Synthetic PSFs

Synthetic PSF models

Atmospheric and lens-related blur is easily modelled, as its behaviour and effects on long exposure photography has been well studied over the decades. 5 subtly different models are available for selection via the 'Synthetic PSF Model' parameter;

• 'Gaussian' uses a Gaussian distribution to model atmospheric blurring.
• 'Circle of Confusion' models the way light rays from a lens are unable to come to a perfect focus when imaging a point source (aka the 'Circle of Confusion'). This distribution is suitable for images taken outside of Earth's atmosphere or images where Earth's atmosphere did otherwise not distort the image.
  
• 'Moffat Beta=4.765 (Trujillo)' uses a Moffat distribution with a Beta factor of 4.765. Trujillo et al (2001) propose in their paper that this value (and its resulting PSF) is the best fit for prevailing atmospheric turbulence theory.
  
• 'Moffat Beta=3.0 (Saglia, FALT)' uses Moffat distribution with a Beta factor of 3.0, which is a rough average of the values tested by Saglia et al (1993). The value of ~3.0 also corresponds with the findings Bendinelli et al (1988) and was implemented as the default in the FALT software at ESO, as a result of studying the Mayall II cluster.
• 'Moffat Beta=2.5 (IRAF)' uses a Moffat distribution with a Beta factor of 2.5, as implemented in the IRAF software suite by the United States National Optical Astronomy Observatory.

Only the 'Circle of Confusion' model is available for further refinement when samples are available. This allows the user to further refine the sample-corrected dataset if desired, assuming any remaining error is the result of 'Circle of Confusion' issues (optics-related) with all other issues corrected for as much as possible.

The PSF radius input for the chosen synthetic model, is controlled by the 'Synthetic PSF Radius' parameter. This parameter corresponds to the approximate the area over which the light was spread; reversing a larger 'blur' (for example in a narrow field dataset) will require a larger radius than a smaller 'blur' (for example in a wide field dataset).

The 'Synthetic Iterations' parameter specifies the amount of iterations the deconvolution algorithm will go through, reversing the type of synthetic 'blur' specified by the 'Synthetic PSF Model'. Increasing this parameter will make the effect more pronounced, yielding better results up until a point where noise gradually starts to increase. Find the best trade-off in terms of noise increase (if any) and recovered detail, bearing in mind that StarTools signal evolution Tracking will meticulously track noise propagation and can snuff out a large portion of it during the Denoise stage when you switch Tracking off. A higher number of iterations will make rendering times take longer - you may wish to use a smaller preview in this case.