TRRlog Model for Supernovae: Resolving the Hubble Tension with Pantheon+ Data (2025)

This repository contains the Pantheon+ Supernova dataset, the corresponding TRRlog model implementation, and the results from applying the TRRlog model to the dataset. The TRRlog model is used to investigate the Hubble Tension in cosmology, offering a dynamic treatment of dark energy through the parameter η\etaη. The results indicate that the TRRlog model provides a better fit to the Pantheon+ Supernova data, particularly in resolving the discrepancies related to the Hubble constant. Files included: Pantheon+ Supernova DatasetThe dataset contains the supernova data used for model fitting, including measurements of redshift, magnitude, and host galaxy mass. The data is provided in a CSV/Tab-delimited format. TRRlog Model Python CodeThe Python code is implemented to fit the TRRlog model to the supernova data. This includes data preprocessing, model fitting, and optimization routines, all using the L-BFGS-B optimization method. ResultsThe results of the TRRlog model fitting, including the best-fit parameters, η\etaη values, chi-squared statistics, and a comparison with the ΛCDM model, are included. These results demonstrate the model’s effectiveness in addressing the Hubble tension. README FileA README file is provided to explain the data, model implementation, and how to use the Python code. The README also includes instructions on how to interpret the results and how to reproduce the analysis. Use of the Dataset and Code: This repository provides the necessary components for researchers to replicate the analysis of the Hubble tension using the TRRlog model. The provided Python code can be used to fit the model to the Pantheon+ Supernova dataset and explore the results. DOI for Data:The DOI associated with this dataset and model is: DOI: 10.5281/zenodo.18079120