bayesian analysis of interferometric data (arXiv:1109.4640) Research Experience For Undergraduates, University of Rochester bayesian analysis of interferometric data (arXiv:1109.4640) 5 August 2004 paul m. sutter benjamin wandelt, siddarth malu paris institute of astrophysics university of illinois at urbana-champaign (c) University of Rochester

mock observations signal primary beam uv-plane data

gibbs sampling extract variance draw power spectrum realization construct Wiener-filtered map add fluctuations consistent with noise and spectrum

fast and scalable – O(np log np) joint analysis of spectrum and signal advantages fast and scalable – O(np log np) joint analysis of spectrum and signal full exploration of uncertainties automatically accounts for beam free Wiener-filtered maps trivial marginalization straightforward foreground removal

results – power spectrum

results - map posterior mean signal

results - map dirty map posterior mean

results – marginalized posteriors

other applications signal primary beam posterior mean

other applications

multiple frequencies, polarization implemented future work multiple frequencies, polarization implemented working on curved sky, foregrounds developing point- and extended-source analysis extending to 21 cm