From fa080de7afc95aa1c19a6e6fc0e0708ced2eadc4 Mon Sep 17 00:00:00 2001
From: Joseph Hunkeler <jhunkeler@gmail.com>
Date: Wed, 8 Jul 2015 20:46:52 -0400
Subject: Initial commit

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+.help apvariance Aug90 noao.twodspec.apextract
+
+.ce
+Variance Weighted and Cleaned Extractions
+
+
+There are two types of aperture extraction (estimating the background
+subtracted flux across a fixed width aperture at each image line or
+column) in the APEXTRACT package.  One is a simple sum of pixel values
+across an aperture.  It is selected by specifying "none" for the
+\fIweights\fR parameter.  The second type weights each pixel in the sum
+by it's estimated variance based on a spectrum model and detector noise
+parameters.  This type of extraction is selected by specifying
+"variance" for the weighting parameter.  These two extractions are
+defined by the following equations.
+
+.nf
+	none:	S = sum { I - B }
+    variance:	S = sum { (P**2 / V) (I - B) / P } / sum { P**2 / V }
+.fi
+
+S is the one dimensional spectrum flux at a particular wavelength (line
+or column along the dispersion axis).  The sum is over all pixels at
+that wavelength within the aperture limits.  If the aperture endpoints
+occupy only a fraction of a pixel then the pixel value above the
+background is multiplied by the fraction.  I is the pixel value and B
+is the estimated background at that pixel (see \fBapbackground\fR), P
+is estimated normalized profile value for that pixel (see
+\fBapprofile\fR), and V is the estimated variance of the pixel based on
+the noise model described below.  Note that the quantity (I-B)/P is an
+independent estimate of the total flux from one pixel since the
+integral of P is one and it is these estimates that are variance
+weighted.
+
+Variance weighting is often called "optimal" extraction since it
+produces the best unbiased signal-to-noise estimate of the flux in the
+two dimensional profile.  The theory and application of this type of
+weighting has been described in several papers.  The ones which were
+closely examined and used as a model for the algorithms in this
+software are "An Optimal Extraction Algorithm for CCD Spectroscopy",
+PASP 98, 609, 1986, by Keith Horne and "The Extraction of Highly
+Distorted Spectra", PASP 100, 1032, 1989, by Tom Marsh.
+
+The noise model for the image data used in the variance weighting,
+cleaning, and profile fitting consists of a constant gaussian noise and
+a photon count dependent poisson noise.  The signal is related to the
+number of photons detected in a pixel by a \fRgain\fR parameter given
+as the number of photons per data number.  The gaussian noise is given
+by a \fIreadnoise\fR parameter which is a defined as a sigma in
+photons.  The poisson noise is approximated as gaussian with sigma
+given by the number of photons.
+
+Some additional effects which should be considered in principle, and
+which are possibly important in practice, are that the variance
+estimate should be based on the actual number of photons detected before
+correction for pixel sensitivity; i.e. before flat field correction.
+Furthermore the uncertainty in the flat field should also be included
+in the weighting.  However, the profile must be determined free of
+sensitivity effects including rapid larger scale variations such as
+fringing.  Thus, ideally one should input the unflat-fielded
+observation and the flat field data and carry out the extractions with
+the above points in mind.  However, due to the complexity often
+involved in basic CCD reductions and special steps required for
+producing spectroscopic flat fields this level of sophistication is not
+provided by the current package.  The package does provide, however,
+for propagation of an approximate uncertainty in the background
+estimate when using background subtraction.
+
+The noise model is described by the following equations.
+
+.nf
+    (1) V = max (VMIN, (R**2 + I + VB) / G**2)
+	    max (VMIN, (R**2 + S * P + B + VB) / G**2)
+
+    (2) VB = 0.                 if (B = 0)
+	   = B / (N - 1)        if (B > 0)
+
+    (3) VMIN = 1 / G**2         if (R = 0)
+	       R**2 / G**2      if (R > 0)
+.fi
+
+V is the desired variance of a pixel to use for variance weighting.  R
+is the photon read out noise specified by the parameter \fIreadnoise\fR
+and G is the photon per data value gain specified by the parameter
+\fIgain\fR.  There are two forms to (1).  The first is used in the
+initial pass of estimating the spectrum flux S and the actual pixel
+value I (which includes any background) is used for the poisson term.
+The other form is used in a second pass (and further passes if
+cleaning) using the estimated data value based on the normalized
+profile P scaled to the estimated total flux plus the estimated
+background B; i.e. I estimated = S * P + B.
+
+The background variance VB is computed using the poisson noise model
+based on the estimated background counts.  If no background subtraction
+is done then both B and VB are set to zero.  If a background is
+determined the background is either an average or function fit to
+pixels in defined background regions.  If a fit is used B need not be a
+constant.   Because the background estimate is based on a finite number of
+pixels, the poisson variance estimate is divided by the number N (minus
+one) of pixels used in determining the background.  The number of
+pixels used includes any box car smoothing.  Thus, the larger the
+number of background pixels the smaller the background noise
+contribution to the variance weighting.  This method is only
+approximate since no correction is made for the number of degrees of
+freedom and correlations when using the fitting method of background
+estimation.
+
+VMIN is a minimum variance need to avoid generating zero or negative
+variances from the data.  The definition of VMIN is such that if a zero
+read out noise is specified (which is certainly possible such as with
+photon counting detectors) then a minimum of 1 photon is imposed.
+Otherwise the minimum is set by the read out noise even if the poisson
+count part is (unphysically) negative.
+
+One deviation from the linear photon response mode which is considered
+is saturation.   A data level specified by the parameter
+\fIsaturation\fR is used to exclude data from the profile fitting.
+During extraction the saturated pixels are not treated any differently
+than unsaturated pixels except that dispersion points with saturated
+pixels are flagged by reversing the sign of the final estimated sigma;
+the sigma output is enabled with the \fIextras\fR parameter.  Exclusion
+of saturated pixels from the extraction, as is done with deviant
+pixels, was tried but this resulted in higher noise in the spectrum.
+
+If removal of cosmic rays and other deviant pixels is desired, called
+cleaning and selected with a \fIclean\fR parameter, they are
+iteratively rejected based on the estimated variance and excluded from
+the weighted sum.  Note that a cleaned extraction is always variance
+weighted regardless of the value of the \fIweights\fR parameter.  This
+makes sense since the detector noise parameters must be specified and
+the spectrum profile computed, so all of the computational effort must
+be done anyway, and the variance weighting is as good or superior to a
+simple unweighted extraction.
+
+The detection and removal of deviant pixels is straightforward.  Based
+on the noise model described earlier, pixels deviating by more than a
+specified number of sigma (square root of the variance) above or below
+the model are removed from the weighted sum.  A new spectrum estimate
+is made and the rejection is repeated.  The rejections are made one at
+a time starting with the most deviant and up to half the pixels in the
+aperture may be rejected.  The total number of rejected pixels in the
+spectrum is recorded in the logfile and a profile plot of data and
+model profile is recorded in the plotfile.
+
+As a final step when computing a weighted/cleaned spectrum the total
+fluxes from the weighted spectrum and the simple unweighted spectrum
+(excluding any deviant and saturated pixels) are computed and a
+"bias" factor of the ratio of the two fluxes is multiplied into
+the weighted spectrum and the sigma estimate.  This makes the total
+fluxes the same.  The bias factor is recorded in the logfile
+if one is kept.  Also a check is made for unusual bias factors.
+If the two fluxes disagree by more than a factor of two a warning
+is given on the standard output and the logfile with the individual
+total fluxes as well as the bias factor.  If the bias factor is
+negative a warning is also given and no bias factor is applied.
+.ih
+SEE ALSO
+apbackground approfiles apall apsum
+.endhelp
-- 
cgit 

