The science goal of many extra-galactic SCUBA-2 observations is to detect unresolved point sources. In the examples below we work through the reduction of just such an extra-galactic field, A1835.
Most extra-galactic objects are on average only slightly brighter than the confusion limit—the fluctuations of the background sky brightness due to multiple super-imposed, unresolved sources within the telescope beam, below which individual sources cannot be detected. It is likely that any sources in the map will be at best, only a few standard deviations brighter than the noise in the map (caused by a combination of instrumental noise and source confusion).
The basic reduction method for maps like these follow two main steps—running the data through the
map-maker using the
dimmconfig_blank_field.lis configuration file (see Section 3.7). Then applying the
SCUBA2_MATCHED_FILTER recipe (see Section 8.7).
Step 1: Run the map-maker
In this example the raw data are stored locally in a directory called
data. We have three observations (#13, #18,
#21) of the field which we will reduced independently.
Step 2: Combine the maps
These three maps are then combined using the Picard recipe
MOSAIC_JCMT_IMAGES. In this case we accept the
default of wcsmosaic mosaicking and nearest-neighbour pixel spreading and so do not supply a parameter
The output map,
cosmo3_mos.sdf (named for the last input file appended by _mos), is shown in the left-hand
panel of Figure 7.1. The advantage of using the Picard recipe over standalone Kappa commands is that the
exposure time is also propagated correctly to the output mosaic (it is stored in the
Step 3: Apply the matched filter
In order to optimally find sources that are the size of the telescope beam, we apply the matched filter recipe, namely
SCUBA2_MATCHED_FILTER. We create a simple parameter file called
SMOOTH_FWHM = 15 indicates that the background should be estimated by first smoothing the map and
PSF with a 15-arcsec FWHM Gaussian. Next, the recipe is executed as follows:
The output of this operation is a smoothed image called
cosmo3_mos_mf.sdf and a cropped version is shown in
the right-hand panel of Figure 7.1. You can immediately see the contrast to the left-hand panel which
is the output from the map-maker. A number of signal peaks now emerge as possible sources.
Step 4: Crop the map
Next we shall crop the map to remove the noisy edges, in this case to a 900-arcsec radius circle. The output file will be named
Step 5: Make an S/N map
Finally, we need to find sources. The filtered map contains a VARIANCE component, so it is easy to produce a S/N map using the Kappa task makesnr:
The resulting map,
cosmo2_mos_mf_snr, is shown in Figure 7.2. Compared with the matched filter map the
edges no longer appear as noisy because they have been down-weighted by the larger noise values where there
were less data.
Step 6: Identify sources
The basic procedure for identifying sources would be to locate peaks above some threshold S/N. The S/N image above shows peaks that are likely to be real sources. For a start, a source appears where expected at the 0,0 position.
But how can we check if these sources are real?
Although this method is considerably simpler to execute, the products have undergone more advanced processing than the manual method just given. The pipeline is particularly recommended for this recipe due to its extra analysis steps.
Step 1: Create input file
Create an file with the names of all the files you wish to process (e.g.
Step 2: Run the pipeline
The pipeline must first be initiated for the wavelength you are working on. In the case below this is 850 m. Note that the date does not have to be specified when initialising the pipeline. The pipeline is run using the
REDUCE_SCAN_FAINT_POINT_SOURCES_JACKKNIFE recipe; this uses
dimmconfig_blank_field.lis as the
configuration file. If you wish to provide an alternative file you will need to put the name of the new
configuration file in a recipe parameter file. See Section 4 for details.
You substitute the required date for
YYYYMMDD. The pipeline will write out a large number of files with the
||The map for each observation|
||The map for each observation with an artificial point source added at the map centre|
||The co-add of all the |
||The whitened version of |
||The calibrated version of |
||The matched-filtered version of |
FAINT_POINT_SOURCES_JACKKNIFE is a recipe designed to process blank field/extra-galactic data. The recipe
uses a jack-knife method to remove low-spatial frequency noise and generate a matched filter output
The recipe processes each observation twice, a standard reduction first, then a re-run with a fake point source
added to the time series. This produces a co-added signal map (
_wmos) and a coadded PSF map
FAINT_POINT_SOURCES_JACKKNIFEcan be used interchangably with
After the map-maker has completed, the recipe will call
SCUBA2_JACKKNIFE. This routine divides the
observations into two groups (odd and even) which are co-added and then subtracted to create a jack-knife
map. This map contains only noise with no contribution from astronomical signal. The angular power spectrum
of this map is then used to estimate and remove the residual 1/f noise from the signal map and the PSF map;
this is the whitening step. The whitened jack-knife map is run through
SCUBA2_MATCHED_FILTER using the
whitened PSF map as the PSF input. It is this matched filter map which will be of most interest to
See SUN/264 for more information on
REDUCE_SCAN_FAINT_POINT_SOURCES_JACKKNIFE and all other pipeline
Step 3: (Optional) Re-run
You may wish to run the
SCUBA2_JACKKNIFE step again independently from the pipeline. If your final map does
not look as expected you might first examine the individual mosaics from the pipeline (
_fmos), one of these
observations might show visible artefacts that you wish to exclude from the co-add. The size of the region in the
jack-knife image which is used to do the whitening step is determined automatically, but the method may fail if
the box is too small.
If you decide to re-run this step you first co-add all the
_mappsf files to create a coadded PSF using the
Next create a parameter file (
recpars.lis) for the jack-knife recipe (
SCUBA2_JACKKNIFE) containing the
Another option for this parameter file is
WHITEN_BOX to set the size of the region used to calculate the angular
power spectrum. Finally run
This will create files beginning with
that should have the same suffices as above:
This example is concerned with recovering bright extended emission. The signal from extended emission varies
slowly as seen by the array passing over it. It thus appears at lower frequencies in the power spectrum and
complicates the high-pass filter selection. Too harsh a filter will make flat maps but any extended emission will
have been removed in doing so.
Step 1: Running the map-maker
We run the map-maker using
dimmconfig_bright_extended.lis; we have also specified a couple of overrides on the
maptol = 0.04 is slightly more stringent than default and
ast.zero_snr = 3.5 constrains everything
below 3.5 to
In this example we give the map-maker a file containing a list of the input files (
dimmconfig_bright_extended.lis is in the local directory.
The resulting map is shown in Figure 7.3.
Step 2: Generating an external mask
Next we create an external mask from the output of makemap. Here we follow the steps outlined in Section 6.6.
This S/N map is thresholded to set everything below 3 to 0 and everything above to 1.
The thresholded map is shown in the left-hand panel of Figure 7.4. The next step is to smooth this map by convolving it with a Gaussian of 16 arcsec. For this we use a factor of 4 for the FWHM parameter.
We threshold the map again to produce our mask. In this case all values below our threshold are set to ‘bad’. The smoothed map now has values scaled between 0 and 1, we set our threshold at 0.02 to include more of the emission beyond the 3 edge.
The final mask is shown in the right-panel of Figure 7.4. Note how it encompasses more emission and has softer
edges than the first threshold map.
Step 3: Re-running the map-maker with an external mask supplied
As a last step the map is re-made with this mask supplied as an external file. For this run we apply the additional parameters in a personalised configuration file,
The configuration file,
mydimmconfig.lis, has the following format—note how it is based on
_bright_extended.lis. It has decreased the convergence parameter to
maptol = 0.03 but increased the
number of iterations to compensate as 40 is unlikely to be sufficient.
Step 4: Cropping the map
We now crop the map to remove the noisy edges using the Picard recipe
CROP_JCMT_IMAGES. To determine what
to trim we can look at the exposure-time image with Gaia. See Figure 7.5.
The exposure-time map shows a sharp drop off at a radius of 30 arcmin. We can thus specify a parameter file like below.
The final cropped map is shown in Figure 7.6. Compared with the first map out of the map-maker (Figure 7.3), slightly more of the faint extended emission is apparent.
One of the challenges facing this type of reduction is the need to account for both faint extended structure and very bright sources in the same map. You may find some degree of bowling remains around the brightest sources.
There are areas you may wish to experiment with. One is to adjust the filtering. Another option is to supply an external mask from a different dataset, e.g. a Herschel map. See Chapter 6 for further discussion.