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Image Analysis

Jim Lovell
ATNF Synthesis Imaging Workshop September 2001


What Do You Want to Measure?
(What you want to do and how to do it.) Flux density of components Absolute positions Relative positions and motions Flux density variability Spectral index, rotation measure etc (image combination). Overlay with other wavelength images



Aips++ has excelent image analysis capabilities. Can do almost everything that Miriad, AIPS and Difmap can plus more. Paths of least resistance (i.e hassle):
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Personal Bias/Ignorance

ATCA data:
Calibrate in Miriad Imaging or model fitting in Difmap. If mosaicing or bandwidth smearing effects are important use Miriad. Calibration and fringe- fitting in AIPS Imaging/model fitting in Difmap. Wide- field imaging with IMAGR in AIPS.
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VLBI/SVLBI data:
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Detailed image analysis in Miriad or AIPS

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Errors
Errors given by fitting software should be treated with skepticism Generally assumed errors are stochastic No accounting for on- source errors etc Components are not necessarily independent. e.g. Usually a strong correlation between intensity and diameter. Extreme example is one (u,v) point:


Amp

(u,v) dist


Component Fluxes
1.
Discrete Components: Model Fitting Model fitting is suitable for relatively discrete, isolated features.


Usually not a unique solution, so choose the simplest possible model (fewest components, simplest shapes) Point source - > circular Gaussian - > elliptical Gaussian.




Model components tend to be too simple for more complex structures.




Component Fluxes cont.
Extended Sources
Reducing the dimensionality can help.


PKS 1333- 33

Killeen, Bicknell & Ekers 1986


Reducing the dimensionality
Fit to jet width vs distance


Width (arcsec)

RA (arcsec)


Component Fluxes cont.
Extended Sources
Reducing the dimensionality can help. Integrated intensity.
Sum the intensity within a given region Sum the clean components making up the region of interest.



Absolute Positions
Depends on the quality of calibration: Precision of the position of the phase- cal Separation of source from phase- cal (closer the better) Weather, phase stability Signal to noise


Relative positions and motions
Limited by signal- to- noise


Flux Density Variability
Between epochs: easy. Within epochs: difficult.
NOTE: Check your secondary cal isn't an Intra- Day Variable!


Imaging algorithms assume the source stays constant during the observation

1) 2) 3) 4)

Split data into N segments and image each one separately Measure S(t) of variable component(s) Subtract variable component from the visibility data. Image whole dataset



A similar procedure may be required before combining data from different arrays or array configs.




Image Combination
Often desirable to combine images to
Measure polarisation, Measure spectral index, Measure rotation measure, Look for differences, Compare with optical, X- ray etc.


When combining radio images, restore all images with the same beam first.


Polarisation
Alignment should not be a problem as any self- cal solutions from imaging I can be passed directly to Q and U. Polarised intensity:
I
P


I

2 Q

I

2 U

Linear polarisation position angle:
0.5 arctan I
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U

I



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Q


Low S/N, Misalignment
Beware of edge effects due to low S/N or image misalignment.
In spectral index mesurements you can end up with a fake gradient.
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S1 S2
A B A/B

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1
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2

A B A/B


Low S/N, Misalignment
Beware of edge effects due to low S/N or image misalignment.
Extreme rotation measures are possible
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RM

1 2 1 2 2
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2

A B A- B

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A B A- B


Image Overlays
Can be tricky if X- ray/optical/radio have different astrometric precision. Two approaches:
1) Accept the uncertainties 2) If there are multiple components in each image, look for an alignment with the best correlation.



Example: PKS 0637-752
Quasar, z=0.651 (Montage from Difmap image and overlays in Miriad) Space VLBI (VSOP)

ATCA 8.6 GHz (contours) Chandra (pixels)

ATCA/HST overlay


PKS 0637-752 cont.

ATCA Contou Pixels: Lines:

8.6 Ghz rs: total intensity fractional polarisation polarisation E- vectors

(Imaged in Difmap, polarisation and overlays in Miriad)


PKS 0637-752 cont.

(Slice along radio jet in AIPS)


PKS 0637-752 cont.
VLBI Component motion (separation vs time).

(Model fit to VSOP and ground- only VLBI data in Difmap)


Tasks, commands
Miriad task Visibility plane model fitting Image plane model fitting Image plane integrated flux Uvfit, uvmodel Difmap command Modelfit AIPS task Uvfit, (slime) Aips++ function image.fitsky, imagefitter (spatial), image.maxfit, image.fitprofile, imageprofiler image.statistics image.getchunk, image.getregion, image.putchunk, image.putregion image.modify image.various imagepol.rotationmeasure, image.fourierrotationmeasure image.calc image.calc image.calc image.regrid viewer/skycatalog

Maxfit, imfit Imstat

Imstat, "S" in mapplot

Maxfit, jmfit, imfit, sad Imean, imstat, tvstat, blsum

Slices

Ellint

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Slfit, xgaus Uvmod, uvsub Comb Comb Comb Comb, sumim Maths Lgeom, ohgeo, hgeom Kntr, pcntr, tvblink

Component or Setcont continuum subtraction Uvsub, imlin, uvlin, uvmodel Forming polarisation multi_model true; polvec; images mapl pcln Impol Rotation measure Spectral Index Other image combinations Maths operations on a single image Re- grid, transformations Overlays Imrm Maths Maths Maths Regrid Kview, Cgdisp -


Resources
Follow the links from the ATNF Software And Tools page: www.atnf.csiro.au/computing/software Aips++: see the Getting Results documentation for an overview of image analysis. Miriad: see Chapter 18 of the Users Guide Difmap: see the Difmap Cookbook AIPS: see chapter 7 of the AIPS Cookbook