NGVS MegaPipe documentation

• Stacking procedure
• File types
• Naming convention
• A note on gain
• Back to the NGVS graphical user interface

The first part of the stacking procedure is the astrometric/photometric calibration of the Elixir-processed input images. This is almost identical to the procedures described on the MegaPipe documentation. (Go here for the astrometry and here for the photometry.) The SDSS is used for both the astrometric and photometric calibrations. When processing the Elixir-LSB data the photometric calibration is determined on the scale of the mosaic. That is, a single scaling factor is applied to all 36 CCDs. This means that the small (0.03-0.06 mag) photometric zero-point variations across the MegaCam mosaic are not corrected. (see this page for a full discussion). However, a global photometric scaling is required because the nature of the Elixir-LSB; if different scaling factors are applied to different chips the surface brightness levels would not match up across chip boundaries. The two methods that do not use Elixir-LSB data determine the photometric zero-point on a chip-by-chip basis and will have slightly superior photometry.

The next steps are background subtraction and stacking. MegaPipe has implemented three separate methods:

• Local background subtraction, median combine (Ml128): This is the standard MegaPipe procedure. SWarp is run on the normal Elixir processed images. SWarp removes the background on a local 128x128 pixel grid. Because of this objects which are about this scale or large are effectively removed from the images. Obviously this highly undesirable if one studying the structure of M87 or any of the larger galaxies. However, objects like small galaxies, stars and globular clusters are unaffected. This method provides the best background removal (provided one is not interested in large extended objects), and because the photometric zero-point is computed on a chip-by-chip basis, this method will provide the best photometry.
  
  
• MegaPipe global background subtraction version 2, artifical skepticism combine (Mg002) In this case, the background of each input image is removed globally, that is on the scale of the whole mosaic. The background subtraction is done by using available MegaCam images to build up a general background map. This map is scaled to the appropriate sky level and the subtracted from the NGVS images. A detailed description can be found here. The background subtracted images are combined using artifical skepticism (descibed in the following section). This method does not do quite as good a job at background substraction as the Elixir-LSB methods described below. However, it provides superior point-source photometry, for the reasons noted above. Also, while Elixir-LSB can only be done if the images are taken in a specified sequence, this method can be applied to any image. This is the best method available for the short exposure images.
  
  
• Elixir-LSB global background subtraction, artificial skepticism combine (Mg004): In this case the Elixir-LSB images are stacked. The Elixir-LSB images produced by Jean-Charles Cuillandre have the background variations removed, but still retain a constant background level. MegaPipe removes this constant level from each input image. The background level is determined for each chip the mode of the pixel values. The mode is a true mode (not 3*median - 2*mean) determined by method described here Different chips can have quite background levels; bright galaxies and scattered light from bright foreground stars will raise the background level locally. The lowest background level, that is to say, the background form the least contaminated chip is used as a constant background level for the whole mosaic. Typically, if the field is uncontaminated, the difference between the darkest and brightest chip is only about 0.1% or so.
  
  Having removed the background, the stacking then proceeds as normal. SWarp is used to resample the images. While using a median for combining the images provides excellent rejection of cosmic rays and other outliers, it increases the noise in the output images slightly. The typical decrease in depth is about 0.16 magnitudes. Artificial Skepticism (Stetson 1989) is a method of computing a robust average image using a continuous weighting scheme that is derived from the data themselves. The weights are given by:
  
  where w_i is the weight of the i-th input pixel, σ_i; is the standard deviation (determined by the read noise and gain), r_i is the residual of i-th input pixel with respect to the current average value. The method starts by using a median as the starting point. Weighted averages are then computed using the equation above iteratively. Pixel values that fall far from the average will be given less weight (r_i is larger so w_i becomes smaller) in each successive iteration. The equation can be tuned using the free parameters α and β. Here they are set to α=1 and β=2, as used the WFPC2 pipeline. After a few iterations (both MegaPipe and the WFPC2 pipeline stop at 5 iterations) the procedure converges. If there is a defective pixel (e.g affected by a cosmic ray) in the list, the weight for the pixel will be such that it contributes negligibly to the output. If there are no defective pixels, all the pixels will be weighted roughly equally. This is different then a sigma clipping method in that there is no hard, discrete cutoff defining when a pixel is "good" or "bad". Pixels are rejected gradually as they deviate from the central value.
  
  Examining the output images, one sees excellent cosmic ray rejection. Experiments show that the depth of images combined with artificial skepticism is within 0.08 magnitudes of those using a blind average.
  
  In practice, for the NGVS data, the images are treated as in the previous method up until the SWarp stage. The images are calibrated and the constant background is removed. as before. SWarp is than used to re-map each of the input images to a common pixel grid, but not to combine them. The remapped images are then combined using gcomb a Fortran implementation of the artificial skepticism algorithm. (The python implementation used by STScI is too slow for 20000x20000 pixel images.).

• Image (.fits): This is the stacked image. All the images have a photometric zero-point of 30.000 in AB magnitudes. This is to say, if you measure a flux in counts from the image, then:

   
  AB_magnitude = -2.5 * log10(counts) + 30.000
  

  If you need Vega magnitudes, consult the following table:

  FILTER	Vega-AB MegaCam
  u	-0.346
  g	+0.092
  r	-0.171
  i	-0.401
  z	-0.554

  
  
  
• Weight map (.weight.fits): These are are delivered with in a compressed format. To uncompress them, use CFITSIO's imcopy:

  imcopy NGVS+0+0.l.g.Ml128.weight.fits.fz[1] NGVS+0+0.l.g.Ml128.weight.fits
  

  The values of a weight map pixel is the inverse of the variance of that pixel in the main image. The variance here only takes into account the mean sky levels of the input images. The flux from the objects is not taken into account. This is adequate for objects within a few magnitudes of the detection limit.
  
  
• Catalog (.cat.fits): The catalogs are generated with SExtractor using this parameter file. The catalogues have a header describing the columns:

  #   1 NUMBER          Running object number
  #   2 X_IMAGE         Object position along x                         [pixel]
  #   3 Y_IMAGE         Object position along y                         [pixel]
  #   4 ALPHA_J2000     Right ascension of barycenter (J2000)           [deg]
  #   5 DELTA_J2000     Declination of barycenter (J2000)               [deg]
  #   6 MAG_AUTO        Kron-like elliptical aperture magnitude         [mag]
  #   7 MAGERR_AUTO     RMS error for AUTO magnitude                    [mag]
  #   8 MAG_BEST        Best of MAG_AUTO and MAG_ISOCOR                 [mag]
  #   9 MAGERR_BEST     RMS error for MAG_BEST                          [mag]
  #  10 MAG_APER        Fixed aperture magnitude vector                 [mag]
  #  11 MAGERR_APER     RMS error vector for fixed aperture mag.        [mag]
  #  12 A_WORLD         Profile RMS along major axis (world units)      [deg]
  #  13 ERRA_WORLD      World RMS position error along major axis       [pixel]
  #  14 B_WORLD         Profile RMS along minor axis (world units)      [deg]
  #  15 ERRB_WORLD      World RMS position error along minor axis       [pixel]
  #  16 THETA_J2000     Position angle (east of north) (J2000)          [deg]
  #  17 ERRTHETA_J2000  J2000 error ellipse pos. angle (east of north)  [deg]
  #  18 ISOAREA_IMAGE   Isophotal area above Analysis threshold         [pixel**2]
  #  19 MU_MAX          Peak surface brightness above background        [mag * arcsec**(-2)]
  #  20 FLUX_RADIUS     Fraction-of-light radii                         [pixel]
  #  21 FLAGS           Extraction flags
  

  All magnitudes are on the AB system.
  
  
• Sigma map (.sig.fits): These images give 1-sigma error bars for each of the pixels of the main map. They are not inverse variance maps like the weight maps above. These images take into account the increase in Poisson noise caused by sources in the image. While the weight maps are sufficient for objects within a few magnitudes of the detection limit, these images better reflect the true noise in bright sources.

NGVS+0+0.l.g.Mg001.fits
^        ^ ^ ^^    ^
|        | | ||    |_____ extension indicating file type 
|        | | ||              fits image
|        | | ||              weight.fits weight map
|        | | ||              cat catalog
|        | | ||              sig.fits  detailed sigma map
|        | | ||              see above for details
|        | | ||__________ 4-character string indicating background subtraction
|        | | |               and stacking method:
|        | | |               l128 local background subtraction on 128x128 grid, median combine
|        | | |               g001 global background subtraction, median combine
|        | | |               g003 global Elixir-LSB background subtraction, median combine
|        | | |               g004 global Elixir-LSB background subtraction, artificial skepticism combine
|        | | |               Detailed descriptions can be found above.
|        | | |___________ source of stacks:
|        | |                 M for MegaPipe
|        | |                 E for Elixir
|        | |                 T for Terapix
|        | |_____________ filter: ugriz
|        |_______________ exposure time: l for long, s for short, m for merged
|________________________ NGVS pointing: roughly degrees relative to M87. 
                          0 is denoted with +0, not -0.

The noise properties of a single image can be described by two parameters: the gain and the read noise. Gain is the number of electrons for a given data number of particular image pixel. The read noise is the noise caused by reading out the CCD.

Neither concept is particularly well defined for the stacked MegaPipe output images. However some astronomical software packages require a value for gain and read noise in order to function properly. Therefore the following table gives the effective gain and effective read noise for the image stacks produced by MegaPipe based on the Elixir LSB images. The details of the calculations are given here.