Atacama Large Millimeter/submillimeter ArrayExpanded Very Large Array

Robert C. Byrd Green Bank TelescopeVery Long Baseline Array

WhoeverNorth American ALMA Science Center

Imaging and Self-CalibrationHands-on CASA introduction

2

Imaging in CASA

• CASA exposes imaging and deconvolution via the clean taskSTARTING POINT: CALIBRATED MS (“CORRECTED” COLUMN, IF PRESENT)

• Can be run interactively (using the viewer) or automaticallyINTERACTIVE 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 axiso What (if any) deconvolution to carry outo Selection and weighting of visibility data

3

• (Visibility) data selection

• Treatment of spectral axis

• Basic image (gridding) parameters

• Deconvolution (actual CLEANing)

• Weighting

clean

4

clean: Imaging

• image sizeTYPICALLY ~ PRIMARY BEAM AREA UNLESS IN SPECIAL CASE

• cell sizeSET THIS TO PLACE ~4-5 PIXELS ACROSS YOUR PSF CORE

• Weighting (“uniform”, “robust”, “natural”)USED TO ASSEMBLE VISIBILITIES INTO IMAGE, AFFECT PSF/SENSITIVITY

• Optionally “taper” (smooth) the data to target resolution

5

clean: Imaging • Handling of spectral axis for cube:

START, STOP, WIDTH OF PLANE

o Define planes by channelo Define planes by velocityo 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 indexI.E., INTENSITY DEPENDENT ON FREQUENCY

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

• Find brightest points in “dirty” image

Deconvolution Illustrated

8

• Find brightest points in “dirty” image• Create model image containing a fraction of those flux points

Deconvolution Illustrated

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

• 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

residual (log scale)

model convolved w/ restorimg beam (log scale)

cleaned image (log scale)residual (linear scale)

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

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 10

iterations

14

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 20

iterations

15

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 30

iterations

16

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 40

iterations

17

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 50

iterations

18

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 60

iterations

19

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 70

iterations

20

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 80

iterations

21

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 90

iterations

22

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 100

iterations

23

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 125

iterations

24

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 150

iterations

25

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 200

iterations

26

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 300

iterations

27

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 400

iterations

28

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 500

iterations

29

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 1000

iterations

30

residual (log scale)

model convolved w/ restoring beam

cleaned image (log scale)residual (linear scale) 1500

iterations

31

CLEANED IMAGE (LOG SCALE)

DIRTY IMAGE (LOG SCALE)

clean: Deconvolution

32

clean: Deconvolution• Key decisions:

o Constraining where the signal can be (clean boxing)MANUALLY USING THE VIEWER OR INPUT AS AN IMAGE OR REGION

o Setting stopping thresholdTYPICALLY A SMALL NUMBER TIMES THE RMS NOISE

o Number of iterations allowedNOT USUALLY A GOOD CRITERIA TO STOP

o Deconvolution algorithmBALANCE OF MAJOR/MINOR CYCLES, ETC.

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

• 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

• clean restarts from existing filesWILL FIRST RECOMPUTE RESIDUALS FROM MODEL

• The mask image, in particular, can be reusedBE CAREFUL OF IMSIZE – MASK MUST MATCH IMAGE

• don’t hit ^C while imaging – this can do bad things to your MS

clean: Notes

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 enoughNEEDED TO GET SUFFICIENT S/N; GET SOME S/N BACK TIME AVERAGING.

• Can be iterated as model improvesUSUALLY PHASE-ONLY SELFCAL FIRST, AMPLITUDE SELFCAL LATER (IF AT ALL)

37

Self-Calibration in CASAImage your source, deconvolve build a model, place model

in MSclean

Calibrate to match data to modelgaincal

Apply the new calibrationapplycal

Re-image the better-calibrated dataclean

Phase Calibration TableAmplitude Calibration

Table

Measurement SetNow has associated

model.

Measurement SetImproved corrected

column.

Improved Image

Initial Image

38

Self-Calibration in practice

• Initial round of cleaningCAREFUL NOT TO OVERDO IT: THE SELF CALIBRATION CAN “LOCK IN” ARTIFACTS

• Experiment with solution interval (solint)S/N USUALLY LIMITING CONCERN, TRY POL. COMBINATION (GAINTYPE=‘T’)

• Inspect resulting solutionsLOOK FOR SMOOTH TRENDS OF PHASE, AMP. WITH TIME

• May take multiple iterationsMODEL WILL SUCCESSIVELY IMPROVE, START WITH PHASE, THEN TRY AMPLITUDE

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 setNO NEED TO USE THIS MORNING’S DATA, BUT YOU CAN IF YOU LIKE

• Try:o CONTINUUM IMAGINGo LINE IMAGINGo SELF-CALIBRATION AND RE-IMAGINGo MOMENT MAP CREATIONo IMAGING YOUR CALIBRATORS

ASK IF YOU NEED HELP!