This repository contains a Python-based statistical analysis of the correlation between X-ray and radio properties of astrophysical sources (e.g., galaxy clusters, AGN).
The notebook demonstrates practical applications of statistical methods in astrophysics, including data preprocessing, correlation testing, regression, and uncertainty analysis.
- Data preprocessing of astrophysical datasets
- Correlation tests: Pearson, Spearman, and Kendall coefficients
- Linear regression modeling with uncertainty estimation
- Statistical visualization: scatter plots, regression lines, residuals
Correlations between X-ray and radio emission provide insights into the physical processes linking
thermal (X-ray) and non-thermal (radio) components in astrophysical systems.
This project showcases:
- Domain expertise in astrophysics
- Statistical analysis skills transferable to data science
- Reproducible workflows with Jupyter notebooks and Python
numpy,scipy,pandas— scientific computingmatplotlib,seaborn— visualizationlinmix— regression and statistical tests
Example scatter plot of X-ray vs Radio correlation with regression fit:
