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JWST/MIRI Imaging/Coronagraphy Flux Calibration

Code to measure flux calibration stars and compute the flux calibration for JWST/MIRI Imaging and Coronagraphic filters.

Main goal is to fully automate this work to allow for straightforward recalculations from the raw data to the final calibration factors for each filter. This enables tests of the sensitivity of the flux calibration to changes in the jwst pipeline or any of the steps in the flux calibration factor calculation.

Contributors

Karl Gordon

License

This code is licensed under a 3-clause BSD style license (see the LICENSE file).

Data

Observations are organized into three main directories for the three types of stars (HotStars, ADwarfs, and SolarAnalogs). In each directory, subdirectories for each filter (e.g., F1500W) have the raw (uncal) data. The automated reductions done in step 1 below create subdirectories in the filter directories for each observation for each star (set1, set2, ...).

Details

  1. Reduce data with jwst pipeline: pipeline_one_filter.py. Runs all three stages of pipeline for a one type of star and one filter. There are some differences from the defaults for MIRI. pipeline_all runs everything for all MIRI imaging/coronagraphic filters.

  2. Compute WebbPSFs: calc_webbpsfs.py. Runs WebbPSFs with specfic parameters for MIRI. Needs to be done for each filter. Critial for coronagraphy to set the normalize="exit_pupil" to ensure the WebbPSF is normalized to a total of 1.

  3. Compute encircled energies and individual observation aperture corrections. Uses calc_encircled_energy.py to compute the encircled energies Uses observations of a bright stars and PSFs from WebbPSFs to compute aperture corrections. Uses the bright star observations for the inner region and the WebbPSFs for the outer regions. calc_all_ees runs all MIRI imaging filters for a defined set of observations. This script varies the fwhmfac where the observed/webbpsf PSFs are merged. calc_all_ees_onlyneeded only runs for the fwhmfacs that are used in the apcor ref file and paper plots.

  4. Create the aperture correction reference file: create_apcor_reffile.py Uses the individual aperture corrections computed in step 3 to compute the aperture corrections with uncertainties (where possible) to create the apcor reference file in the correct format. Manually specified set of files that have been visual vetted from the results of step 3.

  5. Measure the flux in a fixed aperture: aper_one_filter.py. Measures the flux using an aperture and background annulus. aper_all.py does this for all MIRI imaging and coronagraphic filters.

  6. Compute the sensitivity loss using the repeatability observations of BD+60 1753. First run plot_repeatability.py to fit the observations with an exponential model for all the Imager filters. This produces data files with the fit coeffiecients. Then run generate_coron_repeat_from_imager.py to create similar fit coefficient data files for the Coronagraphic filters based on interpolating the Imager results based on filter pivot wavelength.

  7. Compute the model flux densities in all the filters: model_fluxes.py --grieke

  8. Measure the subarray dependence: plot_subtrans_allobs.py. Adjust the "Adopted" points manually till they match the scatter in the ratio of the calibration factors. Copy the values relative to FULL printed to the screen to the subarr_cor variable in the plot_calfacs function.

    Before this plot is made, the subarray dependence based on the dedicated subarray observations need to be measured by running plot_subtrans.py --filter=F770W and the same again for F1280W. In addition, the subarray dependence based on the ratio of calibration factors from all the observations needs to measured using plot_multi_calfacs.py --grieke --xaxisval=subarr --noignore.

  9. Compute the calibration factors: calc_calfactors.py Uses the results of 4, 5, and 6 to calculate the calibration factors for all observed absflux stars for one filter for all three types (if present). Produces a table giving the calibration factors for each observation. Produces plots of calibration factors versus model flux, time, well depth, etc. Example: calc_calfactors.py --grieke --applytime --subarrcor --nocurval

    A meta plot for all the filters can be created using plot_multi_calfacs.py.

  10. Compute a set of photom reference files accounting for the time dependent

sensitivity losses using create_photom_reffile.py

Figures

  1. Montage of observed imager PSFs: Plotting/plot_example_images.py
  2. Montage of observed coronagraphic PSFs: Plotting/plot_example_images.py --coron
  3. Encirciled energy plot: Plotting/plot_encircled_energy.py By default makes a figure showing the encircled energies for all 9 imaging filters, with a veritical offset between them. Call with --coron to get the coronagraphic plot.
  4. Example of photometry technique: aper_one_filter.py One of the standard outputs.
  5. Time dependent sensitivity loss: plot_repeatability.py
  6. Subarray dependence: plot_subtrans_allobs.py. plot_subtrans.py --filter=770W and plot_subtrans.py --filter=F1280W need to be run first to get the values for the dedicated subarray transfer observations.
  7. Source dependencies: calc_calfactors.py --filter=F1280W --sourcemulti --grieke --subarrcor --applytime --nocurval
  8. Source type dependence: plot_srctype_allobs.py
  9. Detector dependencies: calc_calfactors.py --filter=F1280W --detmulti --grieke --subarrcor --applytime --nocurval
  10. Change in delivered calibration versus time: Plotting/plot_del_photom_vs_time.py

Appendix Figures:

plot_multi_calfacs.py --xaxisval=mflux --grieke --subarrcor

Replace mflux with desired xaxis value.

Tables

1, 2, 3. Observation details: Tables/create_obstable.py
Output to screen.
  1. Aperture corrections: create_apcor_reffile.py Portion of output to the screen.
  2. Output from creating Subarray dependence.
  3. Output from create_photom_reffile.py.