Lessons learnt from implementing mosaicing and faceting in ASKAPsoft. Max Voronkov & Tim Cornwell ASKAP team 2nd April 2009

Transcription

1 Lessons learnt from implementing mosaicing and faceting in ASKAPsoft Max Voronkov & Tim Cornwell ASKAP team 2nd April 2009

2 Outline - Imaging software ASKAPsoft re-uses LOFAR design Imaging is treated as a least square problem ASKAP Software: Central Processor

3 Major and minor cycles One solution of the least square problem is one major cycle Major cycle involves iteration over data (building normal equations) Minor cycle solves normal equations for an update of the model Normal matrix is stored in a compressed form: only slice (PSF) and diagonal (Weights) are retained. It implies a shift-invariant PSF Many prediffers and solvers are possible (for parallelisation) ASKAP Software: Central Processor

4 Structure of the prediffer Measurement equation defines what is taken into account Imaging measurement equation uses gridders Component machine is for simple models Gridder determines the type of convolution functions Specialised gridders for mosaicing (CF from antenna illumination) ASKAP Software: Central Processor

5 Linear mosaicing Easy to understand if the only difference between primary beams P k is the pointing direction cycled through a number of fields and all antennas are the same Joint deconvolution Independent deconvolution (approximation) Image credit: Ilana Feain

6 Linear mosaicing - inhomogeneous case The most difficult inhomogeneity is rotation/translation of the beams We use 3-axis mount (i.e. antenna rotator) to get rid of it If done in software, it is easier to use visibility domain

7 Linear mosaicing - visibility domain Visibility domain Effects like grid-correction and weighting were omitted for simplicity See Rau et al., Journal of IEEE, in press for detailed derivation Joint deconvolution is a preferred option for inhomogeneous case

8 Linear mosaicing - PSF Response to point source No longer shift-invariant We can not just replace the visibilities with (1,0) to get PSF Phase tracking is done for each field separately! Have to simulate a point source Available options Use the same convolution functions as for gridding of visibility data? Normalise by weight image? All data vs. a representative field?

9 Use the same CFs as for visibility data? If yes, the sensitivity pattern reflects the actual pointings This approach may be good if the simulated source is in the middle of the sensitivity pattern We can use a separate set of convolution functions with different offsets applied

10 Normalise by weight image? Ideally yes, but we would need to take care of adjacent pointings to get a relatively flat image weight in the region of interest

11 All data vs. a representative field It is not an issue for parallel mosaicing (e.g. for ASKAP) Don t want over-subtraction at the minor cycle Ideally we want to use a few adjacent fields to get PSF Weighting can emphasize the sidelobes All data PSF for weighting?

12 Joint vs. independent deconvolution Individual I k can be deconvolved separately This is an approximation Not very good for the inhomogeneous case But easy to parallelise And can be less sensitive to beam errors Joint deconvolution always produces beam-corrected image Need signal-to-noise based clean

13 Faceted imaging This is one way to solve noncoplanarity problem W-projection is another (better) way to solve the same problem. It works in the visibility domain An image plane algorithm was still worth implementing because it can simplify facet-based calibration Easy to parallelise Well known trick with facets: All facets should have the same projection during deconvolution uvw-rotation to a common tangent point makes overlap easy Tangent point is no longer the image centre

14 Problems - sources outside facets Source outside FOV comes as noise Small facets cause problems Major cycle doesn t help much Simulated point source at various offsets from the image centre Plotted peak flux in the image It doesn t go to zero when the source is moved outside the image (facet) boundaries

15 Problems - facets and large FOV One has to image a large FOV for each facet Ideally full primary beam should be imaged Partial overlap FFT-padding Hard to combine with mosaicing if the joint deconvolution approach is used The weight may be high for the whole facet

16 Conclusions 1. Inhomogeneous case is easier to handle in the visibility domain 2. There are several possible approximations to PSF for mosaicing All data vs. a representative field (or a few fields) With and without normalisation by weight 3. Individual deconvolution can be less sensitive to beam errors 4. Clean based on the signal-to-noise ratio is essential for joint deconvolution approach 5. Faceted imaging requires large facets to get high dynamic range Memory requirements make it less attractive Difficult to combine with mosaicing (and implementation of w- projection is more natural for mosaicing) ASKAP Software: Central Processor

17 Australia Telescope National Facility Max Voronkov Software Scientist (ASKAP) Phone: +61 (2) Web: Thank you ASKAP Software: Central Processor Contact Us Phone: or enquiries@csiro.au Web: