Table of Contents

Dealing with low signal to noise in your bandpass and/or phase calibrator really just means doing extra averaging before finding bandpass/gaincal solutions. Here are some suggestions to improve the signal to noise in your observations, though at the cost of losing some information (fine changes in time/frequency/polarization).

The data reduction script generator has a flag that you should use for calibrating low signal to noise data sets. When running generateReducScript, use the lowSNR option. This will increase the fraction of the bandwidth used doing the gaincal pre-bandpass.

Bandpass Calibrator

If your bandpass calibrator is too faint to get a good channel-to-channel solution for amplitude and phase, you have two options
  • Try to use your phase calibrator
    • In most cases, your bandpass calibrator is brighter than the phase calibrator, but more time is spent on source for the phase calibrator
      • This is unlikely to work, but it's easy to test and worth the attempt
  • Try using your bandpass calibrator, but average over (more) channels
    • For instance, try solint='inf,128ch'
    • For mixed mode data, it is preferable to specify the averaging in frequency units. Use the function: au.bandpassPreAverage(''), which will compute the amount of pre-averaging to perform based on the source flux density, system temperature and time on source. - T. Hunter

Phase Calibrator

To deal with low signal to noise in observations of the phase calibrator, we can average over some combination of time, frequency, and polarization.
  • You may be able to get away with only changing the time averaging in gaincal
    • Instead of using integration length solutions (solint='int'), use scan length solutions (solint='inf')
  • If your phase calibrator is very faint, it may be necessary to combine spectral windows during 'p' solves in order to bring up its signal-to-noise enough to be usable.
    • In the gain calibration step (usually #13 or 15), insert a new gaincal to derive a phase_offsets table containing the spectral window phase offsets using your (bright) bandpass calibrator scan with
      • gaintype = 'G'
      • calmode = 'p'
      • solint = 'inf'
      • gaintable = '(bandpass table)'
    • Be sure to include this phase_offsets table anywhere that the spectral windows will be combined
    • Note that the resulting values in the .fluxscale file for the narrow spws may be biased upward by a large factor (x2), but they are not subsequently used as long as you setup the later applycal commands correctly. - T. Hunter (4/2015)
    • If the flux calibrator is also too weak in the narrow windows, then you will need to set refspwmap in the fluxscale command. - T. Hunter (4/2015)
    • The spectral windows can be combined by in gaincal using
      • combine='spw'
      • solint='inf'
      • The resultant solution will be placed in spectral window 0
        • In future calls to gaincal and applycal, you will have to use spwmap to have these solutions applied to the other spectral windows (for example: spwmap=[[],[],[0,0,0,0]] when applying bandpass, phase_offsets, and a combined-spw-solution).
        • In the applycal calls, you will also have to set gainfield appropriately (for example: gainfield=['','0','2','2'] for the bandpass, phase_offsets, phase_inf and flux_inf tables to the science target)
* It is essential to restore the BeforeGainCalibration flags if you have previously run applycal with solutions that were mostly flagged. Therefore, it is good practice to insert a flagmanager(mode='restore') at the beginning of Step 15 before you starting editing the gaincal and applycal calls, so that you do not forget to do this restore.
  • You can also try averaging together the two polarizations
    • In gaincal, use gaintype='T' in your phase-only solutions (it is already used in amplitude-only solutions)
      • The phase offset between the two polarizations must be removed, as above, with a call to gaincal on the bandpass calibrator

-- ScottSchnee - 2013-02-07
Topic revision: r9 - 2015-08-10, ToddHunter
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