LayerMCAO | Demonstrate how layer-oriented MCAO works

LayerMCAO is a Python library. LayerMCAO has no bugs, it has no vulnerabilities and it has low support. However LayerMCAO build file is not available. You can download it from GitHub.

LayerMCAO is a 2015 UROP project in the Cavendish Laboratory, Cambridge University supervised by Dr Aglae Kellerer. Its purpose is to simulate layer-oriented Multi Conjugate Adaptive Optics (MCAO). Layer-oriented MCAO is a new method of using MCAO. It differs from traditional star-oriented MCAO by optically conjugating not only the Deformable Mirror (DM) but the Wavefront Sensor (WFS) to height. Because the WFS is conjugated to height, ideally it should be able to sense high altitude turbulence without influence from the lower altitude turbulence. THe large field of view necessary on MCAO makes this technique ideal for solar astronomy. LayerMCAO is written in Python and currently implements three AO components: 1) Shack Hartmann Wavefront Sensor 2) Telescope 3) Atmosphere. Most of the work was done on the WFS, which contains the simulation methods to generate and interpret lenslet images. Telescope and Atmosphere objects largely contains the specifications and turbulence information needed by the simulation methods. Lenslet image generation and interpretation are kept as two seperate objects and referenced by the WFS object. The only information exchanged between the two classes should be the lenslet images. This keeps physical simulation of atmospheric seeing separate from post-processing methods. Multiple methods for interpreting lenslet images are implemented and contained in the ImageInterpretor class. The all\_dimg\_to\_shifts method specifies the default method. At the moment, only one routine for generating lenslet images is implemented in the ImageGenerator class.