dc2019/data/README_DC2019_data at master · urvashirau/dc2019 · GitHub

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Datasets we're working on (scripts is whole other matter)

1) M100 (from the casaguides)                Peter           [done]
2) NGC 346 (SMC observatory project)         Dirk/Thomas     [done]
3) skymodel GMC fractal/powerspectrum (Koda) Peter           [done]
4) Gaussian Models (Fomalont)                Ed
5) Protostellar outflow                      Adele           [work in progress]
6) MC/GMC - apex + alma	      	            Alvaro
7) LOFAR / HI                                Nickolas        [will bring on pen drive]


1) M100 from casaguides
   --------------------

The https://casaguides.nrao.edu/index.php/M100_Band3_Combine_5.4 casaguide walks you through
a feather combination. We will also try to provide this example for the other combination
methods.


To get the data, see also https://casaguides.nrao.edu/index.php/M100_Band3#Obtaining_the_Data

For EA use:  https://alma-dl.mtk.nao.ac.jp/ftp/alma/sciver/
    EU use:  https://almascience.eso.org/almadata/sciver/
    NA use:  https://bulk.cv.nrao.edu/almadata/sciver/

For 12m use directory:     M100Band3_12m
For 7m/TP use directory:   M100Band3ACA

you will need the 12,, 7m and TP data, e.g. in NA (replace the stem of the URL with the EU/EA ones)

  wget https://bulk.cv.nrao.edu/almadata/sciver/M100Band3_12m/M100_Band3_12m_CalibratedData.tgz
  wget https://bulk.cv.nrao.edu/almadata/sciver/M100Band3ACA/M100_Band3_7m_CalibratedData.tgz
  wget https://bulk.cv.nrao.edu/almadata/sciver/M100Band3ACA/M100_Band3_ACA_ReferenceImages_5.1.tgz

This totals 24GB.


We are also planning on a trimmed version of these big data to immediately jump into data combination.

TP2VIS: https://github.com/tp2vis/distribute/blob/master/example1.md   needs to be updated

2) NGC 346 (SMC observatory project)
   ---------------------------------

NGC 346 (ALMA#2017.A.00054.S), the Observatory Project with PI: Claudia Agliozzi.

This is a ~15' map with 7m and TP data, lots of structure, demonstrates the need for combinations.

Smaller data in : ftp://ftp.astro.umd.edu/pub/teuben/tp2vis/N346

[AI] Contact: Dirk + Thomas

3) skymodel GMC fractal/powerspectrum (Koda)

http://admit.astro.umd.edu/~teuben/QAC/skymodel.fits
http://admit.astro.umd.edu/~teuben/QAC/skymodel.ptg
http://admit.astro.umd.edu/~teuben/QAC/qac_bench.tar.gz

These are from the old 4096 x 4096 models. A newer 16k x 16k model,
and associate pre-computed MS, available here:

ftp://ftp.astro.umd.edu/pub/teuben/tp2vis/skymodel_16k.fits
ftp://ftp.astro.umd.edu/pub/teuben/tp2vis/tp2vis-sample2.tar.gz

4) Gaussian Models (Fomalont)


5) Lupus 3 mms, Protostellar outflow
   ----------------------------------

ALMA#2015.1.00306.S (PI: Adele Plunkett)

CO molecular outflow from Lupus 3 mms. Observations include 12m, 7m, and TP; here we test 12CO, Band 3.
This has been a relatively challenging dataset to "clean", even with interferometry only.
Certain channels behave better/different than others, due to structure, and sometimes emission near edges.
The quality of the image may depend on turning the right nobs in TCLEAN, in addition to the actual combination method.
But,certainly an interesting emission structure that is good for testing purposes.
[AI] Contact: Adele

6) MC/GMC - apex + alma


7) HI dataset (preparing for future radio surveys)

A group of HI astronomers registered for the workshop, expressing interest
in combining future interferometric and single dish HI surveys (e.g. SKA HI pathfinder experiments).
Kelley Hess and Nickolas Pingel are our contacts.  Nickolas suggests:
- ASKAP+Parkes HI cubes of the SMC. ASKAP cube of the SMC is considerable larger in size,
albeit at a velocity resolution of ~4 km/s.
- GBT+WSRT cubes of NGC 4631, full GBT and WSRT cubes are relatively small at ~500MB and ~50 MB, respectively.
The combination (using CASA) has already been published.

Nickolas will prepare sub-cubes, and bring on a portable drive, and/or upload directly.
He also offers: "In preparation for this workshop, I will redo the combination of the NGC 4631
data cubes utilizing the Faridani, CASA’s feather, and miriad’s immerge methods in hopes that
will provide a head start on developing such metrics with everyone at this workshop."