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Telecons 2019 - 2021

7/12/2021

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Telecons:
2021

July 09, 2021
July 02, 2021

June 25, 2021
June 18, 2021
June 11, 2021
June 4, 2021

May 28, 2021
May 21, 2021
May 14, 2021 (No meeting ISM2021  workshop)
May 07, 2021

April 30, 2021
April 23, 2021 (ALMA deadline - no meeting)
April 16, 2021
April 09, 2021
April 02, 2021 (Easter no meeting)

March 26 2021 (ALMA-IMF Workshop telcon)
March 19,2021
March 12, 2021
March 05,2021

February 26 2021
February 19 2021
February 12 2021
February 05 2021

January 29 2021
January 22 2021
January 15, 2021
January 8, 2021

2020
December 18, 2020
December 11, 2020
December 04, 2020

November 27, 2020
November 20, 2020


June 19, 2020

June 5, 2020


Feb 14 2020
Feb 7 2020
Jan 24 2020
January 10, 2020

...
2019
October 18, 2019
October 11, 2019
October 4, 2019
September 2019
August 2019
July 2019
March 13, 2019
March 6, 2019

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ALMA-IMF Data Reduction Telecon July 2019

7/3/2019

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Time: Jul 3, 2019 09:00 AM Mountain Time (US and Canada) 1500 UTC
https://ufl.zoom.us/j/236013059


Attendees:
Adam Ginsburg, Fumitaka Nakamura, Brian Svoboda, Timea Csengeri, Roberto Galván, Sylvain Bontemps, Hongli Liu, Fredrique Motte, Yohan Pouteau, Fabien Louvet, Fernando Olguin, Amelia Stutz



Agenda:
https://github.com/ALMA-IMF/reduction/wiki/Reduction-Telecon-July-2019

  1. Describe 1-month goals (5 min)
    1. By 1st week of August, continuum image of TC+TE 12M combined image for each field
  2. Issues with mosaic cleaning raised by Ana
  3. Summary of current status (1 min per person). Say whether you have imaged the field and sent the data and whether you have updated the spreadsheet appropriately. Please post images and/or links to the FITS data in this google doc, and please use a perceptually uniform colormap (i.e., not rainbow).
  • Timea: G327 3mm -> G328 3mm + 1mm:
    • B3 and B6 first continuum images with 12m config done but needs to be inspected (tests with selfcal and with 7m combination started)
  • Ana: G327 1mm -> G327 1mm (?)
  • Nichol: G328 3mm -> G327 -> 3mm (?)
  • Patricio: G328 3mm (??)
  • Xing: G328 1mm (??)
  • Roberto: G333.6 1mm
    • B3 is fine

    • B3 is fine but I want still to test different continuum subtraction.

    • B6 I am starting with
  • Fabien: G12.80 1mm, 3mm
    • Reduced, inspected weblog, no imaging yet
  • Thomas: -
    • No time this summer b/c PhD
  • Jordan: G338 3mm
  • Hongli: G338 1mm, G353 both
    • Some overlap on G338 - coordinate?  Can help
    • Notes on G353
  • Ken: G8.67 3mm
  • Natsuko: G8.67 1mm
  • Adam: W51-E and W51-IRS2
    • W51-E done: 12M-only
    • W51-IRS2: imaging started
  • Brian: G351.77 
    • preliminary 1mm imaging, no self-cal
    • Maybe will start working on W43 data sets
  • Sylvain: G351.77 3mm + 1mm
    •  reduced, inspected weblog, no imaging yet
  • Yohan: W43-MM1 3mm, MM2 1 & 3 mm, MM3 1 & 3 mm 
    • selfcal in process, some images available
    • Question about missing flux, bad self-cal on the 3mm data sets?
    • Working through problems on slack
4. Walk through imaging instructions (15 min)
5. If time allows, walk through self calibration instructions
6. Discussion of imaging issues related to specific images.


Minutes:
To be added during telecon

Questions:
  • Amplitude self-cal - safe or not?  When useful?
  • Which robust?
    • Pipeline produces -2, 0, 0.5, 2.  Future question: which is best?  Are they all needed?  Different for 7M+12M vs 12M?
  • Missing large-scale emission
    • Include larger scales in multiscale in imaging_parameters if needed and if doing 7m+12m

TODO: 
  • Examine flux calibration uncertainty: 
    • compare phase calibrator fluxes?
    • Other ideas?


Overview:
Our goal is to have at least first-pass joint 12m imaging (both 12m configurations) for each of B3 and B6 by mid-August.  We need to have the exact reduction script/process required to produce these files also.  These do not have to be final products, but they must be images that we can discuss and use to make further decisions. 

Please fill out information about which field you are working on in the "Continuum Imaging result" tab of this table:
https://docs.google.com/spreadsheets/d/1xM8AfiMpe8SVifqzl0lzjD10CEB2OlLIw_Kw5R1sxAM/edit#gid=1857654559

If you have FITS image files of the continuum already in hand, please share them with me and I will collect them in a single location for further inspection by the team or a subset of the team.







Notes added before telecon:
Add your region-specific notes below.  Bold the heading so they show up in the “Outline” view to the left.

Mosaic PSF * PB weighting:
From Dirk’s reply to the helpdesk ticket (July 2, 2019): “OK, I talked to the imaging development people yesterday again.
The fix is not going to be in 5.6 but probably in the version after that (5.7).

However, they assured me that this is only affecting the case nterms>=2 for mosaics and even there the Stokes I flux is OK.
Just the minor cycle alpha (spectral index) calculation is affected.

There is a known workaround: Use the PSF made from gridder='standard' with the joint mosaic. So, run niter=0 with gridder='mosaic'. 
Then run niter=0 with gridder='standard'. Then copy over the imagename.psf to the names for the imaging run for gridder='mosaic' and 
restart the mosaic run with calcpsf=False. 

So, I'll regard this ticket as resolved now.
I am told by the CASA people that there is nothing broken which could affect standard ALMA data analysis. “
Adam’s commentary: I don’t believe this affects the majority of our data sets significantly or perhaps at all, since our mosaics are symmetric.  This issue means that, when a model component is added at a particular position in the mosaic, there will be no flux removed on a minor cycle from points >0.5x(mosaic width) from the image.  However, the mosaic gridder already strictly enforces that each pointing has a pblimit=0.05, so this problem should have no perceivable effect on our imaging.  For long linear mosaics, particularly L-shaped ones, it could be a problem.


Adam’s notes on W51:
Self-calibration has been successful.  Clean with higher robust values (recovering more extended emission) has also worked reasonably well.  A full analysis of the self-calibration is available:
https://github.com/ALMA-IMF/notebooks/blob/master/W51E_B3_selfcal.ipynb
https://github.com/ALMA-IMF/notebooks/blob/master/W51-E_B6_Selfcal.ipynb 
(if these don’t load, try pasting the URLs in http://nbviewer.jupyter.org)

Notably, the B3 RMS dropped from ~0.31 mJy/beam (light cleaning, no self-cal) to ~0.08 mJy/beam (deep cleaning, self-calibrated).  The total dynamic range went from ~850 to ~5400.
pk/mad= 870.7, pk=0.266 Jy / beam, total=176.968 Jy / beam, mad=0.30520 mJy / beam, beam=0.29x0.26
pk/mad=2761.8, pk=0.409 Jy / beam, total=176.580 Jy / beam, mad=0.14800 mJy / beam, beam=0.29x0.26
pk/mad=3527.9, pk=0.410 Jy / beam, total=283.565 Jy / beam, mad=0.11623 mJy / beam, beam=0.29x0.26
pk/mad=4218.7, pk=0.410 Jy / beam, total=398.153 Jy / beam, mad=0.09730 mJy / beam, beam=0.29x0.26
pk/mad=4346.9, pk=0.410 Jy / beam, total=408.235 Jy / beam, mad=0.09442 mJy / beam, beam=0.29x0.26
pk/mad=4685.6, pk=0.410 Jy / beam, total=448.358 Jy / beam, mad=0.08757 mJy / beam, beam=0.29x0.26
pk/mad=5412.0, pk=0.409 Jy / beam, total=450.818 Jy / beam, mad=0.07561 mJy / beam, beam=0.29x0.26



In band 6, the results are similar but less extreme:
peak/mad= 904.2, peak=0.272 Jy / beam, rms=0.00069 Jy / beam, mad=0.00030 Jy / beam, 
peak/mad=1735.1, peak=0.449 Jy / beam, rms=0.00061 Jy / beam, mad=0.00026 Jy / beam, 
peak/mad=1897.5, peak=0.474 Jy / beam, rms=0.00048 Jy / beam, mad=0.00025 Jy / beam, 
peak/mad=1901.7, peak=0.473 Jy / beam, rms=0.00048 Jy / beam, mad=0.00025 Jy / beam, 
peak/mad=1904.9, peak=0.473 Jy / beam, rms=0.00048 Jy / beam, mad=0.00025 Jy / beam, 
peak/mad=1927.4, peak=0.474 Jy / beam, rms=0.00042 Jy / beam, mad=0.00025 Jy / beam, 
peak/mad=2390.9, peak=0.475 Jy / beam, rms=0.00026 Jy / beam, mad=0.00020 Jy / beam, 


These images show the ALMA data (left) and VLA data (right) and their difference modulo a scaling factor (center) to highlight the dust emission.  The 3 mm data have been self-calibrated with 5 iterations of phase-only selfcal and 2 of amplitude selfcal on the robust 0 data.  The robust 2 images involved additional “custom” cleaning.


Picture
Picture
Part of results from Hongli/Amelia (Two parts in total: the first part is obtained without any self-calibration, the second with self-calibration):
In a very short summary, our results were generated with the scripts in alma-imf repository. We have demonstrated them to be very practical, controllable, useful and resonable by applying to the source G353.41. In particular for the selfcalibration, to our experience, the phase calibration can improve the dynamical range of final images but not always significantly, depending on bands. For example, in the case of G353.41, band3 benefits a lot from self-calibration. However, the amplitude selfcalibration will get the flux decreased in both bands of G353.41, so we probably need to be very careful when doing the amplitude self-calibration.

Below are examples of images of several parameters (i.e., flux, model, residual, and mask) for two types of combinations (i.e., 12m, and 12m+7m) at the robust parameter 0 (I also have done for other robust parameters -2 0.5 2. BTW, what robust parameter(s) will be used at last) without selfcal  in both band 3.

Picture
Picture
Below are examples of images of several parameters (i.e., flux, model, residual, and mask) for two types of combinations (i.e., 12m, and 12m+7m) at the robust parameter 0 without selfcal in both band 6.
Picture
Picture
The same as above but with three-time iterations of phasecal for band 3

Picture
The same as above but with three-time iterations of phasecal for band 6

Picture
Picture
Picture
The list of several critical parameters (i.e., maximum flux, rms, dynamical range) for comparison of images between with and without selfcal. 
Note that we did six-times selfcalibration including four times on phase, and two times on amplitude. You will see below how the dynamical range and the maximum flux change with each calibration.

First, let’s look at the results of Band3 for source G353.41
#without self-cal
'G353_B3_12M_r0': {'max': '0.19 Jy / beam', 'rms': '0.39 mJy / beam', 'drange': '471.70'
#phase self-cal
'G353_B3_12M_r0_self1': {'max': '0.19 Jy / beam', 'rms': '0.32 mJy / beam', 'drange': '573.22', 
'G353_B3_12M_r0_self2': {'max': '0.19 Jy / beam', 'rms': '0.27 mJy / beam', 'drange': '676.93', 
 'G353_B3_12M_r0_self3': {'max': '0.19 Jy / beam', 'rms': '0.27 mJy / beam', 'drange': '692.39', 
 'G353_B3_12M_r0_self4': {'max': '0.19 Jy / beam', 'rms': '0.27 mJy / beam', 'drange': '693.12', 
# amplitude self-cal comes from below
'G353_B3_12M_r0_self5': {'max': '0.19 Jy / beam', 'rms': '0.28 mJy / beam', 'drange': '656.17', 
 'G353_B3_12M_r0_self6': {'max': '0.18 Jy / beam', 'rms': '0.28 mJy / beam', 'drange': '662.36',


Second, let’s look at the results of Band6 for source G353.41 
#without self-cal
'G353_B6_12M_r0': {'max': '0.11 Jy / beam', 'rms': '0.65 mJy / beam', 'drange': '167.18',
#phase self-cal
'G353_B6_12M_r0_self1': {'max': '0.10 Jy / beam', 'rms': '0.65 mJy / beam', 'drange': '158.66', 
‘'G353_B6_12M_r0_self2': {'max': '0.10 Jy / beam', 'rms': '0.61 mJy / beam', 'drange': '170.10', 
'G353_B6_12M_r0_self3': {'max': '0.10 Jy / beam', 'rms': '0.61 mJy / beam', 'drange': '171.05', 
 'G353_B6_12M_r0_self4': {'max': '0.10 Jy / beam', 'rms': '0.61 mJy / beam', 'drange': '171.10', 
# amplitude self-cal comes from below
 'G353_B6_12M_r0_self5': {'max': '0.10 Jy / beam', 'rms': '0.62 mJy / beam', 'drange': '167.04', 
 'G353_B6_12M_r0_self6': {'max': '0.10 Jy / beam', 'rms': '0.60 mJy / beam', 'drange': '164.84', 

Imaging_parameters required in the scripts from the alma-imf repository are below for the source G353.41
# for dirty (the first run)
imaging_parameters_nondefault = {
    #12M of band 3
    'G353.41_B3_12M_robust0': {'threshold': '1.8mJy', # 4*RMS 
                             'scales': [0,3,9],
                            },
    'G353.41_B3_12M_robust0.5': {'threshold': '2.0mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B3_12M_robust-2': {'threshold': '1.8mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B3_12M_robust2': {'threshold': '3.3mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    #7M12M of band 3
    'G353.41_B3_7M12M_robust0': {'threshold': '4mJy', # 4*RMS 
                             'scales': [0,3,9],
                            },
    'G353.41_B3_7M12M_robust0.5': {'threshold': '4.0mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B3_7M12M_robust-2': {'threshold': '4.4mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B3_7M12M_robust2': {'threshold': '4.8mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    #12M of band 6
    'G353.41_B6_12M_robust0': {'threshold': '2.2mJy', # 4*RMS 
                             'scales': [0,3,9],
                            },
    'G353.41_B6_12M_robust0.5': {'threshold': '1.5mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B6_12M_robust-2': {'threshold': '2.9mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B6_12M_robust2': {'threshold': '1.6mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    #7M12M of band 6
    'G353.41_B6_7M12M_robust0': {'threshold': '2.3mJy', # 4*RMS 
                             'scales': [0,3,9],
                            },
    'G353.41_B6_7M12M_robust0.5': {'threshold': '2.3mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B6_7M12M_robust-2': {'threshold': '3.12mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B6_7M12M_robust2': {'threshold': '3.3mJy', # 4*RMS
                             'scales': [0,3,9],
                            }

}

# for selfcal1, selfcal2,self....
imaging_parameters_nondefault_self = {
    #12M of band 3
    'G353.41_B3_12M_robust-2': {'threshold': '1.2mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B3_12M_robust0': {'threshold': '0.9mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B3_12M_robust0.5': {'threshold': '1.0mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B3_12M_robust2': {'threshold': '1.3mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    #7M12M of band 3
    'G353.41_B3_7M12M_robust-2': {'threshold': '1.8mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B3_7M12M_robust0': {'threshold': '1.6mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B3_7M12M_robust0.5': {'threshold': '1.6mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B3_7M12M_robust2': {'threshold': '2.1mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    #12M of band 6
    'G353.41_B6_12M_robust-2': {'threshold': '2.9mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B6_12M_robust0': {'threshold': '2.1mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B6_12M_robust0.5': {'threshold': '1.5mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B6_12M_robust2': {'threshold': '1.6mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    #7M12M of band 6
    'G353.41_B6_7M12M_robust-2': {'threshold': '3.0mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B6_7M12M_robust0': {'threshold': '2.2mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B6_7M12M_robust0.5': {'threshold': '1.7mJy', # 4*RMS
                             'scales': [0,3,9],
                            },
    'G353.41_B6_7M12M_robust2': {'threshold': '2.0mJy', # 4*RMS
                             'scales': [0,3,9],
                            },

}


Roberto’s notes on G333.60 B3: 
Adam visited Morelia during the spring and we set-up the imaging scripts to work for B3. The main result is that we have acceptable 12m-array (both configs), self-calibrated, robust 0 images. 
The continuum subtraction is for the “cleanest”, pipeline-defined continuum.  Selfcal significantly improved the noise and S/N in the images. Three iterations of phase- and one iteration of ap-selfcal were applied. I did some tests to try to automatize the cleaning windows using auto-multithresh without satisfactory results. In the end, self-cal was done through a scripted way but defining the cleaning masks by eye in a previous interactive cleaning, and saving them for posterior use. Details below. 

- Step 1: run continuum_imaging_both.py with interactive=True. I edited it to do only robust = 0, exclude 7m, and do only ‘cleanest’ continuum, since the ‘best sensitivity’ continuum option takes much longer to run. Since the purpose of this cleaning run was to define self-cal masks, I first cleaned shallowly the brightest central emission, and successively cleaned more exterior, fainter emission. The four ‘masks’ can be found in the link. I saved them in CASA in crtf format, but I think the scripts accept other formats, such as ds9 or a true mask image. Note that each successive mask includes the area in the previous one.  
https://www.dropbox.com/s/jh0j1et9owgb7zf/selfcal_masks_G333B3.tar?dl=0

- Step 2: run continuum_imaging_selfcal.py, editing imaging_parameters.py to include the name of the cleaning masks and the selfcal parameters for each iteration. The scripts that I used can be found in the link. Note that  imaging_parameters file is only for G333 (should rename without ‘G333’ for it to run), so I haven’t done this in the framework of including all the params for all targets in a single imaging_parameters file. 
https://www.dropbox.com/s/vruh2x0rosrxjr8/selfcal_scripts_G333B3.tar?dl=0

Results: 
Improvement over the selfcal iterations can be seen in the following animated gif: 


Picture
The non-pbcor fits images can be found here: 
https://www.dropbox.com/s/iom9d2mhit2l834/selfcal_fits_G333B3.tar?dl=0

Image        rms noise    Peak        S/N
        (mJy/b)    (mJy/b)       
No-selfcal    0.252        189        750
Selfcal p1    0.250        198        792
Selfcal p2    0.153        200        1307   
Selfcal p3    0.108        203        1880
Selfcal ap    0.079        194        2456

The final ap selfcal has 4% less peak flux than the previous phase-only selfcal, but looks better and has better S/N.  

- Step 3: do a final clean on the final selfcal ms. interactive=True should be better, but clean masks will be saved to be able to run it in a script. Question for telecon: do this within continuum_imaging_selfcal.py? Another script?

- Other tests: 

Roberto’s tests of automultithresh:
1) Played a bit with parameters of automultithresh. My conclusion is that one cannot get to something as good as the masks that I defined by eye. Below a comparison of an automatic mask (white pixels) with one of the by-eye masks (regions) used in the selfcal



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ALMA-IMF Telecon March 13, 2019

3/13/2019

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Minutes for ALMA-IMF telecon, March 13, 2019
Agenda: https://github.com/ALMA-IMF/reduction/wiki/ALMA-IMF-Data-Reduction-Telecon,-March-13,-2019 

http://desktop.visio.renater.fr/scopia?ID=722288***2252&autojoin


Attendees: Adam Ginsburg, Roberto Galván,Hongli Liu, Patricio Sanhueza, Nichol Cunningham, Jonathan Braine, Fernando Olguin, Brian Svoboda, Thomas Nony



Imaging results:
  • Roberto: G333 some problems with antenna list for ‘best sensitivity image’.  (continuum_imaging_template_3sigma.py, bsens images).  Needs corrections.

Picture
  • We may want to spcetrally average the “best sensitivity” data to speed up continuum imaging

  • Adam: https://github.com/ALMA-IMF/notebooks/blob/master/W51_B6_quicklooks.ipynb 
    • Automultithresh is probably not a good options
  • Liu Hongli:
  • B3 7M+12M combination (what field?):

Picture
B6: only 12M.  sidelobethreshold=2, noisethreshold=4.25 (from casaguide).  negativethreshold=1000
Picture
Picture
Discussion of imaging parameter saving:
https://github.com/ALMA-IMF/reduction/pull/10/files


Masking and thresholding discussion:
  • Patricio: prefer interactive because you can mask regions, force flux to a small sub-region, then once sidelobes are removed, expand the mask
  • Experiment to-do: try re-cleaning an image using the final mask
  • Roberto: maybe, if one could save in the viewer in interactive cleaning the mask that one uses in each cycle, one could save the masks and together with the info of the niter one could put that in a script 

This is what I used for line cleaning, some kind of auto-masking tclean. It worked quite well in IRDCs.
http://adsabs.harvard.edu/abs/2018ApJ...861...14C
(see also https://home.strw.leidenuniv.nl/~ycontreras/yclean.html -> https://home.strw.leidenuniv.nl/~ycontreras/yclean.py ) 

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ALMA-IMF Telecon March 6, 2019

3/6/2019

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Agenda: https://github.com/ALMA-IMF/reduction/wiki/ALMA-IMF-Data-Reduction-Telecon,-March-6,-2019
Minutes:
Participating: Adam Ginsburg, Frederique Motte, Patricio Sanhueza, Brian Svoboda, Ana Lopez, Jordan Molet, Thomas Nony, Sylvain Bontemps, Fernando Olguin, Roberto,  Hongli Liu
  1. General news and update
Mosaics in QA3 for imaging for a few months now. Some have been re-delivered. Others still in QA3: 5 SBs 12m array, 13 SBs 7m array
  1. Brief discussion of status spreadsheet (see link in e-mail)
    • region status
  2. Imaging status
    • Discussion of different scripts (Adam, Timea)
      1. https://github.com/ALMA-IMF/reduction/tree/master/reduction 
      2. E.g. https://github.com/ALMA-IMF/reduction/blob/master/reduction/continuum_imaging.py 
      3. Imaging scripts: suggested spectral averaging ~100 MHz?  Use maximum before bandwidth smearing becomes significant
    • What imaging results have been achieved?
      1. [Fernando] Some examples of imaging of G338 produced with the scripts: https://drive.google.com/open?id=1cJsCQqU-uhJQBSUOsouJ8BPIIUy3jr5V
      2. Roberto: in progress
      3. Adam: W51-E, See below
      4. Patricio: G327, See below
    • Is self-calibration necessary? If so, add that status to Continuum Imaging Results tab in spreadsheet
      1. Probably needed
      2. Andres Guzman working on selfcal of G10.62
    • Self-calibration process:
      1. Basic: tclean -> gaincal -> applycal -> tclean
      2. Difficulty w/mosaics?
        1. Look for pointings that do not contain bright emission to exclude from self-cal (LM - can solve on them but will not have a solution, only apply across from fields with ‘good’ solns)
        2. Potential problem with bright emission at edge of primary beam (maybe?)
      3. Are we concerned about astrometry?  Probably not yet.
    • Measuring image noise
      1. RMS in part of image where PB is “close to 1” in the pb-corrected map
      2. RMS of residual map?  I.e., take the RMS of the residual map in the area pb > 0.8 or some similar threshold
      3. Need comparison of different methods; enter these as different columns in the google spreadsheet
  3. TP baseline issues (Patricio)
    • CASA can’t handle it.
    • Flag out EBs with ripples?
    • Roberto: removed EBs w/ugliest ripples, re-ran pipeline, result looked good.  However, not much difference before/after
  4. Continuum subtraction (Jordan) - DELAYED til next week
  5. Conclusion: will meet next week


W51 examples from Adam, lines excluded:
Left: Before: no selfcal ; Right: after 1 iter selfcal
G327 from Patricio B6 lines excluded, no selfcal (selfcal is needed):
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