Radio Array Model

A playground to explore the probabilistic and inference properties of radio interferometry arrays. The hope is to evolve into a system for computing likelihoods or posterior probabilities over scenes that explain a set of radio interferometry data.

Authors:

• David W. Hogg, New York University

License:

Copyright 2012 the author. All rights reserved.

If you have interest in using or re-using any of this content, get in touch with Hogg.

Contributors:

• Frank Bigiel, Universität Heidelberg
• Brendon Brewer, UC Santa Barbara
• Tom Herbst, Max-Planck-Institut für Astronomie
• Hans-Walter Rix, Max-Planck-Institut für Astronomie
• Fabian Walter, Max-Planck-Institut für Astronomie

Things to think about:

• You have a faint blob in your cleaned image. Is it significant? Surely this is a question that should or could be answered in the space of visibilities.

• Can we write down a good noise model for visibilities; that is, can we take things known about the antennae and correlator and predict the magnitudes of the residuals in the visibilities away from the best-fit "true scene" model (convolved with the dirty beam)?

• Related to the above, is it possible to project the scene inferred by CLEAN back into the visibilities and test the noise model?

• There are N radio telescopes and ~N^2 baselines; does this mean that you can infer the phase delays you get from your phase calibrator from the science data themselves? That is, can you self-calibrate always when N is large?

• Related to the above, shouldn't we be marginalizing over the phase calibration information, since (a) it is noisy and (b) it requires interpolation between calibration measurements?

• Bandwidth-smearing is something that needs to be a part of any real generative model. That is, we might be able to account for this in a proper model.

• Can we go "fully probilistic"? Is it possible to return a posterior PDF over scenes that could have created the data?

• Should we be looking for and using interesting and astronomy-informed prior information on the scene we infer?

• Would a generative model help usefully with RFI identifcation and elimination?

Related projects and bibliography:

• Peter Williams (Berkeley) is developing Python code to do analysis of interferometry data, with an emphasis on transient science; see arXiv:1203.0330
• John Monnier (Michigan) is working on sensible regularizers (which are like priors, of course) for optical interferometry problems.
• Sutton and Wandelt (2006) Optimal image reconstruction in radio interferometry is close to the whole enchilada; includes likelihood function and sophisticated priors. This is the project to outperform or out-code.
• Kemball et al (2010) Further evaluation of bootstrap resampling as a tool for radio-interferometric imaging fidelity assessment looks at bootstrap methods to put uncertainties on radio maps generated from interferometry.