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Rebuild Bayesian galaxy rotation-curve MCMC package - #1
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Modernize the old homework C prototype into an installable Python toolkit with a corrected bulge/disk/halo model, adaptive Metropolis–Hastings, multi-chain Gelman–Rubin diagnostics, tests, and documentation. Keep a fixed legacy C pipeline for educational reference.
Keep the pipeline small: optionally infer the halo scale length, plot data−model residuals, and verify MCMC recovery on synthetic data.
Small quality-of-life outputs: M(<R) figure, human-readable posterior CSV, reproducibility seed in the summary JSON, and a GitHub Actions pytest workflow.
Rewrite the README with a clearer value proposition, example posterior table, and feature gallery. Sync theory/usage/legacy docs with free Ah, residuals, enclosed mass, CSV outputs, mock recovery, and CI.
Add residuals, enclosed mass, corner, and MCMC traces to the README gallery so the project sells visually on GitHub.
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juandapradam12
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October 6, 2026 10:51
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Summary
Rebuilds this old Monte Carlo / Bayesian homework into a working inference package for estimating galactic bulge, disk, and halo masses from a rotation curve — with documentation that sells the project.
What’s in
.npy+.csv; seed + reproducibility command in JSONlegacy/Verification
pytest: 12/12 passed--fit-ah --fit-sigmarecovers (A_h\approx 64) kpc, (\hat{R}\approx 1)Try it