Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Implement physically motivated PSF models #4

Open
AnthonyHorton opened this issue Jul 24, 2017 · 0 comments
Open

Implement physically motivated PSF models #4

AnthonyHorton opened this issue Jul 24, 2017 · 0 comments
Assignees

Comments

@AnthonyHorton
Copy link
Member

Currently the only PSF model is a Moffat function. With just 3 parameters (normalisation, width & 'shape') this can approximate real instrument PSFs pretty well, however there is no simple connection between the width & shape parameters of the model and physical parameters of the imaging system. This limits both the ability to predict the PSF of an instrument and the ability to infer information about an instrument from measured PSFs.

A rigorous approach to PSF modelling is beyond the scope of Gunagala, that's for end-to-end systems engineering models. Physically motivated PSF models can be introduced while retaining the parametric performance model nature of Gunagala, however. Components of these PSF models could include:

  1. Imager aperture -> Airy disc (generalised to include central obstruction)
  2. Kolmogorov/van Karman atmospheric turbulence spectrum -> seeing disc & wings
  3. Optical aberrations.
  4. Scattering from atmospheric aerosols.
  5. Scattering from optics.
  6. Tracking/guiding errors.

1-5 are wavelength dependent to varying degrees, some sort of band pass averaging will be required. For most instruments 1 and/or 2 will dominate, and simple models for 3, 4, 5 & 6 will be sufficient.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Projects
None yet
Development

No branches or pull requests

1 participant