UVIS Calibration Update
Greg Holsclaw
Bill McClintock
Jan 4, 2011
Outline
•
•
•
•
Recent stellar calibrations
Evil pixel experiments
Flat-fielding auroral observations
A simple visualization tool
All stellar calibrations
EUV
FUV
These plots show the total signal on the detector as
a function of star position along the slit
Recent stellar calibrations
EUV
FUV
Most recent
scan was
incomplete
These plots show the total signal on the detector as
a function of star position along the slit
Evil pixel sensitivity estimates
Options for estimating evil pixel
sensitivity
1. Zero
2. Lab-measured calibration curve
without flat-field corrector
3. Not-a-number (NaN)
•
Current approach
4. Measured
•
relative response to interpolated good pixels
for a stellar-calibration
Approach
• Test data set:
– Starcal from 2005, Spica slewed along the slit
– Sum all images during the slew, creating a
simulated extended object
• Compare one row of this array for these
cases:
– Current calibration, unbinned
– Current calibration, binned [2x1]
– New calibration, binned [2x1]
Results
•
•
•
Assigning evil pixels a
sensitivity of 0 or 1
(relative to the labcalibration) significantly
underestimates or
overestimates their
actual response, on
average
The larger the fraction
of evil pixels within a
bin, the larger the error
An evil fraction of 0.5
would be common for
a binning of 2, though
there may be limited
cases where the
fraction is greater at
higher binning
Compare the response of good and
evil pixels
• For each
column,
calculate the
mean value
of good
pixels (black)
and the
mean value
of evil pixels
(red)
• Ratio of
mean evil
pixel
value to
the mean
good
pixel
value for
each
column
• Histogram
of the
previous
slide
• Mean
value is
~0.5 with a
wide
distribution
Conclusions
• Assigning evil pixels a sensitivity of 0 or 1
(relative to the lab-calibration) significantly
underestimates or overestimates their actual
response, on average
• This creates a significant problem for datasets
which are moderately binned, and this approach
would lower the quality of this data
• Using a mean sensitivity value of ~0.5 that of the
good pixel values would be better
• However, using the mean relative evil pixel value
is a poor estimate of the wide distribution of
actual values
Evil pixel sensitivity estimate
and linearity test
Approach
1.
Generate a simulated extended object from two stellar calibrations
by summing all scans as the stellar image is slewed along the slit
–
–
2.
Calibrate the images, and interpolate across evil pixels
–
3.
5.
Consider this the ‘true’ spectrum for both stars
Generate a sensitivity estimate for the evil pixels using the
interpolated Spica values
–
4.
Alp Vir (Spica)
Eta Uma (Alcaid)
ratio of the interpolated evil pixel values in the calibrated spectrum to
the count rate
Fill the calibration matrix with these values, then apply to the Eta
Uma raw data (count rate)
Compare a single row of the Eta Uma data for two cases:
interpolated, evil-corrected
Raw spectrum
Calibrated (evils are NaN)
Interpolated and evil-corrected
Ratio of evil estimates to
interpolated values
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•
•
This shows the
ratio of an ‘evil
pixel corrected’
spectrum to a
spectrum that has
been interpolated
across evil pixels
There are two
cases: Eta Uma
and another
observation of
Spica
On average,
corrected evil pixel
values are too low
by ~20%
Ratio of good pixels to interpolated
values
•
•
The same ratio
plot as the
previous slide, but
now comparing
good pixels to
values that would
be obtained from
interpolation
The mean value of
the test pixels in
the ratio is 1.04 for
Eta Uma
Conclusion
• The sensitivity of evil pixels has been estimated
from their measured response to light from Alp
Vir (Spica)
• This sensitivity estimate has been applied to a
different star, Eta UMa
• Calibrated evil pixels appear too low by ~20%
(on average) compared to interpolated values
• Therefore, it is recommended that evil pixels not
be used in data analysis
Flat-fielding auroral scans
Motivation
• Jacques noticed that
calibrated images of Saturn
auroral scans exhibit
obviously spurious features
in some rows
• FUV2008_07_19_03_21_3
1_000_UVIS_077SA_AUR
ORA002_PRIME
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–
–
Number of spectral elements:
Number of spatial elements:
Number of records:
time
Binning: 32x1
rows
32
64
630
time
rows
Raw data
Calibrated, sum across
all wavelengths
Calibrated, sum across all
wavelengths except last
column
Code fragment
feuv_reader,f,pinfo,desc,wind,w1
w1_cal = w1
for i = 0, nz - 1 do $
w1_cal[*,*,i] = w1[*,*,i] * c_calibrationi_bin
img_cal = total(w1_cal[0:30,*,*],1) ; good images
img_cal2 = total(w1_cal[0:31,*,*],1) ; bad images
Not sure what is causing the erroneous
values, but neglecting the last column is
a temporary fix
A simple visualization tool
EUV2009_06_22_15_16_39_000_UVIS_113TI_EUVFUV003_PRIME
• Accepts a
single data
cube
structure
• Currently
only works
for Titan, but
could be
generalized
for any body