UVIS Calibration Issues
Greg Holsclaw, Bill McClintock
Jan. 8, 2007
Topics
• Point spread function Model
• RTG vs time
• Sensitivity vs time
Point Spread Function
• The UVIS instrumental point spread function is the result of several
contributing phenomena:
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–
Diffraction
Optical aberrations
Grating scatter
RTG background
Stray light
Detector response
• No good, well-separated, narrow line-width sources to measure the
PSF on ground or in-flight
• Best hope is to use the ubiquitous, monochromatic Lyman-alpha
emission from interplanetary hydrogen
• Due to its presence in all directions, the entrance slit will always be
filled and thus a measurement will be convolved with the geometric
slit image
IPH Stellar Contamination
•
Spatial average
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•
Spectral
average of
Columns 2501000
A long campaign
to observe IPH
was conducted in
late 1999
89 observations
with 251 individual
lo-res and 29 hires full image
scans
Many images
contain obvious
contamination
from stars, as
seen here
IPH Stellar Contamination Filter
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•
•
Plotted here is
the row 20
average
(columns 2501000) for all
Lores (top) and
Hires (bottom)
scans
Zeroes are data
dropouts and
large values are
presumably
stars
Used a twopass 1-sigma
filter to isolate
‘good’ scans for
each row
IPH Stellar Contamination Filter
• The number of
scans kept for
each row is
different,
averaging
about 75% for
Lo-res and
55% for Hi-res
IPH Average Images
Lo-res
Hi-res
PSF Model
• Although the Lo-res slit is wider than the Hi-res slit, the
Lo-res observations provide a better dataset with which
to attempt a model PSF because of a higher SNR due
to:
– Larger signal from wider entrance slit
– More observations in Lo-res provide many more scans over
which to average
RTG Background
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•
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•
The magnitude of the
background is important in
the PSF model, as it
determines how much
signal is scattered to large
distances from the core
The signal at the longwavelength end of the
detector for an IPH
observation is negligibly
small, and therefore
represents a measure of
the background caused by
the RTG
This is supported by the
fact that the measured
signal is essentially the
same for both Hires and
Lores scans, as seen here
Only non-evil pixels are
considered, the last 25
columns used, and only
scans with at least 10
uncontaminated rows are
used in the average
RTG Background vs time
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This is a plot of the
average FUV
background as a
function of time
An exponential
curve is fit to the
data (the expected
functional
dependence of the
rate of radioactive
decay)
Only Lo-res fullimage observations
acquired after 1999
Spectral PSF Model
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•
Calibration paper uses a Gaussian + Lorentzian functional model for the PSF
This has been expanded to a Gaussian + Lorentzian + Lorentzian:


 0.5 ( x  a2 ) 2


2
a5
a7
  rect  x  a2 
f  a1  a3e a4


2
2

( x  a2 )
( x  a2 ) 
 w 
1
1
2
2


a6
a8


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•
The rect function models the pixel and geometric slit image response where w is the
slit width
RTG is subtracted before the fit
Fit parameters for x in wavelength units of nm:
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a1 = 0
a2 = 121.581
a3 = 0.0759659
a4 = 0.118578
a5 = 0.000607055
a6 = 1.72248
a7 = 6.67736e-6
a8 = 39.2354
Spectral PSF Model
• This shows the
relative magnitudes
of each of the
model component
functions
• The Gaussian
effectively models
most of the energy
in the core, the first
Lorentzian the
immediate wings,
and the second
Lorentzian the slow
decline far from the
line center
Spectral PSF Model
• These plots show that the fit PSF model does a good job of
approximating the spatially averaged Lo-res IPH spectrum
Spatial PSF
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FUV2005_295_23_51_49_UVIS_017ST_ALPVIR001_PRIME
Plot of spatial profiles
shifted to the Gaussfitcenter from a Spica
along-slit slew in
addition to the spectral
PSF fit
The spatial profile is
consistent in character
for all points along the
slit
Slightly more
scattering toward the
bottom of the detector
The spectral profile
underpredicts the
spatial scattering
Fit requires increase in
second Lorentzian
component by 2.5x
Time variable sensitivity
Consistency of UVIS spectra with
other results
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FUV1999_016_19_47_15 - signal
FUV1999_016_20_16_14 - background
Initial UVIS observations of
Spica just after launch in
1999 are in good
agreement with other
measurements in spectral
shape and magnitude
Adjustment of 0.9 applied
to UVIS after evil pixel
interpolation to preserve
total counts, no flat field
applied, all high spectral
resolution data smoothed
to 1nm resolution
Short wavelength end is
variable among all data
sets
Observed sensitivity variation over time
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•
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This plot shows the first and
most recent spectrum of
Spica (Alpha Vir) and
Fomalhaut (Alpha PsA);
occultation slit, evil-pixel
interpolated, verticallysummed, and 11-pixel
smooth
The sensitivity toward long
wavelengths has increased
over time, while the short
wavelength sensitivity has
decreased by a lesser
amount
This is seen in spectra
acquired from both Spica
and Fomalhaut
(Note that the most recent
Spica observation was
acquired over the central
rows and thus is affected by
starburn, lowering the
signal, especially in the left
half of the spectrum)
Goals and approach
• The physical mechanism for the change in the
red response is not understood
• Goal: characterize this variation with a simple
model and remove the effect from the data
• Approach: track the variation for each
wavelength through the repeated observations
of the stars Spica (Alpha Vir) and Fomalhaut
(Alpha PsA), while avoiding the starburned rows
Spica stellar calibration dataset
•
•
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This plot shows the
spatial position of Spica
as a function of time for
all ‘good’ observations
(central row position is
measured by fitting a
Gaussian to the spectrally
summed array)
Initial observations
subsequent to launch
used the occulation slit
and were limited to the
center of the detector
After the starburn, Spica
was predominantly
observed with the Lo-res
and Hi-res slits across the
entire vertical extent of
the detector
Fomalhaut stellar calibration dataset
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•
This plot shows the
spatial position of
Fomalhaut as a function
of time for all
observations
Observations are few
and limited to the central
rows of the detector
Starburn-affected rows
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•
This plot shows the
total signal as a
function of spatial
position along the
slit for Spica and
Fomalhaut
The starburn is
centered at row 32
and has affected
rows ~28 to ~36
Avoiding starburn-affected rows
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•
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Preburn: average the
central rows with the
occultation slit (four distinct
observations), the ‘interior’
spectrum
Postburn: average the
exterior rows postburn with
the Lo-res and Hi-res slits
(four distinct observations
involving along-slit slews),
the ‘exterior’ spectrum
Other filtering: eliminate
scans in which the target
appears to be located
partially outside the slit, and
those which have large
spectral shifts
Scattered light correction
• Scattered light is the redistribution of signal from the expected
wavelength position to any other position
• This can be caused by diffraction, surface roughness, and even
detector mislocations and is characterized by the instrumental point
spread function
• Scattered light can be removed through a deconvolution of a
spectrum using the measured PSF
• One simple approach is the Van Cittert algorithm, which convolves
the measured spectrum with the PSF, and uses the difference as an
estimate of the scattered light contribution. The first iteration is
given by:
S  M  PSF  M
M' M  S
• Where M is the measured spectrum, S is the scattered light
estimate, and M’ is the corrected spectrum
Scattered light correction examples
The signal at 185nm for the
initial Spica observations is
reduced by ~20%
The effect for Fomalhaut is
small from 155 – 190nm
Fractional variation and fit
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The magnitude of the
average exterior spectrum
may be different than the
interior spectrum due to
sensitivity variations along
the slit
This adjustment can be
estimated from interior and
exterior spectra acquired
just before and after the
starburn, respectively
(here estimated to be
0.92)
A line is fit to each
wavelength of the form:
a1 t
0
2
At 185nm, a=[-2.52812, 0.00115720, 3.51089]
21 pixel-wide spectral bins
f  a e
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•
a
Inclusion of Fomalhaut data
• Fomalhaut provides an independent source in which to
verify the fractional change observed from Spica
• Fomalhaut is observed only in the central rows, so that
complications from starburn cannot be avoided
• However, the starburn effect correlates with the spectral
distribution of Spica, and so the long-wavelength end of
the detector is expected to be less affected
Spica and Fomalhaut
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The second Fomalhaut
observation coincides with
the second Spica
observation
Therefore, we can
normalize the fractional
variation of Fomalhaut to
that observed for Spica at
this point in time
The occultation slit
Fomalhaut measurements
show a good correlation
with the Spica data
Lo-res slit Fomalhaut
measurements are
consistently lower, likely
due to the smaller spatial
distribution coupled with
the strong row-to-row
variation
Goodness of fit
Model fit does a good job of tracking
the spectral and temporal variation in
the Spica measurements, to a mean
error of less than 5% and peak
excursions no greater than 10%
The Spica-derived model provides a
good fit for all but the first observation
of Fomalhaut
Continued Work
• New red patch for the calibration
– observation time is now a parameter
– independent of the flat-field correction
• Validation of derived calibration correction model through
consistency checks of UVIS results with SORCESOLSTICE spectra of Spica and Fomalhaut
• More sophisticated deconvolution techniques will be
explored, useful for removal of scattered light in both the
spectral and spatial dimensions as well as refinement of
the time-dependent red patch