Image Analysis

• Recognising errors in the image plane
  • Expectations: how to decide whether an image is as good as it needs to be/can be.
  • Predicting the off-source noise level.
  • Signatures of phase and amplitude errors:
    • additive and multiplicative errors;
    • long and short timescales;
    • subtracting a model
  • Examples:
    • bad antennas, baselines, data-points,spectral channels;
    • interference;
    • confusion;
    • deconvolution problems (polarization errors in other lecture)
  • Remedial action
• Image Analysis
  • The importance of matched resolution and spatial frequency coverage
  • Image manipulation and combination; error propagation (1, 2 and many images)
  • Interpolation
  • Convolution
  • Deriving parameters from images:
    • (Gaussian) fitting and error estimation
    • Automated source-finding algorithms
    • Integrated flux densities; resolution, short-spacing and zero-level issues; summing clean components, integrating images, ....
  • Time variability; component-fitting and cross-correlation techniques for measuring motion
  • More sophisticated model-fitting (jet models as an example)
  • Getting data out of the standard packages and into your own code