prototype astrometric imaging distortion calibration Critical Algorithm
Imaging: the distortion calibration algorithm is adopted as a Critical Algorithm for FDR. Deadline: FDR review I. AI-HB+GVK: the prototyped algorithms are:
determine the >=3rd order polynomial distortion map starting from a catalog of pinhole PSF positions (x,y,dx,dy). A pair of such catalogs is derived from the Cold and Warm Astrometric Masks observations. The source extraction procedure itself is not part of the prototyping. However, the catalog contains realistic centroiding errors (i.e., measurement uncertainties and systematic errors in the dx,dy) as a function of position in the Focal Plane Array.
Determine the 2nd+3rd order polynomial distortion map starting from a catalog of stellar PSFs. Such a catalog would be extracted from the science exposure on sky. Again, source extraction procedure not part of software prototyping, but catalog will contain realistic centroiding errors.
- read Riechert+18
- describe the astrometric model of MICADO in DRLD: 5.7.4 Astrometric Calibration Imaging
- get MASCADO operational: the code used for that paper and since evolved
- write Jupyter Notebook paralelling the Riechert approach but adapted to the MICADO case as skeleton of the software prototyping effort.
- start from catalogs. No centroid extraction prototyping (perhaps later on).
- determine realistic MICADO version of the needed quantities such as MASK pinhole locations, and PSFs, centroiding measurement errors and systematic errors
- Distortion determination procedure for first COLD MASK (and later repeated for WARM MASK):
- Read in (X,Y)_MP coordinates of pinholes in MASK PLANE coordinate system.
- Determine optimal affine transformation (is a 2x2 matrix M_MP_PP TBC) + (DX,DY)_MP_PP that converts MASK PLANE COORDINATE SYSTEM to PIXEL PLANE COORDINATE SYSTEM. 2a. (X,Y)_PP_predicted = M_MP_PP * (X,Y)_MP + (DX,DY)_MP_PP. 2b. Optimal affine transformation is least-squares solution, with chi-square of (X,Y)_PP - (X,Y)_PP_predicted.
- read in catalog from 2D gaussian fit no1 to pinhole PSFs on cut-outs of the pixels around the pinhole location in PIXEL PLANE COORDINATE SYSTEM predicted by the affine transformation
- Determine FWHM of circular 2D Gaussian of pinhole PSFs by averaging the FWHMs from gaussian fit no1.
- read in catalog with 2D gaussian fit no2 using fixed FWHM and store (X,Y)_PP in PIXEL PLANE COORDINATES for each pinhole. Determine for each pinhole I a distortion vector (DX,DY)_D_i=(X,Y)_PP_i - (X,Y)_PP_predicted_i .
- Determine vector field from pinholes vectors, expressing vector field as Legendre polynomial.
- iterate with J-Uwe on results from this initial Jupyter notebook prototype.
- decide best option for continuation with Hugo: better to continue with stand-alone python prototype in notebook first and then embed it in MicadoWISE or embed prototyping in MicadoWISE framework (to take advantage of its existing information on data items and pipeline infra).
- iterate on first results with J-Uwe and later also Ric and Davide
Nice to have:
- if time permits we can add image extraction prototyping: centroiding experiments on ScopeSIM simulations
- read all distortion calibration relevant articles:
- email thread with JUwe on distortion calibration prototyping