DataCombination team activities - Interferometer and single-dish data combination

Most of the areas of study in astronomy, such as star formation, the study of extragalactic structures, the evolution of stars and planetary nebulae, depend on high spatial dynamic range observations that can provide information on both the diffuse, extended emission and the dense, more localized emission where astrophysical processes happen. In the last decade, facilities such as the Atacama Large Millimeter/submillimeter Array (ALMA), which has opened a new window to the study of the 'Cold Universe', aim at providing extremely high fidelity images by combining observations at high-spatial resolution (usually interferometers) with lower-spatial resolution (usually single-dish telescopes) that can recover the extended emission. The DataCombination team aims at testing different methods for the combination of high and low-spatial resolution images, with the goal of providing the community with a guide on how to combine interferometric and single-dish data.

In this page we will report the activities within the DataCombination team. We aim at discussing different combination methods and report on the results. We provide links with different tests, tutorials and guidelines.

Example of data combination. The left panel shows the image obtained with the single-dish telescope (NRO-45m), the central panel shows the image obtained with an interferometer (CARMA), and the right panel shows the combined image (CARMA+NRO45m). The final combined imaged contains the small scale structures visible with an interferometer together with the large-scale emission that only the single-dish telescope can detect. These images show the CO(1-0) line towards the Orion A molecular cloud (from Kong et al. 2018, ApJS, 236, 25)

​​If you are interested in joining the DataCombination team, contact Alvaro Sanchez-Monge (email: sanchez <at> ph1.uni-koeln.de)
Send a short description of your interests and your experience in data combination.

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Team members

The DataCombination team includes a number of people with experience in data combination all over the world. In alphabetical order the team members (and their affiliations) are:

- Sandra Burkutean (Italian ARC node, Italy)
​- Yanett Contreras (Allegro ARC node, Netherlands)
- Ed Fomalont (JAO/NRAO, Chile)
- Adam Ginsburg (NRAO, USA)
- Daniel Harsono (Allegro ARC node, Netherlands)
- Melissa Hoffman (NRAO, USA)
​- Jin Koda (Stony Brook University, USA)
- Hauyu Baobab Liu (ESO, Germany)
- Ivan Marti-Vidal (Nordic ARC node, Sweden)
- Lydia Moser (German ARC node, Germany)
- Dirk Petry (ESO, Germany)
- Adele Plunkett (ESO, Chile)
- Alvaro Sanchez-Monge (German ARC node, Germany)
- Tsuyoshi Sawada (JAO/NAOJ, Chile)
- Thomas Stanke (ESO, Germany)
- Daniel Tafoya (NAOJ, Japan)
- Peter Teuben (University of Maryland, USA)
- Catarina Ubach (NRAO, USA)
- Ke Wang (ESO, Germany)

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Data combination methods

There are three main methods for data combination between an interferometer and a single-dish telescope. 

• Joint deconvolution 
  In this method, both the interferometer and single-dish data are combined in the so-called uv plane (or visibility space). For this, it is necessary to create visibilities for the single-dish. Some publications that have used this method are:
  Koda et al. 2011, ApJS, 193, 19
  Kong et al. 2018, ApJS, 236, 25
  
  
• Feather
  This method makes use of two individually created images for the single-dish and interferometer telescopes. The feathering technique combines the two images in the Fourier transform plane. Some publications that have used this method are:
  Cotton 2017, PASP, 129, 094501
  
  
• Model-assisted cleaning
  This method uses the single-dish image as a model when performing the cleaning (generation of interferometric image) of the interferometric data. The use of a model usually helps in the convergence of the interferometric image. As a second step, the single-dish image and the interferometric image (produced with the single-dish as a model) are combined using the feathering technique. Some publications that have used this approach are:
  Hacar et al. 2018, A&A, 610, A77
  

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Useful links and documentation

While this is not a complete list of links and documents, it is a starting point to collect different tests and methods/approaches considered in the process of data combination. If you know of some other webpage or documentation that can be useful for this purpose, please contact sanchez <at> ph1.uni-koeln.de

• First CASA guide to show a demonstration of data combination using ALMA data of the M100 galaxy. The approach considered uses the feathering technique, since the joint deconvolution was not originally implemented in CASA. The CASA guide has been prepared for version 4.3, and can be found here
• The project TP2VIS aims at using the joint deconvolution technique within CASA. It is been developed in the NRAO node of ALMA, and more information can be found here
• The project SD2VIS was developed simultaneously to the TP2VIS project in the Nordic ARC node in Europe. It also makes use of the joint deconvolution technique, and it can be obtained from here
• Adam Ginsburg has developed the uvcombine python package, which makes use of the feathering technique to combine data. Details on the package and the package itself are found here
• Jens Kauffmann provides some instructions on how to add zero-spacing data to your interferometric image by using the model-assisted approach. The details can be found here
• The SD2018 workshop organized by Peter Teuben aimed at explaining different aspects of single-dish telescope observations as well as data combination. The material used for the workshop can be found here

Some documentation of different tests is listed in the following:

• Adam Ginsburg has a blog with a number of tests performed in data combination. For the specific tests see here and here. The link to the main page of the blog is here
  Other useful material:
  - finding scaling factor between single-dish and interferometer data, check here
  - detailed tests for feathering using simulated data, check here
  
• Different tests on the different data combination methods have also been conducted by Miles Lucas.
  He has developed RICA (Radio Imaging Combination Analysis) which is a tool to measure the effectiveness of different methods. More details can be found here, and here

For team members, the sandbox repository for testing is located here