5.5  Description of Modules in the 2-Dimensional Coadder Pipeline

This pipeline consists of only one module, COADD2D, and is considered an ensemble pipeline, because multiple input bcd.fits files are combined. The pipeline selects all BCDs of the same exposure (EXPID) within an AORKEY, to form the list of files to be combined.

5.5.1             COADD2D

The module COADD2D (“2-Dimensional Coadder”) computes the asymmetric trim mean of several input files bcd.fits, whose names are listed in the text file inputlist-coadd. Other inputs to the module are the corresponding uncertainty files func.fits and BCD masks bmask.fits, respectively listed in the text files inputlist_unc_coadd, and inputlist_bmask_coadd.

The “asymmetric trim mean” algorithm is carried out for each pixel, independently of other pixels. Asymmetric trimming consists of successively discarding pixel values at the upper or lower extremes of the set that will be averaged. The algorithm details are as follows:

Denote by pix_i (i=1...N) the set of N pixels to be trim-averaged, where N is the number of BCDs being co-added. The asymmetric trim fraction parameter f_asym, also denoted as “Cut-off Fraction” (with default value 0.2) determines the maximum number N_asym of pixel values pix_i that are discarded at the extremes of the set: N_asym  = N_pix f_asym.  That is, using the default for f_asym up to 20% of pixel values may be discarded. The iterative process for discarding extreme pix_i consists of four steps:

1. Compute median(pix) of the set pix_i, where . 

2. Determine which of the two extreme values (the highest or the lowest) of the set pix_i deviates more from median(pix); denote it as P_ext, and its absolute deviation from median(pix) as D_ext.

3. Excluding P_ext, compute the median absolute deviation D_med of the set pix_i from median(pix).

4. If D_ext <m_cut D_med,  where the parameter m_cut also known as “Cut-off Multiple,” has a default value of 5.0, then terminate the iteration and compute the mean of the remaining set pix_i. Otherwise, discard P_ext from the set pix_i; if fewer than N_asym pixels have been discarded through these iterations, go back to step 1 and continue. Else, if N_asym pixels have been discarded from the set, terminate the iteration and compute the mean of the remaining set.

In the main output of the module, coa2d.fits, pixels are set to NaN whenever there are no non-NaN values in the trimmed set pix_i. Equivalently, output NaN values result whenever all pixels in the trimmed set are fatally masked in their respective bmask.fits or pmask.fits files. The inter-order regions are NaN in coa2d.fits.

The output uncertainties in c2unc.fits are the sum in quadrature of the input uncertainties in func.fits for the trimmed set, divided by the number of pixels in the trimmed set. Weights are not used in this procedure.

The output mask file c2msk.fits has bits # 12 and 13 set whenever there are no values in the trimmed set pix_i that are not NaN. Bit # 7 is set for pixels tagged in the input bmask.fits as “questionable flatfield applied.”

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