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Our algorithm to produce the short-spacing information is coded in the
UV_SHORT task. This task have about 25 input parameters which are
not always intuitive to fill in (in particular the relative weight between
single dish and interferometric data). We thus wrote a SIC procedure
which wraps up most of the task input parameters according to the context.
This procedure is driven by the widget called short-spacings
processing in the main MAPPING menu. Although you have the
possibility to access all the control parameters of the UV_SHORT
task through this widget, you should have only the file names and the
single-dish data unit to fill in, all other parameters being guessed by the
procedure.
This task starts from the data format produced by the CLASS TABLE
command. Basically, this is a GDF table containing one line per spectrum,
the columns representing the lambda offset, beta offset, weight, and the
spectrum intensities. This format is subject to change: Please, refer to
the TABLE documentation for up-to-date information. At least two
characteristics of this table must be tuned on the characteristics of the
interferometric observations:
- The velocity axis
- because all the computations are done plane by
plane without velocity resampling.
- The center of projection
- because all the computations are done using
the offset to this center.
To do this, the user needs to work both in MAPPING and CLASS.
- Inside MAPPING,
- both informations can be found with the following
commands
1 read uv field-1
2 uv_map
3 write dirty field-1
4 header field-1.lmv
Comments:
- Steps 1-3
- Images the first field of a mosaic because the information
about the velocity axis will be much easier to dig up in the dirty cube
field-1.lmv than in the
table field-1.uvt.
- Step 4
- Displays the header as follow:
1 File : field-1.lmv REAL*4
2 Size Reference Pixel Value Increment
3 256 129.0000000000000 0.000000000000000 -1.5514037841057871E-06
4 256 129.0000000000000 0.000000000000000 1.5514037841057871E-06
5 49 25.50000000000000 10.50000000000000 0.2000000029802322
6 1 0.000000000000000 0.000000000000000 0.000000000000000
7 ...
8 Axis 1 A0 05:40:54.270 Axis 2 D0 -02:28:00.00
9 ...
- Line 8
- Indicates that the axes 1 and 2 are space coordinates around
the phase center
,
.
- Lines 3-6
- Describes the axes and in particular the velocity axis as
line 5, i.e. 49 channels of 0.2 km/s width, the velocity of the 25.5
channel being 10.5 km/s.
- Inside, CLASS
- you need to type the following commands to align the
characteristics of your single-dish data to the above values
1 file in single-dish-reduced.30m
2 file out single-dish-reprojected.30m single
3 set level 5
4 set system equatorial 2000.00
5 find
6 for ientry 1 to found
7 get next
8 modify position 05:40:54.270 -02:28:00.00
9 write
10 next ientry
11 file in single-dish-reprojected.30m
12 find
13 table short-spacings new /resample 49 25.50 10.50 0.2000000029802322 V
14 xy_map short-spacings
15 header short-spacings.lmv
- Steps 1-10
- Will ensure that the used data is in Equatorial 2000
coordinate system (The default for PdBI data) with the right
projection center. Steps 1 and 2 opens the input and output files. Step 5
decreases the verbosity of CLASS: This is useful when dealing with OTF
maps with a large number of spectra. Step 4 enforces the use of the 2000
equatorial coordinate system. Steps 5 to 10 modifies the coordinates of
all spectra inside the input file and write them in the output files.
- Steps 11-13
- Create the table with an on-the-fly resampling of the
velocity axis. In the future, the CLASS TABLE will also be able
to modify the projection center on-the-fly.
- Steps 14-15
- Enable to verify by eye that the consistency of the
single-dish data with the interferometric data, i.e. the displayed header
information for velocity axis and projection center should be identical
to the above ones. Those steps are optional as the input really used by
the short-spacing processing is produced at step 13.
It is clear that your single-dish data must be reduced before applying the
above steps. CLASS has many facilities to visualize and reduce your
data.
Next: In and out MAPPING
Up: Practical considerations
Previous: How to optimize single-dish
Contents
Index
Gildas manager
2014-07-01