bayesian analysis
ofinterferometric
data(arXiv:1109.4640)
paul m. sutterbenjamin wandelt, siddarth malu
paris institute of astrophysicsuniversity of illinois at urbana-champaign
mock observations
2
signal primary beam
uv-plane data
gibbs sampling
3
extract variance
draw power spectrum realization
construct Wiener-
filtered map
add fluctuations consistent with noise
and spectrum
advantages
4
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
5
results - map
6
posterior meansignal
results - map
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posterior mean
dirty map
results – marginalized posteriors
8
other applications
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primary beamsignal
posterior mean
other applications
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future work
11
multiple frequencies, polarization implemented
working on curved sky, foregrounds
developing point- and extended-source analysis
extending to 21 cm
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