the lamentations of radio astronomy

Malte Kuhlmann (MPI-IS), Rix, and I had a great, long meeting with MPIA radio astronomers Simon Bihr, Carl Ferkinhoff, and Laura Zschaechner, to discuss how radio astronomy works and what issues could benefit from some new technology coming from probabilistic inference or machine learning. We located a few points of great interest. One is the problem of flagging data affected by RFI, where it is a time-consuming task but nonetheless there is now a huge quantity of well flagged data to "learn" from. Great place for supervised classification. A second area is calibration of phase delays: The calibrator stars are used to get calibration estimates, which are interpolated to the science data. This interpolation is neither probabilistic nor uses calibration information latent in the science data nor knows about spatial nor time coherence. I am thinking Gaussian Processes, as my loyal reader can imagine. A third area is, of course, scene reconstruction, which is Kuhlmann's current project. Everyone recognizes the great value of CLEAN but also its limitations: It doesn't propagate noise, it contains many heuristically set parameters, and it doesn't optimize a well-specified scalar objective.

In other news, Rix, Ness and I figured out, with our data-driven model of APOGEE spectra, how to label new spectra, and how to identify the most relevant spectral regions to use as metallicity "indices". We outlined paper one and put the code and outline up on github (tm).

1 comment:

  1. Hogg -- for RFI cleaning, you should check out papers by (and talk to!) Andre Offringa, an ex-Kapteyner who now works for MWA. He's got a background in computer science, particularly in supervised classification and mathematical morphology. He wrote the LOFAR RFI cleaner and spearheaded the MWA imaging system.