Transient reduction pipeline

Transient Reduction Pipeline

2010 May 11 - T. Joseph W. Lazio

There are a class of observations for which only relatively simple data reduction steps are needed. One such example is that of transient observations, which are typically conducted in continuum mode and for which one is merely trying to determine a flux density of an essentially unresolved or only partially resolved source. This guide describes the steps in such a pipeline.

Warning: This guide was written at the time when the EVLA was still in its commissioning phase. As the instrument matures, specific steps taken here may need to be adjusted. Caveat emptor.

In order to process these data in a semi-automatic fashion, certain assumptions are made

• Data stored as an ALMA Science Data Model (ASDM) file on disk.
• Sources, listed in order of appearance, are structured as Option A or Option B
  • Option A: phase calibrator, target source, amplitude calibrator
  • Option B: amplitude calibrator, phase calibrator, target source

This guide should be set up so that a pipeline script can be extracted from it.

User Input

1. Obtain the name of the ASDM file, use it to construct the Measurement Set name.

# In CASA
ASDM_name=raw_input('ASDM file name:')
msname=ASDM_name+'.ms'

2. Determine the order in which sources appear in the observation.

# In CASA
obs_structure=raw_input('Structure of observations [A|B]:')
if obs_structure == 'A':
    sources = {'acal':'2', 'pcal':'0', 'target':'1'}
elif obs_structure == 'B':
    sources = {'acal':'0', 'pcal':'1', 'target':'2'}

3. Reference antenna. (Should be able to determine this from the measurement set!!)

# In CASA
refant=raw_input('Enter desired reference antenna [e.g., ea21]: ')

4. Other set up stuff.

# In CASA
acal_std_name = {}
acal_std_name['3C286'] = frozenset(['3C286','3C 286',
                                    '1328+30','1328+307','B1328+307','B1328+30',
                                    '1331+305','J1331+305','J1331+3030'])
acal_std_name['3C138'] = frozenset(['3C138','3C 138',
                                    '0518+16','0518+165','0518+1635','B0518+16','B0518+165','B0518+1635',
                                    '0521+166','0521+1638','J0521+166','J0521+1638'])
acal_std_name['3C147'] = frozenset(['3C147','3C 147',
                                    '0538+49','0538+498','0538+4949','B0538+49','B0538+498','B0538+4949',
                                    '0542+498','0542+4951','J0542+498','J0542+4951'])
acal_std_name['3C48'] = frozenset(['3C48','3C 48',
                                   '0134+32','0134+329','0134+3254','B0134+32','B0134+329','B0134+3254',
                                   '0137+331','0137+3309','J0137+331','J0137+3309'])

Processing Steps

1. Read the data from the ASDM file converting to a Measurement Set with importevla. Apply basic flagging operations (zeros, shadowing) here.

importevla(asdm=ASDM_name, vis=msname,
           flagzero=True, flagpol=True, shadow=True)

spw_table=msname + '/SPECTRAL_WINDOW'
tb.open(spw_table)
freq=tb.getcol("REF_FREQUENCY")[0]
tb.close(spw_table)
if (1E9 < freq) and (freq < 2E9):
    band = 'L'
elif (2E9 < freq) and (freq < 4E9):
    band = 'S'
elif (4E9 < freq) and (freq < 8E9):
    band='C'
elif (8E9 < freq) and (freq < 12E9):
    band='X'
elif (12E9 < freq) and (freq < 40E9):
    band = 'K'
elif freq > 40E9:
    band = 'Q'

2. Extract the amplitude calibrator name from the data, for later calibration.

vishead(vis=msname, mode='get',
        hdkey='field', hdindex=sources['acal'])
acal_name=vishead()

3. Flag first (dummy) scan.

flagdata(vis=msname,
         mode='manualflag',
         selectdata=T, scan='1')

4. Flag first 10 seconds of each scan.

flagdata(vis=msname,
         mode='quack',quackinterval=10,quackmode='beg',
         selectdata=F)

5. Compress data in time.

   (You've checked that time average smearing isn't an issue ...?)

outmsname=ASDM_name+'_split.ms'
split(vis=msname,outputvis=outmsname,
      datacolumn='data',
      timebin='10s')

6. Set flux density of amplitude calibrator.

  Need some heuristics here.
  Also could be an issue if the frequency setting is such that one should use a model image at a different band than one is observing, e.g., observing near the top of the C band where an X band model might be more appropriate.

for tstname in acal_std_name.keys():
    if acal_name in acal_std_name[tstname]:
        modimage=tstname
modimage=modimage + '_' + band + '.im'
setjy(vis=outmsname,field=sources['acal'],modimage=modimage)

7. Quick-n-dirty bandpass

Bcaltable=ASDM_name + '.B1'
bandpass(vis=outmsname,caltable=Bcaltable,
         field=sources['acal'],
         solint='inf',combine='scan',refant=refant,
         solnorm=T,
         bandtype='B',
         append=F)

8. amplitude and phase calibration

Gcaltable=ASDM_name + '.G1'
gaincal(vis=outmsname,caltable=Gcaltable,
        field=sources['acal'],
        solint='inf',refant=refant,
        minsnr=5,
        solnorm=F,
        gaintype='G',calmode='ap',
        append=F,
        gaintable=Bcaltable)

gaincal(vis=outmsname,caltable=Gcaltable,
        field=sources['pcal'],
        solint='inf',refant=refant,
        minsnr=5,
        solnorm=F,
        gaintype='G',calmode='ap',
        append=T,
        gaintable=Bcaltable)

9. Apply calibration.

fluxtable=ASDM_name + '.flux1'
fluxscale(vis=outmsname,caltable=Gcaltable,fluxtable=fluxtable,
          reference=sources['acal'],
          transfer=sources['pcal'],
          append=F)

applycal(vis=outmsname,
         field=sources['pcal'],
         gaintable=[Bcaltable, Gcaltable])

applycal(vis=outmsname,
         field=sources['target'],
         gaintable=[Bcaltable, Gcaltable])

10. Form target source measurement set.

vishead(vis=msname, mode='get',
        hdkey='field', hdindex=sources['target'])
target_name=vishead()

targetms=target_name + '.ms'
split(vis=outmsname,outputvis=targetms,
      datacolumn='corrected',
      field=sources['target'])

11. Image and CLEAN.

imagename=target_name + '.im'