Improving Image Fidelity on Astronomical Data: Radio Interferometer and Single-Dish Data Combination (DC2019)

This was the material we used for the DC2019 workshop. We planned to have all scripts, code, documentation, and presentations here, but our data are typically large and we will supply URLs from where you can download them from. Some scripts were taking from other projects, and may not be maintained here so well.

A conference summary is being prepared, which is accompanied by script4paper (eventually those will be located in a different github repo).

Combination Techniques

We will probably try out a few techniques:

• feather talk by Ginsburg; see also the uvcombine package
• tp2vis [talk by Teuben]
• hybrid [talk by Kauffmann]
• sdint [talk by Rau]
• ...

Software needed

Since we are going to be CASA based this week, we only support Linux and Mac. WSL on Window10 is currently too slow to be useful (but hasn't been tested).

• CASA: https://casa.nrao.edu/casa_obtaining.shtml. The current release is 5.5, but 5.6 was just released. We have helper scripts in contrib.
  • [https://casa.nrao.edu/casadocs/casa-5.6.0/introduction/release-notes-560](5.6.0 Release notes)
  • [https://casa.nrao.edu/casadocs/casa-5.6.0/introduction/release-notes-560[(5.5.0 Release notes)
  • CASA 6 can also be installed via pip wheels. We also have a helper script.
• QAC (this will also install TP2VIS, SD2VIS, SDINT and AU)
• TP2VIS (comes with QAC)
• SD2VIS (optional, can come with QAC)
• AU (comes with QAC)
• WidebandSDINT (comes with QAC) - as of July 2020 CASA 5.7 has this included as sdintimaging()

and recommended software

• DS9: http://ds9.si.edu/site/Home.html (the XPA tools can also be very useful)
• vanilla python3 via miniconda or anaconda (an install script is available in dc2019, but you may also be able to install modules in CASA's python)
• a spectral cube fits viewer (ds9, carta, casaviewer, QFitsView, glue). See also https://fits.gsfc.nasa.gov/fits_viewer.html

Data needed

see README_DC2018_data for more details

We should have a USB and portable HDD during the meeting for copying large datasets, but we strongly recommend you come prepared with the data loaded on your laptop. We hope to have an estimate for the minimum amount of space you will need for the experiments, and/or bring your own spare external HDD. It will probably be closer to 100GB than 10GB.

Installation

See the INSTALL file for some current guidelines.

Paper

Starting at the tag "draft1" we have moved the "scripts4paper" to a fresh github repo called DataComb