Recipe for Linear Polarization reductions. Basic version

Steps for reducing polarized data taken in the 'Linear-Hi' setting with Robert Stobie Spectrograph on the Southern African Large Telescope. These are a description of the steps currently carried out by the reducepoldata.py script. Currently the script has been verified to work with the red PG0900 observation. These steps aren't necessarily what is needed for reduction of the data but are the steps run and required by the script.

Basic CCD reductions (imred.py)

Basic CCD reductions include overscan subtraction, gain correction, crosstalk correction, cosmic ray cleaning, and geometric corrections. All of these steps can be run individual in the pysalt package.

Creation of a wave map (specpolwavmap.py)

The wave map is a 2-D map between pixel position and wavelength. The program currently does the following steps:

• Identify sets of data associated with each other. These data should have the same configuration, taken on the same track, and have one arc associated with them.
• Split the data into the O+E bean. A file called wollaston.txt contains the predicted pixel position of the middle of the O+E beams as a function of wavelength. This value can then be used to estimate the row to split the images according to the configuration.
• The steps below are run each on the O and E images:
• Correct the image for distortion introduced by the beam splitter. This uses the data in the wollaston.txt file to estimate how much the image should be shifted to correct curvature introduced by the beam splitter.
• Wavelength identify the arc image. Wavelength identification needs to be carried out manually on a single row. Then the program needs to automatically identify other rows in the image.
• Create a wavelength map. This step uses the wavelength solution to determine the wavelength at every good pixel. This step should also map out bad areas in the wavelength map either due to being off the edge of the slit or areas of overlap.
• Apply the correction from the wollaston data to the wavemap so that it now has the same, original curvature of the data.
• For each observation in the data set, apply the following corrections:
• Split the data into the O+E beams
• Add the wavemap to image
• Update the mask for bad areas.

Extract the data (specpolextract)

• Find all configurations that are the same
• Sum all images of the same configuration
• Run specpolsignalmap on summed image
• create a psf image
• create a skyflat image
• create a mask of bad areas including littrow ghost
• creata continuum map? isbkgcont_orc ?
• maprow_od?
• return shift due to beam splitter distortion
• set up wavelength binning
• scrunch psf?
• Correct each image
• background subtract the image
  • use 2d background subtraction
• extract spectra
  • correct for guiding errors
  • use optimal extract
• write out 1D spectra

Create the raw stokes file (specpolrawstokes.py)

The input file is a FITS file containing a 1D extracted spectrum with an e and o level includes the intensity, variance, and bad pixels as extracted from the 2D spectrum.

For each pair of stokes measurements, it produces an output FITS file now with two columns that are the intensity and difference for the pair measured as a function of wavelength and also includes the variance and bad pixel maps. The output file is named as the target name_configuration number_wave plate positions_number of repeats.fits

Create the final stokes file (specpolfinalstokes.py)

Combine the raw stokes and apply the polarimetric calibrations

Plot the data (specpolview.py)