Sunspot Band6 Imaging for CASA 6.5.4: Difference between revisions

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[[File:sunspot_fieldvstime_CASA_6.5.4.png|thumb|right|'''Fig. 1.''' The plot of the FieldID of the scans as a function of the time.]]
[[File:sunspot_fieldvstime_CASA_6.5.4.png|thumb|right|'''Fig. 1.''' The plot of the FieldID of the scans as a function of the time.]]
Define our split and calibrated MS name.
<source lang='python'>
# In CASA
asdm = 'uid___A002_Xae00c5_X2a8d'
msc = asdm + '.ms.split.cal'
</source>


The actual duration of the observation does not exactly equal the required duration of the 149-pointing mosaic. Hence, some fields of the mosaic were observed twice, as shown in Figure 1.
The actual duration of the observation does not exactly equal the required duration of the 149-pointing mosaic. Hence, some fields of the mosaic were observed twice, as shown in Figure 1.

Revision as of 18:44, 5 April 2024

Last checked on CASA Version 6.5.4

Overview

This guide features CARTA, the “Cube Analysis and Rendering Tool for Astronomy,” which is the new NRAO visualization tool for images and cubes. The CASA viewer (imview) has not been maintained for a few years and will be removed from future versions of CASA. We strongly recommend using CARTA, as it provides a much more efficient, stable, and feature rich user experience. A comparison of the CASA viewer and CARTA, as well as instructions on how to use CARTA at NRAO, is provided in the CARTA section of the CASA docs.

This portion of the guide will cover the image synthesis of a sunspot. It begins where Sunspot_Band6_Calibration_for_CASA_6.5.4 is completed. If you completed the calibration, you may continue working in the directory Sunspot_Band6_UncalibratedData.

If you did not complete the calibration portion of the guide, the calibrated visibility data is available to download at Sunspot_Band6#Obtaining the Data. Once the download has finished, unpack and cd to the directory:

# In bash
tar -xvzf Sunspot_Band6_CalibratedData.tgz
cd Sunspot_Band6_CalibratedData

Confirm your version of CASA

This guide has been written for CASA release 6.5. Please confirm your version before proceeding.

# In CASA
from casatools import version
vernum = str(version()[0])+'.'+str(version()[1])
print("You are using CASA ver. "+vernum)
if float(vernum) < 6.5:
 print("YOUR VERSION OF CASA IS TOO OLD FOR THIS GUIDE.")
 print("PLEASE UPDATE IT BEFORE PROCEEDING.")
else:
 print("Your version of CASA is appropriate for this guide.")

Flagging of the surplus scans

Fig. 1. The plot of the FieldID of the scans as a function of the time.

Define our split and calibrated MS name.

# In CASA
asdm = 'uid___A002_Xae00c5_X2a8d'
msc = asdm + '.ms.split.cal'

The actual duration of the observation does not exactly equal the required duration of the 149-pointing mosaic. Hence, some fields of the mosaic were observed twice, as shown in Figure 1.

# In CASA
plotms(vis=msc, xaxis='time', yaxis='field')

Although the visibility data of the surplus scans are valid, it is better that they are not used for the image synthesis, considering the uniformity of the sensitivity. For this reason, we flag the surplus scans with flagdata.

# In CASA
msc = 'uid___A002_Xae00c5_X2a8d.ms.split.cal'
flagdata(vis=msc, mode='manual', timerange='2015/12/18/20:02:35~20:08:20', flagbackup=False)

We also flag the baselines between 7m and 12m antennas.

# In CASA
flagdata(vis=msc, mode='manual', antenna='CM*&DV*;CM*&DA*, flagbackup=False)

Continuum imaging

Fig. 2. The pattern of mosaic AFTER the re-calculation of the direction.
Fig. 3. The residual map (color), effective responsibility (black contours) and CLEAN mask (red box).
Fig. 4. The CLEANed image of a sunspot observed with Band6

Now we are ready to synthesize a sunspot image from the visibilities. For the image synthesis, we execute the following tclean command.

# In CASA
tclean(
vis=msc,
field='0,3~150',
spw='0,1,2,3',
imagename='AR12470_B6AllSpw_I',
imsize=[512, 512],
cell='0.4 arcsec',
phasecenter=0,
stokes='I',
specmode='mfs',
interactive=False,
usemask='pb',
pbmask=0.73,
pblimit=0.73,
gridder='mosaic',
mosweight=True,
deconvolver='clark',
pbcor=False,
weighting='briggs', #briggsbwtaper
robust=1.0,
niter=100000,
gain=0.1,
threshold='1.0Jy'
)

To obtain good SNR of the image, we synthesize an image from the data including all spectral windows. Here we comment on the parameters that are the special measures for our solar image synthesis.

  • usemask='pb'
  • pbmask=0.73
  • pblimit=0.73
  • mask='box[[555pix,565pix],[1505pix,1490pix]]'

In most cases of solar observations, the field of view (FoV) of the map is filled up with solar structures. Therefore, ideally, the CLEAN box for the solar image synthesis is the same as the FoV. Since the observation was done with the 7m + 12m heterogeneous array, the FoV of the 7m-array is not the same as that of the 12m-array, and the area near the outer edge of the FoV is observed only with 7m-array, as shown in Figure 2. If the CLEAN box includes the area, the synthesized map will include remarkable artificial structures. To avoid the artificial structure, we need to make a CLEAN box that is smaller than the FoV of the 12m-array. In the tutorial, the CLEAN box covers the area that the effective response is larger than 0.73, as shown in Figure 3.

  • gridder='mosaic'
  • mosweight=True

To deal the data obtain with a heterogeneous array in the CLEAN task, we need to set the mosaic option to imagemode, even when you synthesize an image from the single-pointing data. In most solar cases, the average brightness of a field might be significantly different from that of the other fields. Therefore, we use mosweight function. See CARMA_spectral_line_mosaic_M99_3.2#Deconvolution_and_Imaging.

  • weighting='briggs'
  • robust=1.0

The briggs weighting, which is the default weighting option of the CLEAN task, is used for the solar image synthesis. The longest baseline of the 12m-array for the observation is shorter than that of C40-1. However, the visibility data includes the data of over 200m baselines, because the center of the 12m-array is about 200 m distant from the center of the 7m-array. When the robust parameter is set to 0 as a default, the contribution of the longer baseline data is too large and the artificial stripes appear in the synthesized map. For the reason, we set the robust value to 1, and the weighting function is set to near the natural weighting. The robust parameter is not fixed based on the deep optimization. There is still room to examine that.

Figure 4 is the synthesized image from the SV data.

Primary beam correction and creating of the FITS file

To do the primary beam correction and create the FITS file of the corrected map, we use impbcor and exportfits.

# In CASA
impbcor(imagename='AR12470_B6AllSpw_I.image', pbimage='AR12470_B6AllSpw_I.pb',
outfile='AR12470_B6AllSpw_I.pbcor', mode='divide')

exportfits(imagename='AR12470_B6AllSpw_I.pbcor', fitsimage='AR12470_B6AllSpw_I.fits')

Note: The FITS file can be imported to the data-analysis environment based on SolarSoftWare (SSW) + IDL without any modification, but the coordinate system is the RA/Dec coordinate. You have to convert the coordinate from the RA/Dec coordinate system to the heliocentric coordinate system (X-cen/Y-cen) for the co-alignment with the other solar instrument’s data.