User Parameters - Spectral-line Source-finding¶
It is possible to run source-finding with Selavy on the created spectral-line cubes. Through the pipeline, it is possible to specify either flux or SNR thresholds, apply a variable SNR threshold, and pre-process the cube with smoothing or multi-resolution wavelet reconstruction (to enhance the signal-to-noise of real sources).
Variable |
Default |
Parset equivalent |
Description |
---|---|---|---|
|
|
none |
Whether to do the source-finding with Selavy on the
final mosaic images. If not given in the config file, it
takes on the value of |
|
selavy-%i.txt |
Selavy.resultsFile (Selavy Basics) |
Base name for source catalogues. The %i wildcard will be resolved into the imageName corresponding to the spectral image. You may find this parameter useful to rename new catalogues generated from re-processing. NB: The same parameter is used for continuum source-finding. |
|
SB%s |
Selavy.sourceIdBase (Selavy Basics) |
Base identifier for catalogue components. The %s wildcard will be resolved into the SBID of the observation. You may find this parameter useful to rename new catalogue components generated from re-processing. NB: The same parameter is used for continuum source-finding. |
|
false |
none |
If true, the source-finding will be run on the individual beam images as well. |
|
false |
none |
If true, the source-finding will be run on the individual field mosaics. Will be set to false if the number of fields is one. |
|
|
none |
Time request for source-finding jobs |
|
|
none |
Number of cores used on each node. If not provided, it will be the lower of the number of cores requested or the maximum number of cores available per node. |
|
6 |
nsubx (Selavy Basics) |
Number of divisions in the x-direction that divide the image up, allowing parallel processing in the source-detection. |
|
9 |
nsuby (Selavy Basics) |
Number of divisions in the y-direction that divide the image up, allowing parallel processing in the source-detection. |
|
18 |
nsubz (Selavy Basics) |
Number of divisions in the z-direction that divide the image up, allowing parallel processing in the source-detection. |
|
0 |
overlapx (Selavy Basics) |
The overlap (in pixels) between neighbouring divisions in the x-direction. |
|
0 |
overlapy (Selavy Basics) |
The overlap (in pixels) between neighbouring divisions in the y-direction. |
|
20 |
overlapz (Selavy Basics) |
The overlap (in pixels) between neighbouring divisions in the z-direction. |
Searching |
|||
|
8.0 |
snrcut (Selavy Basics) |
The signal-to-noise ratio threshold to use in the source-detection. |
|
true |
flagGrowth (Selavy Basics) |
A flag indicating whether to grow detections down to a lower threshold. |
|
3.0 |
growthCut (Selavy Basics) |
The secondary signal-to-noise threshold to which detections should be grown. |
|
|
threshold (Selavy Basics) |
The flux threshold to use in the source-detection. If left
blank, we use the SNR threshold |
|
|
growthCut (Selavy Basics) |
The secondary signal-to-noise threshold to which detections
should be grown. Only used if |
|
|
Weights.weightsCutoff (Thresholds in Selavy) |
The cutoff level, as a fraction of the peak in the weights
image, used in the source-finding. Only applies if the image
being searched has a corresponding weights image. If not
given, the value used is the square of |
|
spatial |
searchType (Selavy Basics) |
Type of searching to be performed: either ‘spectral’ (searches are done in each 1D spectrum) or ‘spatial’ (searches are done in each 2D channel image). Anything else defaults to spectral. |
|
true |
VariableThreshold (Thresholds in Selavy) |
A flag indicating whether to determine the signal-to-noise threshold on a pixel-by-pixel basis based on local statistics (that is, the statistics within a relatively small box centred on the pixel in question). The dimensions of the box are governed by the search type - if ‘spectral’ then it will be a one-dimensional box slid along each spectrum, else if ‘spatial’ it will be a 2D box done on each channel image. |
|
35 |
VariableThreshold.boxSize (Thresholds in Selavy) |
The half-width of the sliding box used to determine the local statistics. |
|
false |
VariableThreshold.reuse (Thresholds in Selavy) |
A flag indicating whether to reuse any existing noise maps created by the variable-threshold algorithm. If false, they will be generated each time the job runs. |
|
5 |
minPix (Selavy Basics) |
Minimum number of (spatial) pixels allowed in a detection |
|
5 |
minChan (Selavy Basics) |
Minimum number of channels allowed in a detection |
|
2592 |
maxChan (Selavy Basics) |
Maximum number of channels allowed in a detection |
Pre-processing |
|||
|
true |
flagSmooth (Preprocessing for Selavy) |
Whether to smooth the input cube prior to searching. |
|
spectral |
smoothType (Preprocessing for Selavy) |
Type of smoothing to perform - either ‘spectral’ or ‘spatial’. Anything else defaults to spectral. |
|
5 |
hanningWidth (Preprocessing for Selavy) |
The width of the Hanning spectral smoothing kernel. |
|
3 |
kernMaj, kernMin, kernPA (Preprocessing for Selavy) |
The specs for the spatial Gaussian smoothing kernel. Either
a single number, which is interpreted as a circular Gaussian
(kernMaj=kernMin, kernPA=0), or a string with three values
enclosed by square brackets (eg. |
|
false |
flagAtrous (Preprocessing for Selavy) |
Whether to use the multi-resolution wavelet reconstruction. |
|
1 |
reconDim (Preprocessing for Selavy) |
The number of dimensions in which to perform the reconstruction. 1 means reconstruct each spectrum separately, 2 means each channel map is done separately, and 3 means do the whole cube in one go. |
|
4 |
snrRecon (Preprocessing for Selavy) |
Signal-to-noise threshold applied to wavelet arrays prior to reconstruction. |
|
1 |
scaleMin (Preprocessing for Selavy) |
Minimum wavelet scale to include in reconstruction. A value of 1 means “use all scales”. |
|
0 |
scaleMax (Preprocessing for Selavy) |
Maximum wavelet scale to use in the reconstruction. If 0 or negative, then the maximum scale is calculated from the size of the array. |
Pre-processing |
|||
|
false |
optimiseMask (Post-processing of detections) |
Whether to improve the mask of detected sources prior to parameterisation via the mask optimisation technique. |