterizations, we also find the probabilities of different parameterizations (in Logistic case) using Akaike weight for full set and binned sets where binning is done in three different ways: $z\lessgtr$ 0.5 bins, two and three equal bins. |
