Advanced Data Combination in the ALMA era

Supervisor: Alvaro Hacar

Contact information: alvaro.hacar@univie.ac.at 

Co-supervisor: Francesca Bonanomi

Expected duration: 9 months

 

Project description & Goals:

The enhanced sensitivity and resolution of ALMA has started a new era for Milky Way studies. However, these new ALMA observations face a fundamental challenge: the recovery of extended emission due to the lack of zero-spacing information. Overcoming this problem requires the combination of interferometric plus single-dish observations (see Plunkett et al 2023). In a recent study (Bonanomi et al in prep.), we explored the application of state-of-the-art data combination techniques for the different ALMA arrays (12m+ACA+TP) using simulations. Our results demonstrate not only the need of data combination to obtain high-fidelity ALMA images in ISM studies but also the critical differences when combining large single-dish data compared to different ALMA arrays. Extending these simulations, in this Masters project we will explore and quantify the performance of different advanced data combination techniques using real ALMA science data. With fundamental implications for the future of ALMA, our goal is to create an optimal framework (e.g., optimal baseline coverage, sensitivity, beam-shape, etc...) for data combination that can be used in current and future ISM projects.

 

 

Working plan & Milestones (including final thesis):

  1. Get familiar with the literature work on ALMA arrays and advanced data combination techniques
  2. Carry out different CASA tutorials (ALMA)
  3. Explore current and new assessment techniques for data combination
  4. Compare and quantify data combination results using ALMA 12m + large single-dish vs ACA+TP arrays
  5. Compare and quantify data combination results using different ALMA configurations
  6. Derive optimal framework for data combination
  7. (optional) Explore beam-shaping techniques for data combination

Requirements / special skills (optional): Experience with molecular observations and python is recommended. This is a technical project that requires intensive programming skills

References (optional):

- ui.adsabs.harvard.edu/abs/2023PASP..135c4501P/abstract
- ui.adsabs.harvard.edu/abs/2018A%26A...610A..77H/abstract