Abstract Details
| Name: Ujjwal Kumar Upadhyay Affiliation: Indian Institute of Science Conference ID: ASI2024_551 Title : Non-linear regression with errors on both axes and its implications on Hubble tension Authors and Co-Authors : Ujjwal Kumar Upadhyay 1,2 Tarun Deep Saini 1 Abstract Type : Poster Abstract Category : Galaxies and Cosmology Abstract : While fitting a non-linear model to data, it is common to consider errors only in the dependent vari- able, and treat other variables as perfectly measured. A more flexible model fitting considering errors in independent variables is expected to better estimate the parameters of the model from the same data. We employ a Bayesian method to consider redshift errors in the Pantheon sample of Type-Ia supernovae, and find that it improves the ΛCDM fit to the data. We are investigating the implications of this method on cosmological tension in the value of Hubble constant H0 with presently available data and with simulated data of larger volume and better quality expected to be available in the future. |