Bayesian MCMC Analysis of Magnetic Fields in Superclusters of Galaxies

This repository contains a tutorial and reproducible workflow for analyzing magnetic fields in galaxy superclusters using Bayesian statistics.
The analysis is based on RM-Synthesis of LOFAR observations and demonstrates:

• Selection of real polarized sources from RM catalogs
• Inspection of Faraday depth spectra (FDFs) and Q/U parameters
• Cross-checks with polarization and NVSS maps
• Removal of duplicates and construction of clean catalogs
• Bayesian inference of rotation measures (RMs) using MCMC with emcee

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Why Bayesian?

Astrophysical data often contain significant uncertainties and noise.
Bayesian methods allow us to:

• Incorporate prior knowledge about expected RM distributions
• Sample from full posterior distributions of parameters with emcee
• Quantify credible intervals and correlations between parameters
• Visualize posteriors with corner plots for interpretation

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Tools & Libraries

• Python (NumPy, Pandas, Matplotlib, SciPy)
• emcee — affine-invariant MCMC ensemble sampler
• corner — posterior visualization
• astropy — astrophysical utilities

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Context

This project is inspired by ongoing research in cosmic magnetism,
using LOFAR observations to understand magnetic fields in large-scale structures.
It provides a hands-on example of how Bayesian MCMC methods can be applied in astrophysics.

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Example Results

Here's a density cube with redshift of a supercluster of galaxies:

Here’s a corner plot on magnetic field distribution in superclusters of galaxies: