Algorithms: The Flat Fields for SDSS Data Release 2
J. E. Gunn, Daniel Eisenstein, Brian Yanny, and Zeljko Ivezic
Contents: Introduction / Flattening the Color Response Using the ColorColor
Relations / Flattening the Flux Response Using the
PT / Does it Work?
In any CCD photometric survey, the calibration internal to an
individual CCD (the `flat field' ) is of comparable importance to the
overall mean photometric calibration. The flatfielding exercise is
simplified for a TDI scanning survey like the SDSS because any given
object samples and averages over all the rows on a CCD along its
columns and so the flat field is a one dimensional vector, not a 2D
image as is usual in CCD photometry.
In the first two years of the survey we used the statistics of the sky
to construct flat fields. The data acquisition system produces in real
time for each photometric CCD a quartile array, which are the three
quartiles in a frame for each column. The data are clipped at what
would correspond to 2.3 sigma if the distribution were gaussian. The
quartiles for individual frames can be influenced by very bright
stars, large galaxies, etc, and so these arrays are interpolated with
outlier rejection to create an array characteristic of a whole
run. Subsampling these data verify that the changes within a run are
negligible. The flat is then created by inverting and scaling the
biassubtracted median vector.
It was realized in 2001 that these flats were not
working very well; comparison with photometry from the PT suggested
that in u, particularly, there were large calibration errors which
were correlated well with the column number on a given photometric
chip. It seemed likely (and still does) that scattered light was the
culprit. About the same time it was found that the forms of the
flatfield vectors were timedependent, and seemed to change
discontinuously over the summer period, when the camera is typically
disassembled for maintenance. These changes are a few percent in g, r,
i and z, but are observed to be as large as ten percent in u, about
the same size as the photometric errors suggested by careful
comparison with the PT photometry. The changes are almost certainly
associated with subtle changes in surface chemistry in the CCDs which
affects the electric fields in the devices very near the
photosurface. The u chips are most affected because of the very small
penetration depth of ultraviolet photons in silicon. It is unfortunate
that the scattered light in the camera, which invalidates the sky
flats, is also worst in u on two counts, the higher reflectivity of
the u devices and the higher reflectivity of the u filters. So we had
two problems: scattered light and temporally variable response. The
former meant that we could not use the sky flats, the latter that one
master set of flats derived once and for all would not work.
Analysis of the sky data reveals that there are nine 'seasons' during
which the flat fields are constant at approximately the one percent
level. The list of runs and seasons through the spring of 2003 is
Season  Run range 
1  94 to 1140 
2  1231 to 1478 
3  1659 to 1755 
4  1869 to 2078 
5  2125 to 2143 
6  2188 to 2283 
7  2298 to 2507 
8  2566 to 3187 
9  3225 to 9999 
It was realized then that we would probably have to construct flats
for each season using stellar photometry. In the spring of 2002 we
took some imaging data using a scan path which crossed one of our data
stripes at an angle of 30 degrees. In principle this allows one to
determine a flatfield unambiguously, but the data were flawed because
of the quality of the night. We are now taking such crossscans
regularly, but we did not have them for all data releases prior to DR2.
We have therefore used a technique which combines internal checks using the
stellar locus in various colorcolor diagrams with an overall
magnitude relation using a robust total flux estimator from the PT to
construct the flats. The procedure is detailed in the following.
The stellar locus in our four colorcolor diagrams is observed to be
very narrow and change little over the sky, and it has been suggested
that it be used as a direct calibration of our photometry. We have not
done that here, but have adopted an iterative technique which is very
much more robust and makes few astrophysical assumptions. We only
assume here that the stellar locus is constant over the width of a
single CCD (13 arcminutes) as averaged over an entire run, or,
indeed, over an ensemble of many runs from the same season. If we make
that assumption, we can derive a set of colors which are linear
combinations of u, g, r, i, and z which are `perpendicular' to the
stellar locus for some color range and for which the dispersion simply
measures the small (typically .02.03 magnitude) width of the locus.
We establish flat fields which ensure that these colors are constant
across each device in the camera. The colors we use are (below and in
the rest of this document combinations like ri are shorthand for ri,
etc.):
1. gr directly for red stars, 0.8 < ri < 1.4, 1.2 < gr < 1.6, r < 19.7
2. sperp = ri  0.363*gr
for 0.3 <gr < 0.9, 0.2 < sperp < .2, r < 19.7
3. uperp1 = ug  1.5*gr  0.52
for 0.2 < gr < 0.4 , 0.6 < ug < 1.5, r < 19.7
4. uperp2 = ug  2.1*gr  0.30 + 0.025*(r17)
for 0.4 < gr < 0.8 , uperp2 < 0.3, r < 18
5. Gperp = iz  0.197*(gr + ri) + 0.074
for 0.5 < (gr + ri) < 1.6, 0.15 < Gperp < 0.15, r < 19.7
The bright cutoff is r = 15.0; only objects likely to be stars (r_psf
 r_model < 0.2) are used and only those with good psfs (PSP_STATUS
is OK), not BRIGHT, and not SATUR. All magnitudes are dereddened,
which does not really matter on these scales.
We thus begin with an initial guess about the flat fields (the `trial
flats'), which can be the ones for the previous season, the ones derived
from the sky medians, etc., run PHOTO, and use the derived photometry
on the sets of objects defined by the photometry and the above
definitions.
If one averages over a group of camera columns, 32 in our case,
each of these quantities has an average value (arranged to be near zero for the
`perp' colors by appropriate choice of the constants in the above
expressions) and a standard deviation which is the root sum square of
the width of the stellar locus and the measuring errors. The locus
width is typically 0.05 magnitudes in uperp1, uperp2, and gr, and
0.03 magnitude in the others. The mean color is calculated using weights
derived from these errors.
If the flat field is correct, these colors will all be equal; if not,
they will vary across the device. From these, one can algebraically
derive the offsets which must be added to each of ug, gr, ri, and iz
to achieve the best constancy across the device. The median offset
for each of ug, gr, ri, and iz is arbitrarily set to zero; note that
we are NOT setting photometric zero points, only variations in
response across the device. So for each run/season (and typically the
data for a single run do not define the flats well enough, and a large
fraction of a whole season must be combined to do this) we produce a
set of 64 points (bins of 32 columns across the 2048 columns of the
CCD) of errors in the ratios of the flatsthe gr error, for example,
is
delta_gr = 2.5*log((trueflat_g*trialflat_r)/(trialflat_g*trueflat_r))
These data are very powerful diagnostics, but do not give us enough to
reconstruct the true flats, because there is only COLOR data, no magnitude
datain other words, we only have ratios in different colors of the
ratios of the true flats to the trial ones.
In practice, the errors in these 32column bins are such that one is
forced to do some smoothing to reduce the errors satisfactorily and
reject points with large errors (typically caused by having too few
stars in a bin, which unfortunately is usually at the ends where
the scans do not overlap perfectly.) This was done by fitting a properly
weighted Tchebychev polynomial to the values, of order chosen in each
case to yield a satisfactory chisquared. The orders are typically
10 to 15.
In order to tie down the overall sensitivity, one must use something
which determines the total flux sensitivity. One could use a
wellmeasured color such as r from the PT for the calibration patches
which both telescopes observe, and determine an r flat field from the
photometric matches. If one does this, the full effect of flatfield
errors in the PT, which are not well understood, enter the calibration
of the 2.5meter. If one has ONE such flat field and the color errors
derived above, clearly, one can determine the corrections for all the
devices in a camera column. We have chosen instead of doing this to
at least average over what we hope are independent flatfield errors
in the PT, by constructing a mean total magnitude
t = (g + r + i)/3
We do not use z and u because they are typically less well measured and
are known to have serious flatfielding problems in the PT; we do not
know of serious errors in g, r, and i, and indeed tests involving
measurements of a field on a grid of pointings suggest that the flats
in these primary bands are determined to of order one percent.
The procedure for using the t measurements is straightforward. In
each of the 32column wide bins, the measurements of the magnitude of
each star in g, r, and i are compared between the PT and the
2.5meter. It is assumed that the photometric error in the 2.5meter
is negligible and that the flat field is constant over the bin. Thus
the scatter in the differences are measures of the PT
measurement errors. These are determined in magnitude bins, and
weights derived from these. For each bin, those weights are used to
compute a mean difference and a chisquared, and stars are rejected if
the difference from the mean is larger than 3 of the standard
deviations appropriate for that star. These means and the associated
standard deviations are then used to construct a set of difference
values for t and associated errors. Again the median of the values
for all the bins on a given device is subtracted  again we are not
interested in photometric calibration, only in response
differences at different places on the chip. It is somewhat disturbing
that the chisquared values for these differences are seldom as small
as they should be, suggesting perhaps that there are problems at much
higher spatial frequencies than we are resolving, but there is very
little structure in the sky quartiles on these scales, so there is
left something of a mystery.
Given, then, these t difference values for each bin of columns, one can
trivially derive the perband flatfield errors:
g = t + 2*gr/3 + ri/3,
r = t  gr/3 + ri/3
i = t  gr/3  2*ri/3
z = i  iz
u = g + ug
Thus we derive a correction in each 32column bin for each device in the
camera. If the corrections are large enough (i.e. they affect the
colors enough) that the data rejection in the determinations of the
`perp' and ug colors used to derive the color corrections are
significantly impacted, one must iterate again. This was not deemed
necessary for DR1, and comparison of the predicted results for the
first iterate and the measured one after the correction had been
applied to the flats indicates in all cases that one iteration was
sufficient for this data.
These values in bins of 32 columns are again smoothed with weighted
Tchebychev polynomials, here of order 4 because of the noisier data from
the PT. The polynomial values as a function of column for the colors
and t are then combined using the above relations to produce a set of
corrections for each column of each device, and the new flat fields are
then constructed by the application of those corrections.
The technique removes all of the low spatial frequency structure in
the flat fields. It is unclear how much of the remaining rapid
variations are real, but since (a) the sky quartiles do not show high
spatial frequency structure, (b) we do not completely understand the
statistics within the binned column data, and (c) the rapid, small
amplitude variations which remain do not appear to be correlated from
season to season, we suspect that they are just noise, though they are
formally quite significant. Their nature and existence will become
clear, we are sure, when oblique scan data are properly analyzed.
Flatfield demonstration plots for season 5
Plot  GIF  Postscript 
Figure 1: t fits 
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Figure 2: fits to gr color errors 
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Figure 3: fits to ri color errors 
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Figure 4: fits to ug1 color errors (using uperp1) 
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Figure 5: fits to ug2 color errors (using uperp2) 
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Figure 6: ug color errors for corrected data 
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Five plots which illustrate the effect of the processing described
are shown here for season 5, which was the least wellbehaved season
in this data set. Figure 1
illustrates the t fits for this season. The formal chisquares/dof are
large, of order 34, but the rms scatter about the fourthorder
Tchebychev fit is of order 57 millimagnitudes. In most cases the
polynomial is a very much better fit than a constant. The next four
plots illustrate the fit to the color errors: gr (Fig. 2), ri(Fig. 3), ug1 (Fig. 4), and ug2 (Fig. 5). The two ug plots are
shown to illustrate the excellent agreement between the two estimates,
which are derived from uperp1 and uperp2, respectively. In these
cases, the chisquares are of the same order, and the rms deviations
from the fits about 6, 3, 13, and 13 millimag, respectively. Since the
ug1 and ug2 fits are averaged, the rms is about 10 millimag for ug and
considerably smaller for the other bands. The color error dominates in
u, the errors in t for all the other bands. Formally at least, the rms
errors in the 5 bands are of order 12, 7, 6, 6, and 8 millimagnitudes
for u, g, r, i, and z, respectively (the error in iz, not shown, is
about 4 millimag). This is a sizeable part of the error budget for our
goal of .02 magnitude RMS total, but since most of the rms is in small
scale features whose reality is in doubt and we do not understand the
noise very well, we may well be doing considerably better. We are at
least doing no worse than these numbers, which ascribe no noise to the
data. If one chooses to believe the chisquares, the contribution of
noise is only of order 10 percent of the error, but it may well, in
fact, be most of it.
The last plot, Figure
6, shows the ug color errors for the corrected data. This merely
demonstrates that we did the arithmetic right and that, in fact, one
iteration was sufficient in the worst band.
Last modified: Mar 8 2004
