Deconvolution modes¶

  • Masks and auto-masking
    • Providing a mask
    • Auto-masking
    • Combining auto-masking with a regular mask
    • Specifying an absolute auto-masking threshold
    • Availability
    • References
  • Local RMS thresholding
    • Auto-generated RMS map
    • Using a pre-existing RMS map
    • Local-rms strength
  • Wideband deconvolution
    • Usage
    • Multiple measurement sets and subbands
    • Fitting smooth spectra
    • Forced spectrum fitting
    • Fit normal or logarithmic polynomials?
    • Avoiding “steps” in the model data visibilities
    • Relation to CASA’s multi-term deconvolution
  • Polarimetric deconvolution
    • Linking polarizations
  • Rotation-measure (RM) synthesis
    • Basic RM-synthesis imaging
    • Using the full bandwidth for cleaning QU cubes
    • What is calculated?
    • Availability
  • Multi-scale cleaning
    • Introduction
    • Scale kernel shape
    • Bias parameter
    • Multi-scale with multi-frequency or polarimetric cleaning
    • Masks
    • Adaptive-scale pixel algorithm
    • References
    • History
  • IUWT compressed sensing
    • Relation to MORESANE
    • Usage
  • MORESANE deconvolution
    • Using MORESANE
    • History
  • Parallel deconvolution
    • Implementation
  • Direction-dependent Point Spread Functions (PSFs)
  • Implementing custom strategies in Python

WSClean

Navigation

  • Getting started
  • Weighting
  • Imaging modes
  • Gridding modes
  • Deconvolution modes
    • Masks and auto-masking
    • Local RMS thresholding
    • Wideband deconvolution
    • Polarimetric deconvolution
    • Rotation-measure (RM) synthesis
    • Multi-scale cleaning
    • IUWT compressed sensing
    • MORESANE deconvolution
    • Parallel deconvolution
    • Direction-dependent Point Spread Functions (PSFs)
    • Implementing custom strategies in Python
  • Results & problems
  • Technical details
  • Further information

Related Topics

  • Documentation overview
    • Previous: W-gridding
    • Next: Masks and auto-masking
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