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Cresitello-Dittmar, M., Aldcroft, T. L., & Morris, D. 2001, in ASP Conf. Ser., Vol. 238, Astronomical Data Analysis Software and Systems X, eds. F. R. Harnden, Jr., F. A. Primini, & H. E. Payne (San Francisco: ASP), 439
On the Fly Bad Pixel Detection for the Chandra X-ray Observatory's Aspect Camera
Mark Cresitello-Dittmar, Thomas L. Aldcroft, David Morris
Harvard-Smithsonian Center for Astrophysics
Abstract:
The Chandra X-ray Observatory uses an optical CCD in its aspect camera.
As with all space-based CCD detectors, radiation damage will accrue with
time and substantially increase the dark current of individual pixels,
resulting in ``warm pixels.'' In order to obtain the most accurate
aspect solution possible, it is necessary to identify and compensate for
these regions when processing the guide star images. If a warm pixel is
included in a guide star image, it will bias the centroid location for
that image. As the spacecraft dithers, this bias will introduce a wobble
to the star location that translates to a wobble in the aspect solution.
Special dark current calibration observations can be taken to provide a
full-frame dark current map, however, it is not operationally feasible
to obtain a new map for each observation.
The CXC data systems group has developed software to analyze the star
image data and identify warm pixels as part of standard processing.
This ``on the fly'' determination allows us to adjust for variations in
CCD conditions between dark current calibration observations and provides
useful information for identifying bad regions on the Aspect camera CCD.
In order to achieve the unprecedented accuracy of Chandra's aspect
solution, it is necessary to get the most accurate star centroid locations
possible. As the aspect camera CCD is exposed to radiation, warm pixels
will develop. These warm pixels can affect the centroid locations of
the stars by creating a bias in that direction. If the pixel is very
warm, this bias can be quite pronounced and create relatively large
centroid errors. It is possible to correct for these warm regions
during processing by applying a background subtraction using a dark
current map. These dark current maps show the expected number of counts
to be registered by each CCD pixel when no source photons fall on it.
They are generated through special dark current calibration observations.
Since it is not feasible to conduct a dark current calibration for each
observation, a mechanism is needed to identify and correct for new warm
pixels as they evolve. This can be accomplished by analyzing data from
the guide star images themselves.
Chandra's Aspect camera detector is a
pixel CCD. To save
bandwidth, only a small subset of pixels centered on each star is
telemetered to the ground.
To determine dark current, we want to collect data from pixels with few
or no counts from the source. The typical star image will have a FWHM of
1.8 pixels. This means that the outer rim of pixels in a
pixel image
are largely unaffected by star light, and can be used in the analysis.
If the spacecraft did not dither, the same set of pixels would be seen
in each image and we would have only a few pixels to analyze. However,
spacecraft dither will cause the star image to move along the CCD.
The aspect camera tracks this motion and adjusts the set of pixels used
so that the star remains centered in the field. As a result, a much
larger sampling of pixels can be obtained.
Background subtracted image values are accumulated for each pixel matching
the above criteria. For each pixel, we also determine the average total
image counts of all images containing that pixel.
To determine if a pixel is warm, it must consistently show a number of
counts above some threshold. Each pixel will show random fluctuations
in counts from background radiation. They may also show higher count
levels from extended source emissions or from elevated background levels
in the vicinity of the star. Stars located near the outer ends of the CCD
will have elongated PSFs which could cause source photons to land in the
outer rim pixels. Since there are several factors that affect the number
of counts seen in these `background' pixels, we cannot simply apply a
static threshold to all pixel data to determine if it is warm. We use
a two-tiered method for calculating the dark current threshold level
to apply. The dark current threshold is defined to be the greater of:
- An absolute threshold level (default = 200 counts/sec).
- A fraction of the average total image counts (default = 0.005).
The dark current value for each pixel is determined by a percentile method.
|
(1) |
where N is the Percentile level, typically 0.10,
numvals is the number of pixel values accumulated, and
pixel_value is the sorted array of pixel values.
Any pixel whose dark current level is above the dark current threshold
is considered `WARM.' Its location and dark current level are stored.
Once the warm pixels have been identified, this new information must be
applied to the star images in order to remove the effects these pixels
will have on the image centroids.
Figure 1:
Reconstructed star location without bad pixel correction.
|
The dark current map is updated to reflect the elevated dark current
levels for all bad pixels found. Since the image data we use has already
been background subtracted, this correction is additive:
|
(2) |
The raw image data is then re-run through the background subtraction
process. With the proper dark current subtracted, the centroids will
not be biased by the elevated counts, and a more accurate centroid can
be obtained.
When a warm pixel contaminates a star
image, it produces an offset to the image centroid in the direction
of that pixel. As spacecraft dither moves the image along the CCD,
the direction of this offset changes, creating a periodic wobble in
the star locations. This wobble is apparent in the aspect solution.
The effect is reduced by the use of multiple guide stars and smoothing
techniques, but it can still have a noticeable impact on pointing accuracy.
Figure 2:
Reconstructed star location with bad pixel correction.
|
Figures 1 and 2 show a series of plots that characterize the accuracy of a
star's centroids. The plots show the difference between the star centroid
and that star's `expected' location. Since a guide star's actual position
is well known, one can use the spacecraft motion described by the aspect
solution to predict where that star should fall on the CCD as a function
of time. By comparing these values with the locations described by the
star centroids, one can gain a sense of the accuracy of these centroids.
The two spatial plots at top show the same data at different scales.
These show the distribution of the centroid offsets in the spacecraft Y
and Z axes from expectations. With good centroids and a good solution,
this distribution should be centered on 0.0 with a small random spread.
The other plots show the offsets for each axis as a function of time.
These allow the periodic nature of the effect to be seen.
Figure 1 shows the results of a run containing two bad pixels. The warm
pixel detection was turned off during processing. The spatial plots
show a significant elongation in the Z direction. This is a result of
the warm pixels moving in and out of the image field as the star dithers
on the CCD. The plots of offset vs. time show the periodic nature of
the effect. The RMS of the offsets is indicated on these plots.
Figure 2 shows the same data when warm pixel detection is applied.
Notice the spatial plot shows significant improvement in the distribution.
The plots of offset vs. time also show significant improvement, especially
in the Z axis. The RMS has dropped from 0.24 to 0.09arcsec.
The remaining periodicity is most likely due to pixels that are warm,
but not yet above the threshold level.
Acknowledgments
This project is supported
by the Chandra X-ray Center under NASA contract NAS8-39073.
© Copyright 2001 Astronomical Society of the Pacific, 390 Ashton Avenue, San Francisco, California 94112, USA
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