Small-Grid Dithers
This is my scripts used to generate PSF, add noise to them, apply the LOCI algorithm, and calculate the azimuthal average and contrast curves. The first three steps were originally written in Mathematica code and I have translated (and made them run faster) into Python.
The overall procedure is summarized in the Figure below and the Python scripts below are used sequentially to arrive at the final average 5-sigma contrast curves.
Note that some input data to the scripts below such as filter transmission, QE curves, and/or background noise levels as well as Brian York's JWST Simulator python modules (for AddNoisetoPSF.py) will only be provided upon request.
1. Generate_PSFs.py
Generate PSF grids using WebbPSF.
The idea here is that we generate one science target PSF using the expected target acquisition accuracy, then simulate the acquision of another reference PSF that we dither behind the coronagraphic mask, following a small square grid pattern. Typically, the square grids have 9 points and the grid step is 20 mas, although both can be modified on the command line (see below). The PSF are all generated using WebbPSF.
Command-line arguments (-- for optional):
| Argument | Name | Description |
|---|---|---|
| -h, --help | Show this help message and exit | |
| -I | INSTRUMENT | Instrument to use (MIRI or NIRCam) |
| -f | FILTER | Filter to use |
| -mask | MASK_CORON | Mask coron. to use |
| -stop | PUPIL_STOP | Pupil stop to use |
| --rms | RMS | Select OPD rms to use, if available (default: 136 nm) |
| --noopd | Do not use any OPD (default: False) | |
| --jitter | JITTER | Sigma Jitter (default: 0 mas) |
| --fov | FOV | Field of view (diam.; default=7.04 arcsecond) |
| --nruns | NRUNS | Number of SGD grids to generate (default: 1) |
| --gstep | GSTEP | SGD grid steps (default: 20 mas) |
| --gnpts | GNPTS | SGD square grid points (default: 9) |
2. AddNoisetoPSF.py
Add simple noise sources to pre-generated PSFs. Included are approximations for stellar photon noise, background photon noise, detector noise (readnoise, dark noise), as well as OTE throughput and detector quantum efficiency. The script scales the original PSF (output of generate_PSFs.py) according to the user's inputs regarding the stellar spectrum, distance, and exposure time and saves the output images in a separate folder.
Note that the image size for MIRI and NIRCam (Short wave & Long wave) are hardcoded in the function "prepareSpectrum". The default values are 64, 109, and 222 pixels across for MIRI, NIRCam short wave, and NIRCam long wave, respectively. The user may need to change these values accordingly.
Command-line arguments (-- for optional):
| Argument | Name | Description |
|---|---|---|
| -h, --help | show this help message and exit | |
| -I | Instrument | JWST instrument |
| -f | FILTER | filter to use |
| -step | STEP | dither grid step size (default: 20 mas) |
| -jitter | JITTER | sigma Jitter (default: 0 mas) |
| --webbpsf | use if images were created with WebbPSF | |
| --t | T | effective temperature (default: Solar T=5800 K) |
| --z | z | metallicity (default: Solar z=0) |
| --g | G | surface gravity (default: Solar log_g=4.44) |
| --R | R | radius of star (default: 1 Rsun ) |
| --dist | DIST | distance to star parsec (default: 10 pc) |
| --exptime | EXPTIME | exposure time (default: 1 sec) |
| --run | RUN | run number (default: all) |
| --vega | use Vega spectrum (default: False) | |
| --clobber | overwrite output files (default: False) | |
| --nonoise | to turn off noise sources (default: add noise) | |
| --rms | RMS | select which OPD rms to use, if available (default: 400 nm) |
| --planets | Inject HR8799 planets (default: False) | |
| --roll | Roll the planets clockwise in degrees (default: 0 deg.) |
Note: the --webbpsf flag is used for MIRI PSF generated with WebbPSF so that the Quantum efficiency as well as the Germanium and OTE transmission profiles can be applied. For MIRI PSF create with Mathematica or NIRCam PSF generated with WebbPSF, this is not necessary as these contributions are taken into account in the filter profiles included.
3. (ApplyLOCI.py)
See https://github.com/Skyhawk172/PythonLOCI for now...
4. AzimuthalAverage_SGD.py
Ingests the all outputs "Maps" of ApplyLOCI.py and calculates an average 5-sigma contrast curve along with a +/- 1-sigma range.
Contrast is simply defined as the...