1. Atacama Large Millimeter/submillimeter Array Expanded Very Large Array Robert C. Byrd Green Bank Telescope Very Long Baseline Array Whoever North American ALMA Science Center Imaging and Self-Calibration Hands-on CASA introduction

2. 2 Imaging in CASA CASA exposes imaging and deconvolution via the clean task S TARTING POINT : CALIBRATED MS (“ CORRECTED ” COLUMN, IF PRESENT ) Can be run interactively (using the viewer) or automatically I NTERACTIVE ALLOWS ON - THE - FLY CLEAN BOXING AND STOPPING Key decisions: o How to grid the data (image, cell size) o How to handle the frequency axis o What (if any) deconvolution to carry out o Selection and weighting of visibility data

3. 3 (Visibility) data selection Treatment of spectral axis Basic image (gridding) parameters Deconvolution (actual CLEANing) Weighting clean

4. 4 clean : Imaging image size T YPICALLY ~ PRIMARY BEAM AREA UNLESS IN SPECIAL CASE cell size S ET THIS TO PLACE ~4-5 PIXELS ACROSS YOUR PSF CORE Weighting (“uniform”, “robust”, “natural”) U SED TO ASSEMBLE VISIBILITIES INTO IMAGE, AFFECT PSF/ SENSITIVITY Optionally “taper” (smooth) the data to target resolution

5. 5 clean : Imaging Handling of spectral axis for cube: START, STOP, WIDTH OF PLANE o Define planes by channel o Define planes by velocity o Define planes by frequency Handling of spectral axis for image: o “multifrequency synthesis” accounts for u-v position vs. frequency o (Optional) Deconvolution components can have spectral index I. E., INTENSITY DEPENDENT ON FREQUENCY

6. 6 clean : Deconvolution Image reconstruction to account for imperfect u-v coverage Basic Procedure: o Identify brightest spot in image o Subtract a point source with some fraction of that intensity o Add a corresponding point source to a “model” image o Proceed until no signal left in image o Convolve model with “clean beam” and add to residuals

7. 7 Find brightest points in “dirty” image Deconvolution Illustrated

8. 8 Find brightest points in “dirty” image Create model image containing a fraction of those flux points Deconvolution Illustrated

9. 9 Find brightest points in dirty image Create model image containing a fraction of those flux points Subtract model from data, leaving a residual Deconvolution Illustrated

10. 10 Find brightest points in dirty image Create model image containing a fraction of those flux points Subtract model from data, leaving a residual Final product = residual + model (convolved with restoring Gaussian beam)

11. 11 residual (log scale) model convolved w/ restorimg beam (log scale) cleaned image (log scale)residual (linear scale)

12. 12 residual (log scale) model convolved w/ restoring beam (log scale) cleaned image (log scale)residual (linear scale) restrict where the algorithm can search for clean components, with a mask

13. 13 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 10 iterations

14. 14 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 20 iterations

15. 15 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 30 iterations

16. 16 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 40 iterations

17. 17 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 50 iterations

18. 18 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 60 iterations

19. 19 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 70 iterations

20. 20 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 80 iterations

21. 21 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 90 iterations

22. 22 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 100 iterations

23. 23 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 125 iterations

24. 24 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 150 iterations

25. 25 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 200 iterations

26. 26 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 300 iterations

27. 27 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 400 iterations

28. 28 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 500 iterations

29. 29 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 1000 iterations

30. 30 residual (log scale) model convolved w/ restoring beam cleaned image (log scale)residual (linear scale) 1500 iterations

31. 31 CLEANED IMAGE ( LOG SCALE ) DIRTY IMAGE ( LOG SCALE ) clean: Deconvolution

32. 32 clean : Deconvolution Key decisions: o Constraining where the signal can be (clean boxing) M ANUALLY USING THE VIEWER OR INPUT AS AN IMAGE OR REGION o Setting stopping threshold T YPICALLY A SMALL NUMBER TIMES THE RMS NOISE o Number of iterations allowed N OT USUALLY A GOOD CRITERIA TO STOP o Deconvolution algorithm B ALANCE OF M AJOR /M INOR CYCLES, ETC.

33. 33 residual image in viewer define a mask with R-click on shape type define the same mask for all channels or iterate through the channels with the tape deck and define separate masks Interactive clean

34. 34 perform N iterations and return – every time the residual is displayed is a major cycle continue until #cycles or threshold reached, or user stop Interactive clean

35. 35 clean restarts from existing files WILL FIRST RECOMPUTE RESIDUALS FROM MODEL The mask image, in particular, can be reused B E CAREFUL OF IMSIZE – MASK MUST MATCH IMAGE don’t hit ^C while imaging – this can do bad things to your MS clean : Notes

36. 36 Self-Calibration in CASA “Self-calibration” is just regular calibration With a model of your source, you can calibrate on your source Requires that your source is bright enough N EEDED TO GET SUFFICIENT S/N; GET SOME S/N BACK TIME AVERAGING. Can be iterated as model improves U SUALLY PHASE - ONLY SELFCAL FIRST, AMPLITUDE SELFCAL LATER ( IF AT ALL )

37. 37 Self-Calibration in CASA Image your source, deconvolve build a model, place model in MS clean Calibrate to match data to model gaincal Apply the new calibration applycal Re-image the better-calibrated data clean Re-image the better-calibrated data clean Phase Calibration Table Amplitude Calibration Table Phase Calibration Table Amplitude Calibration Table Measurement Set Now has associated model. Measurement Set Improved corrected column. Improved Image Initial Image

38. 38 Self-Calibration in practice Initial round of cleaning C AREFUL NOT TO OVERDO IT : T HE SELF CALIBRATION CAN “ LOCK IN ” ARTIFACTS Experiment with solution interval (solint) S/N USUALLY LIMITING CONCERN, TRY POL. COMBINATION ( GAINTYPE =‘T’) Inspect resulting solutions L OOK FOR SMOOTH TRENDS OF PHASE, AMP. WITH TIME May take multiple iterations M ODEL WILL SUCCESSIVELY IMPROVE, START WITH PHASE, THEN TRY AMPLITUDE

39. 39 Your Turn Follow the imaging CASA guide http://casaguides.nrao.edu/index.php?title=TWHydraBand7_Im_SS12 We have provided the full calibrated data set N O NEED TO USE THIS MORNING ’ S DATA, BUT YOU CAN IF YOU LIKE Try: o C ONTINUUM IMAGING o L INE IMAGING o S ELF - CALIBRATION AND RE - IMAGING o M OMENT MAP CREATION o I MAGING YOUR CALIBRATORS A SK IF YOU NEED HELP !