Reduction Strategies

Here we give an overview of the reduction process, and review some of the basic decisions that you will have to make during the reduction of the data. We now consider some questions that you should ask yourself before the reduction process.

Figure 7.1: ATCA Data Reduction Strategy
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Figure 7.1 gives a flow chart of the normal steps involved in reduction of ATCA data. Note that this is somewhat abbreviated by necessity - additional flow-charts in subsequent chapters give more detail and describe some of the variations.

We will now consider each step in the flow-chart in turn.

  1. Data from the ATCA is written in ``RPFITS'' format - an ATNF-specific format - and a special task (atlod) is needed to read it. Loading your data into Miriad is described in Chapter 8.

  2. Data editing (flagging) can be very time consuming, especially if you do are affected by interference. The Miriad flagging tasks are described in Chapter 10.

  3. The actual calibration steps can be the most confusing steps, as there are a large variety of paths that can be followed. Only the most frequently trodden paths are described in Chapter 12.

  4. The task invert takes a visibility dataset and forms either a single image or an image cube (for spectral-line observations). It also produces a point-spread function (dirty beam), ready for deconvolution. See Chapter 13

  5. The main deconvolution tasks are clean, maxen and mfclean. Task clean, which is the most commonly used, attempts to decompose your image into a number of delta functions. It can be slow for images with a large amount of extended emission. An alternative to clean is maxen, a maximum entropy-based deconvolution task. It tends to be less robust and more difficult to run correctly. A third alternative is mfclean. This is a derivative of the CLEAN algorithm, and simultaneously determines a flux density and spectral index image. It is only appropriate for multi-frequency synthesis experiments when more than one observing band has been used.

    The deconvolution tasks are described in Chapter 14.

  6. As noted above, self-calibration is useful for determining antenna gains directly from a strong program source. It is described in Chapter 15.
  7. The deconvolution tasks produce an output image that is in units of flux density per pixel. That is, the outputs are CLEAN component images. Task restor converts these to flux density per CLEAN beam, and adds back the residuals. This is covered in Chapter 14.
  8. At last, you are ready to display and think about your images!

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