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apvariance noao.twodspec.apextract


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 weights 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.

	none:	S = sum { I - B }
    variance:	S = sum { (P**2 / V) (I - B) / P } / sum { P**2 / V }

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 apbackground ), P is estimated normalized profile value for that pixel (see approfile ), 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 gain parameter given as the number of photons per data number. The gaussian noise is given by a readnoise 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.

    (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)

V is the desired variance of a pixel to use for variance weighting. R is the photon read out noise specified by the parameter readnoise and G is the photon per data value gain specified by the parameter gain . 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 saturation 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 extras 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 clean 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 weights 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.


apbackground approfiles apall apsum

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