STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
with assistance from Kevin Lindsay (DAB)
Goals of the study:
Present the principal scientific drivers for different dither patterns, in the
context of the science goals outlined in the JWST DRM
 Define metrics to quantify advantages/disadvantages of dither patterns
 Carry out simulations of different dither patterns and calculate metrics
 Discuss optimization of dither patterns, including overhead issues
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Methodology:
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Concentrate on NIRCam with the most detailed simulations:
– numerically simulated exposures with different dither patterns
– analytic calculations of overhead times associated with various dither patterns
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Extend results to FGS-TF, MIRI imaging modes
Consider spectroscopic issues related to MIRI/IFU,LRS and NIRSpec
Reference:

Koekemoer & Lindsay, “An Investigation of Optimal Dither Strategies
for JWST”, STScI-JWST-R-2005-0002-A, July 2005 [circulated for
approval]
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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Science Drivers for Dithering
Improve sub-pixel PSF sampling:
Several DRM programs require high spatial resolution: e.g., faint white
dwarfs in globular clusters; AGN/galaxy connection; weak lensing, ...
 Most severely undersampled instruments are:

– FGS-TF (1 - 5 μm: 65 mas/pix) - only Nyquist sampled above ~4 μm
– NIRCam with two wavelength channels:
• 0.6 - 2.3 μm: 31 mas/pix - only Nyquist sampled above ~2 μm
• 2.4 - 5 μm: 65 mas/pix - only Nyquist sampled above ~4 μm
– NIRSpec has two different types of undersampling:
• 0.6 - 5 μm: 100 mas/pix - undersampled across entire wavelength range
• MSA shutters: 200x450 mas, spaced on a 250x500mas grid

Note that MIRI is relatively well sampled: 5 - 27 μm, 110 mas/pix
Note - sampling vs. resolution:
Sub-pixel dither and small output scale (1/2, 1/3) provides good sampling
 Removing extra convolution by pixel size (“pixfrac”) improves resolution

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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Science Drivers (cont’d)
Improved astrometry and photometry:
Precise photometry is required by some DRM programs, including halo
population studies and globular cluster stellar photometry
 Intra-pixel sensitivity variations cause photometric errors, hence also
affecting astrometry, in single exposures - effect can be up to ~0.4 mags
in HST/NIC3 (Lauer 1999)
 Mitigated by dither patterns to sample sub-pixel sensitivity structure: the
additional variance from this effect decreases as ~1/N dither points

Improved background subtraction:
Many DRM programs are aimed at faint sources, often a few orders of
magnitude fainter than the background
 Variations in background (time-dependent, or intrinsic limitations in
flatfield calibration accuracy) propagate into background subtraction
 Mitigated by dither patterns that sample the scales of background
variations across the detector

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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Science Drivers (cont’d)
Dithering to avoid detector gaps, blemishes etc:
All these represent pixels with lost information that needs to be recovered
 Even programs that don’t require contiguous imaging of large regions
will likely still want to cover the gaps between the 2x2 NIRCam detectors
(similar to the 2” chip gap in ACS)

Large-scale mosaic dithering:
Some DRM programs require contiguous imaging of regions much larger
than the detector (~100 or more pointings): e.g. weak lensing; rich clusters
 Large-scale dithers can be combined with smaller-scale patterns to
mitigate other effects previously discussed
 Requirements are:

– good acquisition accuracy to minimize overlap required between pointings
– maintaining a similar orient for all pointings, to minimize overlap required

Also need to consider overhead time due to dither slews / filter changes:
this requires specific trade-off studies for various observing schemes
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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Metrics for Optimizing Dither Patterns
Philosophy:

In order to determine which patterns are “optimal”, define metrics that
provide a quantitative description for a given dither pattern:
– “Positive” metrics that show how well a given measurable quantity can be
recovered from the data, for a given dither pattern
– “Negative” metrics that reflect various costs associated with carrying out a
given dither pattern

The trade-offs may be different, depending on the type of science
“Positive” metrics:
Morphological properties (incl. FWHM, etc.)
 Astrometric centroiding precision
 Photometric precision

“Negative” metrics:
Overhead time from dithering maneuvers, filter changes, etc.
 Area coverage lost near the edges of the field

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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Measuring Metrics for Simulated Data
NIRCam - properties:
NIRCam will likely be the dominant imager at short wavelengths (<5 μm)
and is also relatively undersampled
 Two optical trains, each with five 20482 sensor chip assemblies (SCAs):

– 0.6 - 2.3 μm: 31 mas/pix, 2x2 array of SCAs, 5” gap, undersampled < 2 μm
– 2.4 - 5 μm: 65 mas/pix, one SCA, undersampled < 4 μm
Assume read-noise = 15 e-/pix; dark current = 0.01 e-/s/pix
 Filters: F070W, F110W, F150W, F200W, F270W, F357W, F444W

Simulations:

Concentrate initially on 5 sub-pixel dither patterns:

For each dither pattern, create a set of dithered exposures for each filter:
– 1/2-pixel dithers: 2pt-line; 4pt-box, 4pt-box with short & long λ matched
– 1/3-pixel dithers: 3pt-line; 9pt-box
– populated with 1024 stars (32x32 grid), offset randomly in sub-pixel space
– nominal JWST PSF (obtained from S. Casertano / A. Sivaramakrishnan)
– count-rate uniformly sampled from 0.01 - 10 counts/s
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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Example Simulated NIRCam Image
Images created:
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For each of the 5 dither patterns
For each of the 7 filters
Total of 154 dithered 20482 images
Each image contains 1024 stars,
separated by 64 pixels, with random
shifts added up to 1 pixel
Image simulation software provided
by S. Casertano / E. Morse
Example image (right): F270W
Processing:


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Images were combined using
MultiDrizzle (Koekemoer et al. 2002)
Final drizzled image has pixel
scale = 0.5x input pixel size
Combined drizzled images used in
subsequent analysis
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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Close-up of a simulated star:
F270W
 4-point dither pattern:
(1.0,0.5) (1.5,1.5)
(0,0)
(0.5,1.0)
 Includes detector-related readnoise, dark current and photon
statistics

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Output drizzled image (lower
2 panels):
– 1/2 the input pixel scale
– two different greyscale stretches:
• extended large-scale PSF
• improved PSF core sampling
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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Analysis of Dithered Images
Properties measured:
Centroid and photometry of each star, using DAOphot
 FWHM of each star, using SExtractor
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Verification checks performed:

measurements compared between input values, dithered exposures, and
output drizzled images
Metrics calculated:
Astrometry: measured position of each star, relative to its input position:
 Photometry: measured count-rate of each star, relative to input values
 For both astrometry and photometry:

– compare width of the resulting distribution with the theoretical width
expected if the only sources of error were due to Poisson statistics, and not
undersampling
– Goal is to quantify additional error introduced by undersampling

FWHM: measured for each star, compared with theoretical value expected
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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Astrometry: Comparison of x,y values
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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Astrometry as a function of count-rate
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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Photometry Comparison
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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
FWHM Comparison
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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Summary of Results
Individual Dither Patterns / Filters:
Produced a set of 35 sets of plots like the preceding ones, for each dither
pattern and filter
 For each quantity (astrometry, photometry, FWHM), extracted one
quantity:

– Astrometry: dispersion of measured values, relative to theoretical dispersion
expected if undersampling did not contribute any error
– Photometry: dispersion of measured values, relative to theoretical dispersion
expected if undersampling did not contribute any error
– FWHM: Average width of bright stars, relative to theoretical FWHM for each
filter

All these quantities can be summarized on two plots:
– short-wavelength filters F070W, F110W, F150W, F200W
– long-wavelength filters F270W, F357W, F444W
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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
NIRCam Short-Wavelength: Results
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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
NIRCam Long-Wavelength: Results
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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
NIRCam Dither Patterns: Overall Results
FWHM:
significant improvement generally obtained from 2 to 3-point
 further improvement from 3 to 4-point, with near-optimal results
 9-point pattern (1/3-pixel 3x3) does not yield significant further gains likely because 1/2-pixel 2x2 dither already provides Nyquist sampling

Photometry:
Improvements are more gradual than for FWHM; for several filters,
optimal results are obtained only for the 9-point (1/3-pixel 3x3) pattern
 Most likely related to the fact that photometric fidelity is driven more by
pixel-to-pixel effects

Astrometry:
Generally, optimal astrometry is only obtained with a 9-point dither
pattern
 Likely related to a combination of pixel-to-pixel effects as well as
Nyquist sampling

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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Overhead Time Evaluation
Consider 3 types of program (following NIRCam OCD):
short programs, representative of bright star surveys
 medium programs, eg moderately faint extragalactic programs
 long programs, aimed at deep imaging

Scenario A: N dithers, cycle filters at each dither pattern
Scenario B: N dithers, cycle filters only once
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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Final Results: Cost/Benefit Metrics
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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Summary & Conclusions (NIRCam)
For short programs:
9-point patterns are generally not optimal
 for 3- or 4-point patterns, infrequent filter changes are significantly more
cost-effective
 for 2-point patterns, filters could be changed at each dither without too
much penalty

For medium programs:
for 2,3,4-point patterns, overhead difference is less when changing filters
at each dither point, so this might be desirable to produce more uniform
data
 9-point patterns are probably generally not worth the additional overhead

For long programs:

Relative cost increase for 9-point pattern is less, reaching a maximum of
only 3% of total observing time, even when changing filters at each dither
point, so this will likely be preferred in most cases
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STScI TIPS/JIM
15 September 2005
Optimizing JWST Dither Strategies
Anton Koekemoer (INS)
Future Work
NIRCam:
include effects of intra-pixel sensitivity variations
 include geometric distortion, which produces different subsampling
across chip
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Other instruments:
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FGS-TF: can adapt current code relatively easily, since detectors are the
same
MIRI imaging mode: code can be run with some modifications
MIRI/IFU - study already conducted by A. Glasse, combining instrument
design with dither pattern optimization
MIRI/LRS - code will need work to adapt to spectroscopic observations
NIRSpec: preliminary study by M. Regan
Broadening the science applications:
Include different galaxy types (exponential disks, r1/4 profiles, etc)
 Simulate galaxy luminosity functions and possible selection effects
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