bayesian analysis

ofinterferometric

data(arXiv:1109.4640)

paul m. sutterbenjamin wandelt, siddarth malu

paris institute of astrophysicsuniversity of illinois at urbana-champaign

mock observations

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signal primary beam

uv-plane data

gibbs sampling

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extract variance

draw power spectrum realization

construct Wiener-

filtered map

add fluctuations consistent with noise

and spectrum

advantages

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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

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results - map

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posterior meansignal

results - map

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posterior mean

dirty map

results – marginalized posteriors

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other applications

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primary beamsignal

posterior mean

other applications

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future work

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multiple frequencies, polarization implemented

working on curved sky, foregrounds

developing point- and extended-source analysis

extending to 21 cm