Once the list of detected objects has been obtained, there are several post-processing steps that can be utilised. We describe here mask optimisation, two-dimensional component fitting, and spectral index & curvature measurement.

The WALLABY team have provided an algorithm to optimise the source mask (equivalent to the Duchamp concept of growing detections), such that the integrated flux of the detection is maximised subject to the noise present. This has been provided to avoid issues with the fact that the Duchamp-based parameterisation makes use only of the detected pixels, so structure below the detection threshold is not taken into account. Growing the object in this way increases the number of faint pixels contributing to source parameterisation.

Unlike the Duchamp-derived “growing” algorithm
(**Selavy.flagGrowth=true**), the growing process does not use a flux
or signal-to-noise threshold. Rather, it adds elliptical annuli to the
source until the total flux of the *annulus* is negative or a
specified maximum number of iterations is reached. It does this for
all channels within ±W50 of the central spectral channel.

The algorithm is implemented in the following way:

- Source detection is performed using the usual Duchamp/Selavy approaches. Note that the growing option can be used prior to the mask optimisation.
- First step for a given object is to define the spectral range over which the optimisation is done. This is taken to be W50 either side of the centre of the detection (in turn, defined by the Duchamp parameter pixelCentre). All channels within this range are treated equally.
- An ellipse is then fitted to the moment-0 map of the object. The moment-0 map uses the detected voxels only, so is dependent on the source-detection done prior to the mask optimisation.
- If
**clobberPrevious=true**, the source is pared back to the single pixel at the centre, which is used as the seed for the optimisation. Otherwise, we keep the existing mask and build on it. - The mask is then grown:

- For each spatial pixel in the current mask, all neighbouring pixels not already in the mask that lie within the ellipse are included in a new object. This is done for every channel over the spectral range
- If the flux of this object is positive, each of its pixels are added to the current mask,
- The size of the ellipse is increased by 1 pixel in the major axis direction, and enough in the minor axis direction to preserve the shape.

- This continues as long as the flux of the new “object” is positive,
and the number of growing iterations is less than
**maxIter**. - The parameterisation of the object is redone.
- Once all objects have been done, the Duchamp merging process is re-run to cleanup any pairs of objects that may have intersected.

Note that the flux etc that is calculated for the object after the
mask optimisation replaces the value that would have been calculated
before it. At this point, if you want to know, say, the integrated
flux of an object with and without this mask optimisation, you will
need to run the algorithm twice. I am looking at providing this as an
*alternative* flux measurement - at that point the code may be put
into the Duchamp library proper, rather than just in Selavy.

The result of the mask optimisation does depend somewhat on what is
done in the source-detection prior to its use. Using the growing
method of Duchamp (**flagGrowth=true** and **growthCut** or
**growthThreshold**) allows a good initial estimate of both the
spectral range and the ellipse fitted to the moment-0 map. The
optimised mask still tends to be larger than that from Duchamp-growing
alone (at least from the limited testing done so far).

To aid evaluation of this algorithm, the mask FITS output of Duchamp
has been enabled in Selavy, although *only for the serial case*. If
running a multiple-node job, the mask will not be written. When
written, the mask will be called *selavy-MASK-IMG.fits*, where the IMG
refers to the input image.

Parameter |
Type |
Default |
Explanation |
---|---|---|---|

optimiseMask | bool | false | Whether to use the mask optimisation algorithm |

optimiseMask.maxIter | int | 10 | The maximum number of iterations to do. The growing process stops if this is reached, or if the flux of the annulus is negative |

optimiseMask.clobberPrevious | bool | true | If true, the algorithm starts with only the central pixel and grows out from there, after fitting an ellipse to the input detection. If false, the algorithm starts with the input mask already determined by the Duchamp algorithms. |

For continuum images, we have implemented the ability to fit 2D
Gaussian components to detected sources. Note that no such facility
yet exists for 3D cubes. For the purposes of this section, we adopt
the terminology used elsewhere of describing the detected objects
(that is, collections of pixels above the detection threshold) as
‘islands’ which have ‘components’ fitted to them. For Selavy, the
islands are the result of running the detection algorithms, and it is
the islands that are written to the catalogue given by
**Selavy.outFile** (defaulting to *selavy-results.txt*).

The fitting itself is done by the *fitGaussian* function from the
*casacore* package. This returns a set of parameters for each
Gaussian: peak flux, x-pixel centre, y-pixel centre, major axis FWHM,
axial ratio, position angle.

The pixels that are used in the fit are chosen in one of two ways. If
**Selavy.Fitter.fitJustDetection=true**, then only the island pixels
(ie. those that were detected) are used in the fit - no “background”
pixels are used. This avoid confusion with possible neighbouring
sources.

Alternatively, if **Selavy.Fitter.fitJustDetection=false**, the
fitting is done using all pixels in a box surrounding the island. This
box is defined by padding a border of a minimum number of pixels
(defined by the **boxPadSize** parameter) in all directions around the
island pixels (moving out from the extremities of the detected
pixels). Only the pixels lying within this box are used in the
fit. This approach is more consistent with the approach used in the
FIRST survey.

Note that all parameters governing the fitting require a
**Selavy.Fitter.** prefix, but for convenience this is left out in the
text that follows.

The noise level (ie. the standard deviation of the noise background)
is used by the fitting function to weight the fitting function and
obtain the chi-squared value. This noise level is not that used by the
detection algorithm, but rather one obtained by finding the median
absolute deviation from the median (MADFM) in a square box centred on
the island’s peak pixel. The side length of the box is governed by the
**noiseBoxSize** parameter, which defaults to 101 pixels. Note that it
is different to the box mentioned above. The MADFM is converted to an
equivalent rms for a Gaussian noise distribution (by dividing by
0.674888).

It is possible to do the fit without calculating the noise level, by
seeting **useNoise = false**. This sets the pixel sigma value to 1 for
each pixel, effectively removing the noise from the chi-squared
calculation.

The *fitGaussian* function requires an initial estimate of the
parameters. There are two ways Selavy obtains this initial
estimate. The first way (the default) uses the following recursive
algorithm to provide a list of sub-components (being distinct peaks
within an island):

- Using the island pixels, define a set of parameters: peak flux and location are obvious, while the major & minor axes & position angle are obtained from Duchamp algorithms, using a separate detection process at half the peak flux (to get the full width at half maximum).
- Define a set of sub-thresholds (the number of these is given by the
**numSubThresholds**parameter) spaced either linearly or logarithmically between the peak flux and the detection threshold (the separation between sub-thresholds is constant in either log or linear space depending on whether the input parameter**logarithmicThresholds**is true or false). - For each threshold, search the box surrounding the object, and record the number of separate detections.

- If there is more than one separate object, call the getSubComponentList function on each of these and add the result to the vector list.

- When you reach the final threshold, add the initial set of parameters to the vector list and return the vector list.

The second approach attempts to replicate the algorithm described in Hancock et al. (2012), MNRAS 422, 1812 (which is used in Paul’s ‘Aegean’ source-finder). This creates a curvature map using a discrete 2D Laplacian kernel. The curvature map, in combination with the mask of detected pixels, is used to locate local maxima within the island. The image is convolved with the 3x3 kernel - note that this means the single row of pixels around the edge will not have a curvature value assigned.

A point source is placed at each local maxima to provide the initial
estimate of components. To use this mode, set
**useCurvature=true**. The curvature map can be saved to a CASA image
by setting the **curvatureImage** parameter.

If the Gaussian fitting fails to provide a good fit, these initial
estimates can be returned as the results, with a flag indicating they
are only estimates. Whether this is done is governed by the parameter
**useGuessIfBad**, which defaults to **true**. If an estimate is
reported in the results output, the final column *Guess?* will take
the value 1, else it will be 0.

If **numGaussFromGuess=true** (the default), the number of Gaussians
that are fitted to the island is exactly equal to the number of
subcomponents in the initial estimate. If, however,
**numGaussFromGuess=false**, then the number of Gaussians fitted is
between 1 and **maxNumGauss**. The subcomponents are chosen as initial
estimates in order of their peak flux. If there are more Gaussians
needed than there are subcomponents, we simply cycle through the list.

The fitting is done by casacore’s *fitGaussian* function. The fit is
repeated a further two times, each time using the output of the
previous fit as the initial guess. This results in a slight refinement
of the fit, usually (but not always!) improving the chi-squared value.

The parameters that are fitted to the data are defined by the
**fitType** parameter. This can take one of the following values:

*full*: All six parameters of the Gaussian are free to be fitted to the data.*psf*: Only the position and height of the Gaussian are fitted. The size & shape are fixed to match the beam size, taken from the image header (or the beamSize parameter if the image header does not have the beam information).*shape*: Only the position and shape of the Gaussian are fitted. The height is fixed to match the peak pixel flux of the object.*height*: Only the height of the Gaussian is fitted, with the location and shape kept constant.

All types can be given in vector format to the **fitType**
parameter. In this case, all listed types of fits are done, and the
best result (judged by the reduced chi-squared value) is chosen as the
best fit. This means that if the best fit for the “full” case is a
beam-sized Gaussian, the fit from the “psf” case will be chosen as it
has more degrees of freedom and so a lower reduced chi-squared.

When **numGaussFromGuess=true** and the fit converges but is poor, we
test to see if an additional, confused component is present (that was
not picked up by the original initial estimate). The fitted Gaussian
is subtracted from the pixels, then the initial estimation algorithm
is re-run. The brightest component found is added to the initial
estimates provided to the fitting routine, and the fit is re-run with
one extra Gaussian. This process continues until the fit does not
converge.

The fit is accepted according to a list of criteria, that follow those used in the FIRST survey (Becker, White & Helfand 1995). These are:

- The fit must have converged.
- The chi-squared value is examined in one of two ways. The second
method is used provided the
**chisqConfidence**parameter is between 0 and 1. Otherwise (the default case), the first method is used.

- The reduced chi-squared is compared to the
maxReducedChisqparameter, and accepted if smaller. (Here we definerchisq = chisq / (npix - numGauss*nfree - 1), wherenfreeis the number of free parameters :See below for discussion)- The chi-squared value and the number of degrees of freedom are used to calculate the probability of a chi-squared-distributed parameter having the given value or less, and compared to the
chisqConfidencelevel. For numbers of degrees of freedom greater than 343, computational requirements mean this is approximated by requiring the reduced chi-squared to be less than 1.2.

- The centre of each component must be inside the box
- The separation between any pair of components must be more than 2 pixels
- The flux of each component must be positive and more than half the detection threshold
- No component’s peak flux can exceed twice the highest pixel in the box.
- The sum of the integrated fluxes of all components must not be more than twice the total flux in the box.

The results of each of these tests is printed to the log as a 1 (pass) or a 0 (fail).

If **numGaussFromGuess=true**, then we only use the same number of
Gaussians as the number of components in the initial estimate. If this
is **false**, the the behaviour is governed by the
**stopAfterFirstGoodFit** parameter. If
**stopAfterFirstGoodFit=true**, once the first acceptable fit is found
(starting with a single Gaussian), the fitting is stopped. Multiple
Gaussians are fitted only if fewer Gaussians do not give an acceptable
fit. If **stopAfterFirstGoodFit=false** then the fitting using one
through to the maximum number of Gaussians, and the best fit is chosen
to be the one that passes all the above criteria and has the lowest
reduced chi-squared value.

Given the above, however, Selavy will react to fits that are unacceptable in the following way:

- If the fit fails to converge, and there was more than one Gaussian being fitted, then Selavy will try again with one fewer Gaussian (unless it has already tried that number).
- If the fit converges, but has a high chi-squared value, Selavy will remove the fitted components and search for any further components that may have been missed with the initial search. The brightest of any found is added to the list of initial estimates, and the fit is re-done. This process can recover additional components that do not stand out as separate peaks when all components are present.

The expression used to calculate the reduced chi-squared as shown above is fine if the pixels are independent. However, this is not the case for radio data, where neighbouring pixels are correlated due to the finite beam size. It is not immediately obvious what the correct way to estimate the reduced chi-squared is. It may be that, formally, a different metric should be used in assessing the goodness-of-fit (since an underlying assumption of the chi-squared test is that the pixels are independent).

Note that, leaving aside the formal requirements of the statistical test, this is primarily a problem when comparing different successful fits that have different numbers of Gaussians. The determination of the best fit for a given number of Gaussians should not be affected (although the second of our acceptance criteria might have to change).

Several files are produced to show the results of the Gaussian
fitting. The first is a CASDA-compliant components catalogue. In
pipeline operation, this would be the catalogue sent to CASDA, the
CSIRO ASKAP Science Data Archive. This takes its name
from the **Selavy.resultsFile** parameter, replacing the *.txt*
extension with *.components.txt*. An XML/VOTable version is also
produced (always), with a *.xml* extension. An example of the text
version of this catalogue is shown here:

```
# island_id component_id component_name ra_hms_cont dec_dms_cont ra_deg_cont dec_deg_cont ra_err dec_err freq flux_peak flux_peak_err flux_int flux_int_err maj_axis min_axis pos_ang maj_axis_err min_axis_err pos_ang_err maj_axis_deconv min_axis_deconv pos_ang_deconv maj_axis_deconv_err min_axis_deconv_err pos_ang_deconv_err chi_squared_fit rms_fit_gauss spectral_index spectral_curvature spectral_index_err spectral_curvature_err rms_image has_siblings fit_is_estimate spectral_index_from_TT flag_c4 comment
# -- -- [deg] [deg] [arcsec] [arcsec] [MHz] [mJy/beam] [mJy/beam] [mJy] [mJy] [arcsec] [arcsec] [deg] [arcsec] [arcsec] [deg] [arcsec] [arcsec] [deg] [arcsec] [arcsec] [deg] -- [mJy/beam] -- -- -- -- [mJy/beam]
SB2338_island_1 SB2338_component_1a J221655-452149 22:16:55.2 -45:21:49 334.230130 -45.363683 0.09 0.07 1400.5 1877.111 12.318 2165.969 20.689 33.45 23.95 74.68 0.22 0.08 0.79 15.08 4.60 -87.62 1.00 13.76 1.13 686.286 4782.906 0.00 0.00 0.00 0.00 2.916 0 0 1 1
SB2338_island_2 SB2338_component_2a J221021-454251 22:10:21.5 -45:42:51 332.589424 -45.714267 0.32 0.36 1400.5 811.963 17.958 1391.736 39.933 43.87 27.13 36.09 0.98 0.23 1.80 33.63 9.24 26.57 0.13 2.90 1.88 4966.390 11142.699 0.00 0.00 0.00 0.00 2.262 0 0 1 1
SB2338_island_3 SB2338_component_3a J221105-433313 22:11:05.3 -43:33:13 332.772286 -43.553874 0.07 0.05 1400.5 884.571 4.219 928.795 6.669 32.49 22.44 70.75 0.16 0.06 0.51 12.04 0.00 81.97 0.80 0.00 0.74 209.765 2787.307 -0.00 0.00 0.00 0.00 1.631 0 0 1 0
SB2338_island_4 SB2338_component_4a J222254-452730 22:22:54.3 -45:27:30 335.726258 -45.458383 0.14 0.11 1400.5 494.099 4.639 693.789 8.906 36.76 26.53 75.50 0.35 0.12 1.14 21.37 12.40 88.23 0.28 1.15 1.05 439.707 3766.180 0.00 0.00 0.00 0.00 1.515 0 0 1 1
SB2338_island_5 SB2338_component_5a J221416-425710 22:14:16.8 -42:57:10 333.569959 -42.953043 0.05 0.04 1400.5 538.313 1.863 622.263 3.127 34.77 23.09 75.13 0.12 0.04 0.34 17.65 0.00 86.46 0.12 0.00 0.35 174.815 2374.698 -0.00 0.00 0.00 0.00 0.900 0 0 1 0
SB2338_island_6 SB2338_component_6a J215840-471934 21:58:40.9 -47:19:34 329.670361 -47.326273 0.07 0.07 1400.5 374.492 2.079 471.220 3.714 32.58 26.81 49.71 0.18 0.08 1.15 16.56 7.90 5.86 0.22 1.33 0.56 202.162 2220.534 0.00 0.00 0.00 0.00 1.027 1 0 1 0
SB2338_island_6 SB2338_component_6b J215834-471931 21:58:34.5 -47:19:31 329.643940 -47.325282 1.63 1.30 1400.5 19.343 2.557 21.011 4.164 31.75 23.75 88.85 4.14 1.94 18.90 15.15 0.00 -61.47 3.21 0.00 7.16 202.162 2220.534 0.00 0.00 0.00 0.00 1.027 1 0 1 0
SB2338_island_7 SB2338_component_7a J221804-461322 22:18:04.8 -46:13:22 334.519855 -46.222850 0.23 0.15 1400.5 281.466 3.426 470.890 7.475 46.59 24.93 69.10 0.58 0.11 0.74 35.39 9.79 70.19 0.04 1.27 0.94 283.404 3073.564 -0.00 -0.00 0.00 0.00 1.492 0 0 1 0
```

The columns are:

*island_ID*and*component_ID*are the unique identifiers of the component, and the island from which it comes. These are of the form “SB<SBID>_island_<NUM>” and “SB<SBID>_component_<NUM><CMP>”, where <SBID> is the numerical scheduling block ID provided by`Selavy.sbid`, <NUM> is the island numerical identifier, and <CMP> is one or more characters indicating the order of components for that island. These will be a-z for the first 26, then aa-zz, then aaa-zzz and so forth. If no scheduling block ID is provided, the “SB<SBID>_” prefix will be omitted.- The position is indicated by the
*component_name*(J2000 IAU format), as well as HMS/DMS-format strings and decimal degree values for both RA and DEC. Errors in the position will also be given in time (not yet implemented fully). *freq*shows the frequency of the image.- The peak and integrated fluxes are shown, with their errors.
- The size and orientation of the fitted Gaussian is shown by the
major and minor axes (FWHM) and the position angle of the major
axis. This is shown for the fitted values (
*maj_axis*et al), their errors (*maj_axis_err*et al), and their deconvolved values given the image’s restoring beam (*maj_axis_deconv*et al) along with errors. - The quality of the fit is shown by
*chi_squared_fit*and*rms_fit_gauss*. - The fitted spectral index and spectral curvature are shown when calculated. This is only done when the appropriate flags are set - see Spectral Terms. Errors are given for these as well.
*rms_image*shows the measurement of the local noise.- Several flags are reported:
*has_siblings*is true if the component is one of many fitted to the same island*fit_is_estimate*is true if the fit failed for some reason - the reported values for the component parameters come from the initial estimate*spectral_index_from_TT*shows that the spectral index & curvature were calculated from Taylor-term images (if*true*) or the continuum cube (if*false*).- The as-yet unnamed
*flag_c4*indicates that the fitted component is formally bad - it doesn’t meet the chi-squared criterion, but is the best fit possible.

Along with the components catalogue, a matching Karma/CASA/DS9
annotation (region) file will be produced showing the location & size
of the components (each Gaussian component is indicated by an ellipse
given by the major & minor axes and position angle of the
component). These are named in the same way as the catalogue file,
but with a .ann/.crf/.reg extension respectively. Whether these are
produced is governed by the flagKarma/flagCasa/flagDS9 parameters (see
*Selavy Basics* for details).

By setting **Fitter.writeComponentMap=true** (the default), an image
is made showing just the fitted Gaussian components. This is the
“component map”. At the same time, a residual map (input image with
the component map subtracted) is created. These default to being FITS
files, unless **Fitter.imagetype=casa** is given. If *imagename* is
the input image given to Selavy, the name of these images will be
componentMap_*imagename*.fits and componentResidual_*imagename*.fits
(with no ”.fits” extension for casa images),

A similar output file is the fit Results catalogue. This is only
produced when **writeFitResults=true**. This shows the fit results
with a different emphasis (this is the original method of showing the
fit results in Selavy). This too takes its name from the
**Selavy.resultsFile** parameter, replacing the *.txt* extension with
*.fitResults.txt*. An example start for the file is as follows below.

```
# ID Name RA DEC X Y F_int F_peak F_int(fit) F_pk(fit) Maj(fit) Min(fit) PA(fit) maj_axis_err min_axis_err pos_ang_err Maj(fit_deconv) Min(fit_deconv) PA(fit_deconv) maj_axis_deconv_err min_axis_deconv_err pos_ang_deconv_err Alpha Beta spectral_index_err spectral_curvature_err Chisq(fit) RMS(image) RMS(fit) Nfree(fit) NDoF(fit) NPix(fit) NPix(obj) fit_is_estimate
# -- -- [deg] [deg] [pix] [pix] [mJy] [mJy/beam] [mJy] [mJy/beam] [arcsec] [arcsec] [deg] [arcsec] [arcsec] [deg] [arcsec] [arcsec] [deg] [arcsec] [arcsec] [deg] -- -- -- -- -- [mJy/beam] [mJy/beam] -- -- -- --
1a J221655-452149 334.230130 -45.363683 1454.46 1721.87 391.352 1726.833 2165.969 1877.111 33.45 23.95 74.68 0.22 0.08 0.79 15.08 4.60 -87.62 1.00 13.76 1.13 0.00 0.00 0.00 0.00 686.286 2.916 4782.906 6 23 30 30 0
2a J221021-454251 332.589424 -45.714267 1800.17 1619.95 246.408 766.953 1391.736 811.963 43.87 27.13 36.09 0.98 0.23 1.80 33.63 9.24 26.57 0.13 2.90 1.88 0.00 0.00 0.00 0.00 4966.390 2.262 11142.699 6 33 40 40 0
3a J221105-433313 332.772286 -43.553874 1760.88 2268.01 168.103 880.182 928.795 884.571 32.49 22.44 70.75 0.16 0.06 0.51 12.04 0.00 81.97 0.80 0.00 0.74 -0.00 0.00 0.00 0.00 209.765 1.631 2787.307 6 20 27 27 0
4a J222254-452730 335.726258 -45.458383 1140.46 1684.45 123.720 426.248 693.789 494.099 36.76 26.53 75.50 0.35 0.12 1.14 21.37 12.40 88.23 0.28 1.15 1.05 0.00 0.00 0.00 0.00 439.707 1.515 3766.180 6 24 31 31 0
5a J221416-425710 333.569959 -42.953043 1585.47 2447.06 112.662 493.966 622.263 538.313 34.77 23.09 75.13 0.12 0.04 0.34 17.65 0.00 86.46 0.12 0.00 0.35 -0.00 0.00 0.00 0.00 174.815 0.900 2374.698 6 24 31 31 0
6a J215840-471934 329.670361 -47.326273 2393.13 1125.49 87.930 329.294 471.220 374.492 32.58 26.81 49.71 0.18 0.08 1.15 16.56 7.90 5.86 0.22 1.33 0.56 0.00 0.00 0.00 0.00 202.162 1.027 2220.534 6 28 41 41 0
6b J215834-471931 329.643940 -47.325282 2398.50 1125.59 87.930 329.294 21.011 19.343 31.75 23.75 88.85 4.14 1.94 18.90 15.15 0.00 -61.47 3.21 0.00 7.16 0.00 0.00 0.00 0.00 202.162 1.027 2220.534 6 28 41 41 0
7a J221804-461322 334.519855 -46.222850 1399.46 1462.93 81.990 261.075 470.890 281.466 46.59 24.93 69.10 0.58 0.11 0.74 35.39 9.79 70.19 0.04 1.27 0.94 -0.00 -0.00 0.00 0.00 283.404 1.492 3073.564 6 23 30 30 0
```

The columns provided are:

*ID*is a unique ID for the component. It comprises the ID number of the island, plus one or more characters indicating the order of components for that island. These will be a-z for the first 26, then aa-zz, then aaa-zzz and so forth.*Name*is the name taken from the island.*RA*,*Dec*,*X*and*Y*are the world and pixel locations of the*component*.*F_int*and*F_peak*are values for the island as calculated by the Duchamp code, and reported in the Duchamp results file given by**Selavy.outFile**. These are the same for each comonent of that island.*F_int(fit)*and*F_pk(fit)*are the integrated & peak fluxes from the fitted Gaussians.- Alpha and Beta are the spectral index and spectral curvature terms (with associated errors). These are only provided when the appropriate flags are set - see Spectral Terms.
*Maj*,*Min*and*P.A.*are the major and minor FWHMs and the position angle of the fitted Gaussian, quoted for both the fit and the fit deconvolved by the beam, with errors for each version.- The goodness of fit is indicated by the
*Chisq(fit)*and*RMS(fit)*values, while*RMS(image)*gives the local noise surrounding the object. *Nfree(fit)*is the number of free parameters in the fit, and*NDoF(fit)*is the number of degrees of freedom.*Npix(fit)*is the number of pixels used in doing the fit, and*Npix(obj)*is the number of pixels in the object itself (ie. detected pixels).- A value of 1 in the
*fit_is_estimate*column indicates that the “fitted” parameters come from the initial estimate (the fitting procedure failed for some reason).

If no fit was made (see components 1a and 1b in the example above), the Gaussian parameters are taken from the initial estimate, while those parameters relating to the quality of the fit are set to zero (for RMS, Nfree etc) or 999 (chisq).

A VOTable version of the fit results is also produced, with a *.xml*
suffix. This is always produced whenever the fit results file is
produced.

As well as the “best” fit catalogue, a catalogue of fit results is
written for each fitType considered (out of ‘full’,’psf’,’shape’ and
‘height’). These will be named, for instance,
*selavy-results.fitResults.full.txt*.

Two types of annotation files will also be produced:

- Fitted components - a Karma/CASA/DS9 annotation (region) file showing
the fitting results (each Gaussian component is indicated by an
ellipse given by the major & minor axes and position angle of the
component). These are named in the same way as the fit results file,
but with a .ann/.crf/.reg extension respectively. Whether these are
produced is governed by the flagKarma/flagCasa/flagDS9 parameters
(see
*Selavy Basics*for details). **fitBoxAnnotationFile**[selavy-fitResults.boxes.ann] - an annotation file showing the boxes used for the Gaussian fitting (if boxes were not used, ie.**fitJustDetection=true**, this file is not created).

The user can request that a component parset be created,
showing the fitted components. Such a file could be used in tasks such
as *ccalibrator*, for self-calibration, or
*csimulator*. This file is created by providing a
filename with the **Selavy.outputComponentParset** parameter. By
default, all components are written, but you can specify a maximum
number to be included using the
**Selavy.outputComponentParset.maxNumComponents** parameter (the list
is ordered by flux, so that the brightest ones are written first). The
shape of the components is included by default, but you can force them
to be point sources by setting
**Selavy.outputComponentParset.reportSize=false**. See
*csimulator* for details on how components should be
specified.

Additionally, images showing the fitted Gaussians and the residual map
after subtracting these Gaussians from the input image are always
created when *doFit=true*. These will be named to match the input
image: the map of fitted Gaussians will have **componentMap_** prepended
to the image name, and the residual map will have **componentResidual_**
prepended.

Parameter |
Type |
Default |
Description |
---|---|---|---|

Basic control parameters |
|||

Selavy.distribFit | bool | true | If true, the edge sources are distributed by the master node to the workers for fitting. If false, the master node does all the fitting. |

Selavy.Fitter.doFit | bool | false | Whether to fit Gaussian components to the detections |

Selavy.Fitter.fitJustDetection | bool | true | Whether to use just the detected pixels in finding the fit. If false, a rectangular box is used. |

Selavy.Fitter.fitTypes | vector<string> | [full,psf] | A vector of labels for the types of fit to be done. The input format needs to be a
comma-separated list enclosed by square brackets (as in the default). The possible
options are ‘full’, ‘psf’, ‘shape’, or ‘height’. See text above for details. |

Selavy.Fitter.maxNumGauss | int | 4 | The maximum number of Gaussians to fit to a single detection. Ignored if
numGaussFromGuess=true. |

Selavy.Fitter.boxPadSize | int | 3 | When fitJustDetection=false, a border of at least this size is added around the
detection to create a rectangular box in which the fitting is done. |

Selavy.Fitter.stopAfterFirstGoodFit | bool | true | Whether to stop the fitting when an acceptable fit is found, without considering fits
with more Gaussian components. Ignored if numGaussFromGuess=true. |

Initial estimates |
|||

Selavy.Fitter.numGaussFromGuess | bool | true | Whether the number of Gaussians fitted should be the same as the number of components in
the initial estimate (the “guess”). If false, the maximum number is taken from
maxNumGauss. |

Selavy.Fitter.numSubThresholds | int | 20 | The number of levels between the detection threshold and the peak that is used to search for subcomponents. |

Selavy.Fitter.logarithmicThresholds | bool | true | Whether the sub-thresholds should be evenly spaced in log-space (true) or linear-space (false) |

Selavy.Fitter.useCurvature | bool | false | Whether to find the initial component estimates from the curvature map instead of the sub-thresholding. |

Selavy.Fitter.curvatureImage | string | <No default> | The name of the CASA image in which to write the curvature map. It will be made the same size as the input image. |

Selavy.Fitter.useGuessIfBad | bool | true | Whether to print the initial estimates in the case that the fitting fails |

Quality control parameters |
|||

Selavy.Fitter.maxReducedChisq | float | 5.0 | The maximum value for the reduced chi-squared for a fit to be acceptable. |

Selavy.Fitter.chisqConfidence | float | -1.0 | A probability value, between 0 and 1, used as a confidence level for accepting the
chi-squared value. If outside this range of values (as is the default), the test is done
with the reduced chi-squared value, using the maxReducedChisq parameter. |

Selavy.Fitter.maxRMS | float | 1.0 | The value that is passed to the FitGaussian::fit() function. |

Selavy.Fitter.useNoise | bool | true | Whether to measure the noise in a box surrounding the island and use that as the sigma value for each point in the fit. Setting to false has the effect of setting the sigma to one for each point. |

Selavy.Fitter.noiseBoxSize | int | 101 | The side length of a box centred on the peak pixel that is used to estimate the noise level (ie. the rms) for a source: this is used for the fitting. |

Selavy.Fitter.minFitSize | int | 3 | The minimum number of detected pixels that an island has for it to be fit. |

Selavy.Fitter.maxIter | int | 1024 | The maximum number of iterations in the fit. |

Selavy.Fitter.maxRetries | int | 0 | The maximum number of retries used by the fitting routine (ie. the maxRetries parameter for casa::FitGaussian::fit()). |

Selavy.Fitter.criterium | double | 0.0001 | The convergence criterium for casa::FitGaussian::fit() (this does not seem to be used in the fitting). |

Output files |
|||

Selavy.Fitter.writeComponentMap | bool | true | Whether to write out an image showing the fitted Gaussian components, as well as a “fit residual” map (the input image with the component map subtracted). |

Selavy.Fitter.imagetype | string | fits | Type of image to write - either “casa” or “fits”. |

Selavy.writeFitResults | bool | false | Whether to write out the fitResults files (catalogues and annotations). |

Selavy.fitBoxAnnotationFile | string | selavy-fitResults.boxes.ann | A Karma annoation file showing the location and size of boxes used in the Gaussian fitting (only produced if Fitter.fitJustDetection = false). |

Selavy.outputComponentParset | bool | false | Whether to write out a component parset. |

Selavy.outputComponentParset.filename | string | <No default> | The name of the file to which the component parset should be written. |

Selavy.outputComponentParset.maxNumComponents | int | -1 | The maximum number of components to be written to the parset. If negative (the default), all will be written. |

Selavy.outputComponentParset.referenceDirection | vector<string> | “” | The direction that is used as the reference (tangent point), from which the positions are
calculated as l & m coordinates. If left blank, the image centre position will be
used. This should be in the same format as given for the direction parameters in
(cimager) (ie. a 3-element vector). |

Selavy.outputComponentParset.reportSize | bool | true | If true, the fitted shape of the components is written to the parset. If false, they are written as point sources. |

Selavy is designed to work in conjunction with the ASKAPsoft pipeline. For continuum data, a common processing mode will be multi-frequency synthesis, where the output will be a series of “Taylor-term” images, being the coefficients of a Taylor-expansion of the frequency spectrum at each pixel. The equations governing this expansion are indicated here

Equation 1 and 2 show the assumed spectral shape (in linear and log space respectively), while Equation 3 shows the Taylor expansion about the reference frequency (nu_0) for 3 terms. The coefficients of these terms are the quantities in the Taylor-term images: taylor 0 - a total intensity (I) image (at a fiducial frequency); taylor 1 - the alpha * I map, where alpha is the spectral index; taylor 2 - I * (beta + 0.5*alpha*(alpha-1)), where beta is the spectral curvature.

We want to extract a value for alpha & beta for each component. We do this by fitting to the total intensity image, as described above. Each resulting component is then fitted to the taylor 1 & 2 images, keeping the shape and location constant. This just fits the normalisation of the Gaussian. The total flux of the Gaussian is then extracted and used in the above relations.

The measurement of the spectral information in this way is dependent on the fitting, so one needs to request Gaussian fitting via the above parameters. The measurement of spectral index and spectral curvature can be requested independently (if, for instance, you have only a spectral index map).

Selavy defaults to assuming the images have been produced in the
ASKAPsoft pipeline, and are thus named in a specific way. It is
possible, however, to specify alternative names for the spectral index
& curvature images (ie. Taylor 1 & 2 maps), although the data they
hold must be formed in the same way (ie. conform to the above
relationships). The image names are specified via the
**spectralTermImages** input parameter. If this is not given, the
names are derived, if possible, from the image name, assuming a
standard format: if the total intensity image is named
XXX.taylor.0.YYY, then the spectral index map will be XXX.taylor.1.YYY
and the spectral curvature map will be XXX.taylor.2.YYY.

If the additional Taylor maps are not available, or the
**findSpectralTerms** parameters are set to **false**, then the values
for spectral-index and spectral-curvature will be set to the special
value of -99.

Selavy provides an alternative method for obtaining the spectral terms. If a continuum cube is available (that is, a cube that preserves the individual channels, rather than collapsing them to form the Taylor images), then spectra can be extracted from it and the spectral terms determined via fitting.

Each component has its spectrum extracted in the same manner as for
the Rotation Measure Synthesis (see below, and on *Extraction of Spectra, Images and Cubelets*).
This spectrum is then fitted, using a non-linear Levenberg-Marquardt
algorithm, with the function given by Equation 1 above. The user can
select how many terms to fit - the default is 3, so that I_0, alpha
and beta will be fit, but this can be set to 1 or 2 instead.

The user can also impose a threshold S/N value on a component-by-component basis, below which no fitting will be done. This defaults to zero, however, meaning all components will have their spectra fitted.

Parameter |
Type |
Default |
Explanation |
---|---|---|---|

Selavy.spectralTermsFromTaylor | bool | true | Which mode to use to measure the spectral terms. True means use the Taylor-term images, while false means use a continuum cube. |

Taylor-term images |
|||

Selavy.findSpectralTerms | vector<bool> | 2 terms, same as Fitter.doFit | A vector of 2 terms, indiciating whether to find the spectral index
(first term) and the spectral curvature (2nd term). It is possible to
give only one term (e.g. findSpectralTerms = true) - then the second
term will be set to false. To request both, put findSpectralTerms =
[true,true]. |

Selavy.spectralTermImages | vector<string> | Derived from image name - see text | You can explicitly set the images for each term like so:
spectralTermImages = [image1, image2]. |

Continuum-cube |
|||

Selavy.spectralTerms.cube | string | “” | The name of the continuum cube from which spectra should be extracted. |

Selavy.spectralTerms.nterms | int | 3 | The number of terms to fit to in the continuum cube spectrum. Valid values are 1 (only I_0), 2 (I_0 &alpha), or 3 (I_0, alpha & beta). Larger values are set to 3, smaller values to 1. |

Selavy.spectralTerms.snrThreshold | float | The threshold in component signal-to-noise ratio, below which no fitting is done to the spectra. |

Selavy can be used to perform Rotation Measure Synthesis on the full-Stokes spectra of continuum components identified from a continuum map. The procedure follows that specified by the POSSUM survey science team, and can be described as follows:

- A continuum component is found in a continuum image, and fitted with a 2D Gaussian (as described above).
- Spectra are extracted from the corresponding location in Stokes I, Q, and U continuum cubes. The spectra are extracted from an NxN pixel box centred on the peak of the Gaussian (where N defaults to 5, as per the POSSUM specification). See
Extraction of Spectra, Images and Cubeletsfor details on the method used.- The noise spectra in Q & U are extracted and averaged together to form the QU noise spectrum. This is used for weighting (if the “variance” weightType is selected).
- The Q and U spectra are normalised by a model spectrum of Stokes I - we use either the Taylor-term expansion from the Stokes-I imaging or a low-order polynomial fit. They are then used to generate a Faraday Dispersion function (FDF) as a function of lambda-squared. The FDF is then multiplied by the model I flux at the reference wavelength.
- The location of the peak of the FDF is recorded as the rotation measure of the component, with the peak value of the FDF (multiplied again by the Stokes I model spectrum) giving the polarised intensity.
- The peak is also fitted with a three-point quadratic function, yielding a better estimate of the peak (avoiding the sampling of the Faraday depth function).
- These and other quantities are written to a polarisation catalogue, that is named in the same way as the component and island catalogues - see the description above.
- The extracted spectra of I, Q & U can also be written out to individual files, along with the FDF and RMSF arrays. These files can be either CASA or FITS format, selectable via the
RMSynthesis.imagetypeparameter. Unlike elsewhere, this defaults tofits. The FDF and RMSF files can each be written as either single complex-valued spectra, or separate spectra for the phase & amplitude. If FITS output is being used, the complex-valued option is not available.

Parameter |
Type |
Default |
Explanation |
---|---|---|---|

Selavy.RMSynthesis | bool | false | Whether to run Rotation Measure Synthesis |

Selavy.RMSynthesis.cube | string or vector<string> | “” | The name of the input cube to read the continuum spectra from. This
can take wildcards (“%p”) for the polarisation or be a list of cubes
corresponding to at least I,Q,U. See Extraction of Spectra, Images and Cubelets for more
details. |

Selavy.RMSynthesis.beamLog | string | “” | The filename of a beam log file (see Extraction of Spectra, Images and Cubelets) that can be
used to correct the extracted fluxes with a channel-dependent
beam. This can incorporate the “%p” wildcard. |

Selavy.RMSynthesis.boxWidth | int | 5 | The width (N) of the NxN box to be applied in the extraction of Stokes spectra. |

Selavy.RMSynthesis.noiseArea | float | 50 | The number of beam areas over which to measure the noise. |

Selavy.RMSynthesis.robust | bool | true | Whether to use robust statistics in measuring the noise. |

Selavy.RMSynthesis.writeSpectra | bool | true | Whether to write out the extracted spectra to image files on
disk. This will also write out the FDF and RMSF determined from the
RM Synthesis. The filenames are of the form
[outputbase]_spec_[Stokes]_[objectID] for the spectra,
[outputbase]_FDF_[objectID] for the FDF, and
[outputbase]_RMSF_[objectID] for the RMSF. |

Selavy.RMSynthesis.imagetype | string | casa | Type of image to create when extracting. Can be either “casa” or “fits” - anything else will throw an error. |

Selavy.RMSynthesis.outputBase | string | “” | The base name for the output files - a front-end to
extractSpectra.spectralOutputBase (Extraction of Spectra, Images and Cubelets). |

Selavy.RMSynthesis.writeComplexFDF | bool | true | If true, write the FDF and RMSF spectra as single complex-valued image files. If false, the amplitude and phase of each are writtedn as separate files. |

Selavy.RMSynthesis.weightType | string | variance | The type of weighting to be used in the RM Synthesis. Can be either “variance” (each channel is weighted by the inverse square of its noise), or “uniform” (each channel has a weight of 1). Anything else defaults to “variance”. |

Selavy.RMSynthesis.modelType | string | taylor | The type of model used to represent the Stokes-I spectrum. This can be either “taylor”, in which case the Taylor-term parameters from the imaging & component fitting are used, or “poly”, in which case a low-order polynomial is used to model the spectrum. |

Selavy.RMSynthesis.modelPolyOrder | int | 3 | The order of the polynomial to use in the Stokes-I model fit. Only used if modelType=poly. |

Selavy.RMSynthesis.numPhiChan | int | 40 | Number of channels in the Faraday depth sampling vector. |

Selavy.RMSynthesis.deltaPhi | float | Spacing between the Faraday depth channels [rad/m2]. | |

Selavy.RMSynthesis.phiZero | float | Centre RM of the Faraday depth vector, [rad/m2]. | |

Selavy.RMSynthesis.polThresholdSNR | float | Signal-to-noise threshold (in the FDF) for a valid detection. | |

Selavy.RMSynthesis.polThresholdDebias | float | Signal-to-noise threshold (in the FDF) above which to perform debiasing. |