Infrared Data Reduction
K. Michael Merrill
7/1/2016
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Windows on the Universe
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Electromagnetic Spectrum
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Historic Perspective
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1960’s - Discrete pixel devices
1968 - Two Micron Sky Survey to K=3
1970’s - AFCRL Rocket Survey
1980’s - IR arrays deployed
1983 - IRAS deployed
1990’s - Rapid growth in array technology
1998 - 2MASS to K=14
2003 - SIRTF
201? - JWST
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Atmospheric
Transmission
1 - 6 mm
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Atmospheric Transmission 6 - 30 mm
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Background sky radiation
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Background sky radiation
OH airglow
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Thermal emission
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The Operations Challenge in the IR
•The sky is always bright (and variable on many time scales)
•Site selection
•Adopting an observing strategy with active sky subtraction
•The telescope can be seen in thermal emission
•Reduce mirror emissivity & do not warm baffle
•Re-image the telescope mirror inside the instrument & cold baffle
•The instrument can be seen in thermal emission
•Cool the instrument in vacuum (at or below 77K)
•The array can see itself
•Cool the array as needed (77K, 30K, 8K, depending on device)
•Observations tend to be background (rather than detector) limited
•Detecting 2X fainter takes 4X longer
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Planck function:
black-body radiation
Wein’s Law:
 max
2898mK
Emittance = T4
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InSb Array Development
at NOAO
March of the pixels:
•58X62 (smallest box)
•256X256
•1024X1024 ALADDIN - deployed
worldwide
•2048X2048 Orion - active development
NEWFIRM footprint with 4 Orion
detector focal plane mosaic
Science in the raw:
•H2 gas emission (left insert)
•PAH dust emission (middle insert)
•JHK color composite (right insert)
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Backdrop: 2MASS JHK view of the Orion Nebula
Orion Focal Plane Module
Clock and Biases
Output Current Mirrors
Light Baffles
Outputs 1-32
Outputs 33-64
Invar36 Pedestal
AlN
Motherboard
Alignment Locator
Detector SCA
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Photo Courtesy RIO12
2X2 Mosaic of Orion Modules: 4098X4098
Build a 4Kx4K
Focal Plane
from four
Orion Modules
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MBE HgCdTe Cross Section
Silicon Read Out IC
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Incident Photons
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InSb Array Cross Section
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•Non-destructive Readout
Pixel Readout
•Photo-electrons accumulate until reset
•Difference between two reads minimizes fixed pattern noise
kTC noise
Readout
Double Correlated Sampling:
Fowler 1
0V
Time
Reset
CDS Signal
Diode Bias Voltage
Reset
0.5 V
Readout
•Non-destructive Readout
Pixel Readout
•Photo-electrons accumulate until reset
•Difference between two reads minimizes fixed pattern noise
kTC noise
Readouts
Multiple Correlated Sampling:
Fowler N (=4)
0V
Time
Reset
MCS Signal
Diode Bias Voltage
Reset
0.5 V
Readouts
Read noise
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SQIID Optical Schematic
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SQIID: dichoric side
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SQIID channels from the camera side
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Old SQIID
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Mosaic: a grid of spatially offset images
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Registered composite images at
K(2.2mm), H(1.6mm) & J(1.25mm)
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NGC 2024: the Flame Nebula
Visible:Red
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IR: JHK26
SQIID
JHK composite
of the Galactic
Center Region:
7X7 dithered
spatial grid
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Sgr A @ K
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Star Speeds Around Milky Way’s Black Hole
QuickTi me™ and a YU V420 codec decompressor are needed to see thi s pi ctur e.
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Galactic Center
IR Composite
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Sgr A at 3 to 4 microns
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Galactic
Center in
Brackett Alpha
Brackett Gamma
and Molecular
Hydrogen
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Galactic Center at 9/13/21 microns
Visible
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Near IR
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Multi-wavelength astrophysics with SQIID:
simultaneous operation of 4 arrays sharing a single FOV through dichroics
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M17: the Omega Nebula
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Image processing: separating the
stars from the debri of gas and dust
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NGC 7129: JHK SQIID composite
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NGC7538: JHK SQIID composite
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W3 IRS1
K L L’ composite
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M42: the Orion Nebula
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Views of Orion Molecular Cloud 1
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Egg Nebula in Polarized Light
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High Background
Science
Imaging at the South Pole
•NOAO Abu system on
SPIREX
•two season demonstration
•relentless observing
•limited by data flow, not
natural background
Challenge to excel…
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NGC6334 - PAH,L,M’ composite
S106
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S106
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Data cube
The spatial grid of
long slit spectra can
be assembled into a
3D structure, then
sliced along the
dispersion axis (by
wavelength) to yield
registered images
throughout the
spectral range.
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S106: infrared
spectral imaging
Observations at the KPNO 1.3m with
the Cryogenic Spectrometer (CRSP) at
a (2 pixel) wavelength resolution of
2000 across a single spectral baseline
from 2.12 mm to 2.25 mm stepping the
slit to map the source simultaneously.
Left Panel: molecular H2 lines
[red:v=1-0 S(1) at 2.122mm; green:v=10 S(0) at 2.224mm; blue:v=2-1 S(0) at
2.248mm]. These line ratios depend
sensitively on excitation (fluorescence
or dynamic shock) and density.
Right Panel: ionized lines of hydrogen
Brackett g
m
and
3
3
[FeIII] (green: G5- H6 at 2.218mm;
blue:3G5-3H4 at 2.242mm). These line
ratios depend on excitation, density
and temperature.
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S106
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H2
Br g
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SQIID Data Processing Overview
•The NOAO SQIID Infrared Camera produces simultaneous images of the same field
in the J, H, K, and narrowband L passbands, using individual 512X512 quadrants
of ALADDIN InSb arrays.
•The observations are generally background (photon statistics) limited.
•Typical observing programs include:
• taking a few (2-5) exposures on the same target with small offsets (to counter
ghosts and bad pixels and improve spatial sampling of the images)
•taking many exposures of the same target with a dither pattern of offsets
(to build up long exposures) - DEEP
•spatial mosaics of dithered pairs of images covering larger regions with limited
overlap between images (to build up large images) - WIDE.
•These three kinds of observations are distinguished because they require somewhat
different data reduction strategies.
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SQIID Data Processing Overview
•UPSQIID software initiated by K. Michael Merrill in 1988 to provide data
reduction tools for exploiting the emergent IR array technology at NOAO.
•Aim to provide an analysis cookbook for IR arrays with seasoned recipes and
to provide a on-site quick-look capability to enhance observing efficiency.
•Near-term goal to provide system-wide IRAF routines for general-purpose IR
data reduction and multi-wavelength analysis with immediate application to
the SQIID four-color infrared camera.
•Software "hooks" useful for multi-wavelength image registration incorporated
into the IRAF imcombine, iralign/irmatch and imalign/imcentroid tasks.
•Using simple IRAF script procedures, a core set of tasks to perform list-based
infrared image processing, including sky subtraction, accurate flat-fielding,
bad pixel masking and response linearization were generated to regularize
the complex task of IR data reduction:
usqdark, usqsky, usqflat, usqcorr, and usqproc.
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SQIID Data Processing Overview
•These programs reside in the “upsqiid” IRAF package as a set of IRAF
cl script procedures designed to facilitate the reduction of SQIID datasets.
•They have a number of imperfections in the user interface (especially a large
number of irrelevant parameters) and currently do not have help files.
•Package has evolved from “sqiid” to “sqiid211”, “abu”, “spabu”, & “upsqiid” to
meet changes in array size and in IRAF.
•Spectroscopic elements have been incorporated to handle low to moderate
spectral resolution imaging spectroscopy (grism and grating): slit is stepped
orthogonal to the dispersion axis to build up a spectral data cube.
•Although not a formally supported IRAF package, it is in wide distribution.
•Current location of SQIID information, manuals, and IRAF packages:
<http://www.noao.edu/kpno/sqiid/>
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SQIID Data Processing Overview
•Users are cautioned that IR image datasets often present a greater data
reduction challenge than optical CCD images both due to the superior
performance of optical CCD detectors (lower dark current, readout noise,
and pixel to pixel sensitivity variations) and especially due to the extreme
background limited nature of most IR observations.
•The results at each step in the process should be carefully examined and
problems understood before proceeding.
•Many problems can be solved by excluding bad images from the datasets.
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SQIID Data Processing Overview
•IR observations generally produce spatially composite images.
•Since imaging is background limited with integration times measured in
minutes, many on-target observations, interspersed with off-target sky
frames are required to attain high sensitivity.
•Owing to the restricted field of view of IR detectors, sources larger than a
few arc minutes are best mapped using a series of pointed observations
in an overlapping grid.
•Dithering compensates for bad-pixels in the on frames and assists source
rejection in the off-frames.
•Image overlap assures proper spatial registration and background intensity
matching among the individual frames.
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SQIID Data Processing Overview
•Image registration highly interactive task not generally amenable to automation.
•Interrelated scripts produce a generalized registration database used by the
IRAF imcombine task to produce a composite image of an arbitrary set of
connected images not necessarily sharing a common overlap region.
•Spatial registration, using the interactive IRAF centroid task for an overlapping
grid and the interactive xyget script for groups of overlapping images, relies
on position matching individual stars appearing on multiple images.
•Groups of images merged into a registration database by mergecom script.
•Since SQIID images at JHKL are taken simultaneously, registration using one
channel (usually K) implicitly registers the other channels.
• xyadopt script produces registration databases for the other channels from
the master channel registration database using the relative linear geometric
transformation (offset, rotation, & magnification) between channels based on
observations of globular clusters by the interactive getmap script.
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SQIID Data Processing Overview
•Intensity offsets for data which do not contain a single region in common are
determined by the zget script.
•nircombine script produces a composite image from the registration database.
•Each frame is masked for bad pixels, geometrically transformed to lie atop the
master channel frame, shifted linearly in X and Y, intensity offset as required
according to the master database and combined into a composite image using
the IRAF task imcombine with threshholding (to exclude bad pixels) and
median filtering at each pixel in the resultant image.
• Careful comparison of photometry of individual and composite images has
determined that these modest linear geometric transformations have no
significant effect on aperture photometry.
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SQIID Data Processing
•A "dataset" is the set of direct observations of a given field.
•These may be dithered observations of a single target (DEEP) or a mosaic of a
larger region (WIDE).
• In the extreme case, the dataset may contain only a single exposure (and only
the first two steps listed below would be required).
•The basic path for the reduction of SQIID dataset can be described as follows:
•Create DARK, FLAT, and SKY frames for each passband.
•Process each individual source frame to remove instrumental dark current,
sky background, and pixel to pixel sensitivity variations.
•Combine all images of a given target into a "database".
•Interactively define the relative spatial offsets between each image in the
database for one of the channels.
•Combine the images in the database into a single image suitable for
analysis, using bad-pixel masks to exclude bad pixels.
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SQIID Data Processing Overview
Calculate average background
radiation and subtract it from the raw
images.
 Flat-field difference images.
 Register the flat-fielded difference
images.
 Median through the registered
images to produce the final image.

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DARK, FLAT, and SKY Frames
• Proper data reduction requires accurate solutions for :
• the small additive effects of internal illumination and charge generation
(DARK frames)
• the large additive effects of sky illumination (SKY frames)
• the multiplicative effects of position dependent pixel sensitivity
(FLATFIELD frames).
• Creation of DARK, FLAT, and SKY calibration frames are the first step in the data
reduction process.
• DARK frames are simple to obtain and process
• FLAT and SKY frames are more difficult to create and are crucial to the quality of
the final images. IR observations are extremely background limited and the
background in the near-infrared is variable at many temporal and spatial scales.
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Darks
DARK frames (taken with the internal cold dark slide in place) are stable over
a night and are probably stable over an entire observing run.
Changes in dark current can accompany changes in the array temperature.
Since the SQIID dark current has both a base level and a time-dependent
component, a dark frame must be created for each exposure time (that you
intend to use to determine flat-fields or otherwise require dark subtraction)
during the observing run.
Composite DARK frames are created with the task “usqdark” :
• typically 7 - 9 frames are sufficient
• submitted frames are averaged using per pixel minmax rejection
• no image scaling or zero-point adjustments are allowed
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Flat-Field
•Pixel to pixel sensitivity (i.e., the flat-field) is generally stable over the night
and perhaps several nights.
•Unlike optical CCD observations, images of the interior of the dome or the
twilight sky are not practical for the flat-fielding of infrared images.
• Since FOV of infrared array detectors now larger than the sources of interest,
the sky background may be relatively easier to compensate, but since IR
sources are often much fainter than the sky background very precise flat-field
calibration is required.
• IR background results in sky levels with S/N of better than 100 in single
exposures (order 30 sec at J, H, K and order 1 sec at L band).
•Taking the median of a reasonable number of blank fields (or target fields
without large objects) obtained at different times during the night and at
different locations on the sky, a satisfactory measure of the system flat-field
can be derived.
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Flat-field
•Since direct illumination of the array is possible (IR secondary mirrors are
usually undersized), observations near bright sources (e.g. the Moon),
with atypical illumination should not be used to determine global flatfields.
•Observations of illuminated targets inside the dome and during twilight will
also have illumination atypical of nighttime observations. Also15 minutes
into astronomical twilight, it is doubling every five minutes and you need
to be cautious about observing it when it's too bright
•Flat frames are examined and compared to those created on other nights.
•A common problem is the presence of a large source (e.g. a galaxy) in the
center of many observations. This can be ameliorated by the observation of
sky frames, the combination of distinctly different observing programs during
the same night, and the use of sufficiently large dithering motions.
•Post-observation options include the use of carefully selected observations
which avoid overlap of the stronger sources.
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Flat-Field
•The SQIID system response is stable and is very flat across the arrays.
•Consequently, the flat-field for each channel should be stable at the percent
level under normal illumination and global flat-fields can be constructed
which are viable for extended periods of time.
•A list of suitable observations with comparable median statistics for each
frame, carefully edited to exclude fields with sources larger than the
dithering range, is prepared.
•Composite FLAT frames are created with the task "usqflat” by:
•subtracting a DARK frame (taken with the internal cold dark slide in place)
from appropriately selected sky frames
•normalize each dark-subtracted frame by dividing by its the median value
within the central region
•no additional image scaling or zero-point adjustment is allowed
•per pixel median filter the ensemble to produce FLATFIELD with high S/N.
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Sky
•Background illumination in the near IR is far from stable with time or location.
•In the near IR (especially at J) moonlight (direct or reflected off clouds) is a factor, and
the night sky emission (especially at H, K, and L) is a function of temperature and
humidity. H can vary by a factor of 2 and J can vary by 40% on hour time scales.
•Although 10 to 30 percent background variations do not strongly limit the S/N of
observations (except at K and L for large changes in temperature), they greatly
complicate both the creation of mosaics of large regions and accurate surface
photometry of objects with extents comparable to SQIID's field of view.
•Such observing programs, need a sufficient number of exposures (and intermixed
sky exposures if necessary) to create a sky frame for that specific observation.
•Programs with single or a few observations of many targets, a sky calibration
based on the average over the night is the best that can usually be accomplished.
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Sky
•After examination of the individual images a list of suitable images is prepared.
•Composite SKY frames are created with the task "usqsky”:
•submitted frames are brought to a common mean/median within a fixed image
subsection using per frame zero-point adjustment and pixel median filtered
zero-point adjustments
•no image scaling is allowed
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UPSQPROC: processing with discrete sky frames
Each image (except for dark exposures and blank sky frames) needs to
be processed prior to combining the collective datasets into dithered
images or mosaics. The processing applied is:
•Optionally correct the data for non-linearity
•Subtract BLANK frame (DARK or SKY to remove bias and dark
current)
•Normalize (divide) by the FLAT frame
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MOVPROC: processing with moving median sky frames
•MOVPROC list processes raw image data using sky frames generated for each
processed image.
•For a given image the running (moving) median from a subset of frames within a
selected list distance of each frame (excluding the given frame; no distinction
between on and off source frames) is processed to produce a SKY frame.
•The number of frames which enter into the sky can be selected and whether they
precede or span the image.
•Appropriate for "quick look" type processing of data where the stability of the sky
is either unknown or is known to be varying on a time scale shorter than the
full data set (grouping skys over long periods is inadequate).
•To facilitate list processing, the data processing can begin and end at selected
element in the list.
•Data which are off source can be optionally included in the running sky frame
generation but withheld from full processing into final images.
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Average Background Radiation
(MOVPROC)
A
Off-Source
Median
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B
B
On-Source
A
Off-Source
Median
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PATPROC: processing data in fixed patterns
PATPROC processes raw image data generated for representative data acquisition
protocols which intersperse object (+) and sky (-) frame in known fixed repeating
patterns. These patterns vary in the amount of time devoted to off target images:
all_on: + + + + + +
all time on source field
pair: +- +-+ equal time on and off
triad: +-+ +-+ +-+ 2/3 on and 1/3 off
alt-triad: -++ -++ -++
quad: +--+ +--+ +--+ equal time on & off with less telescope motion
alt-quad: -++- -++- -++-
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PATPROC: processing data in fixed patterns
•Data are normally processed using the same protocol as UPSQPROC.
•Sky frames are generated from the data (sub)set as part of the data processing.
•Sky frames can come from all the data, all the off-source frames, or the
off-source frame nearest in time to the data sequence to a given frame.
•The data at each +|- pattern position can either be single frames or same-size
groups of images.
•When observing conditions permit one to benefit from a composite sky
(improved noise and spatial structure rejection), one can generate composite
off-source frames for subtraction from the appropriate on-sky frames.
•Each group (size="multiple") off-field images is used to generate composite sky
frames for each member of the (size = "multiple") 0 group. The member of a given
group of on-source frames is processed using the group sky frame nearest in time
in the data sequence.
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Average Background Radiation
(PATPROC)
A
Off-Source
Median
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B
B
On-Source
A
Off-Source
Median
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Building a Database from a Dataset
•
•
•
After processing the individual frame, each dataset needs to be
converted into a "database".
As with datasets, each database contains the observations of a single
field or target in a single filter passband.
The database construct is used to bring the individual exposures
together into the desired final image (dither or mosaic) with
appropriate spatial and intensity offset registration.
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Building a Database from a Dataset
The 'UPSQIID' package contains a set of image registration tools
designed to facilitate image registration and combination. The image
registration process naturally separates into the following steps:
• Determine the relative spatial offsets between images for a selected channel
• Link these relative offsets into a single map which contains
the offset of each image relative to the origin of the final image.
• Determine the relative intensity offsets between images which overlap.
• Combine the images into a single image using bad-pixel masks to
eliminate bad pixels.
• If possible, geometrically transform images at other channels to the
selected channel so that they do not need to be spatially registered
separately (this is a feature of SQIID).
The ’UPSQIID' package image registration tools are designed to operate from
ordered image lists.
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Building a Database from a Dataset
•The initial data flow through the system is determined to a large extent by the
degree of spatial overlap among the images in a given data set.
•IR observations of extended regions (FOV greater than array FOV) are built up fro
spatial grids of pointed data sets which overlap on the edges.
•While the degree of spatial overlap is an uneasy compromise between the competing
desire to map efficiently and the need to have objects in common along the edges,
these data do not in general share a common overlap region.
•IR observations of high sensitivity are built up from pointed data sets which are
heavily overlapped and share a (generally large) region in common. (The process
of producing such data multiply overlapped with relatively small spatial offsets
between frames is commonly called "dithering".)
•Actual data collection may involve a complex hierarchy spatial offsets (e.g., grids
of dithered data) and might require combination of registration techniques.
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Building a Database from a Dataset
The dataflow for purely WIDE or DEEP observations through
the 'UPSQIID' package image registration tools proceeds as follows
after generation of the appropriate image list:
tile spatially ordered image
interactive spatial registration
link registration
intensity match
make composite image
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WIDE
usqmos
center
xylap
DEEP
xyget
zget
nircombine
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Building a Database from a Dataset
•These tools generate a database which links the untouched processed data (generally
either stored as mosaic of tiled images using USQMOS, or as lists of images) via
a prescription which describes how each image is the be transformed, shifted,
intensity offset, and masked to make a composite image.
•The input images (in the database mosaics or lists) are not themselves modified
during this process; rather, each tool in turn inserts the necessary information
into the database text files.
•When required, temporary copies of the images are created, modified, and discarded
at task completion.
•One can edit the prescription as required to meet the special needs of each data set.
•Tools are provided for merging registration data which share a common image into
larger databases.
•Ultimately, the images are combined according to the database prescription using
the powerful IRAF task 'imcombine'
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Registration – Manual
Image0nn.fits
Image001.fits
Compare
Click on reference
star
Click on the same
star in every image.
Shifts
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Registration – Auto
Reference
Segment Stars
Compare Star
Positions
Shifts
Image # n
Segment Stars
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Combination (Co-adding)
Registered difference
images
Final Image
Median through
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Correction for system drift and distortions
Corrections must be made for:
•Intensity non-linearity owing to device physics
•Intensity zero-point drifts owing to device physics - now
possible using detector reference pixels
•Spatial distortion owing to instrument and telescope optics
and relative array placement
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Cdet (fF)
Intensity non-linearity
45
40
35
30
25
20
15
10
5
0
-0.60
Cdet(fF) vs. Vdet(volts)
-0.50
-0.40
-0.30
-0.20
-0.10
0.00
Vde t (volts )
Sample InSb detector junction capacitance versus
detector bias from 0.0 to -0.6 volt reverse bias
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SQIID Linearity
Relative error for H (dashed), K (solid), and J (dotted) Channels.
Conversion gain is approximately 11 electrons per ADU.
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Reference pixels
Spatial averaging along a
row using reference column
to correct for bias drift
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Combined temporal averaging
using multiple reads of reference
83
column
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Data Pipelining with Abu/Spirex
Status Report 3/22/99
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The Pipeline is Up and Running





Automatic packaging and shipment of
files
Single point of contact at RIT
Automatic logging of sky brightness
Automatic logging of standard calibration
Automatic reduction and picture assembly
– Standard scripts for sky subtraction & dither

Based on NOAO abu/upsqiid package
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System Performance

Routine monitoring of:
– sky brightness
– sky dips
– Photometric reproducibility

Working presently only at 3.3um
– To baseline performance vs temperature
– To establish & debug procedures & protocols
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Sky brightness and Ambient Temperature
500
300
Sky Brightness
280
31Jan
300
9Feb
260
10Feb
200
7Mar
100
240
Temperature (Kelvins)
Sky (Counts per Second)
400
18Mar
Ambient Temperature
220
0
0
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20
40
60
Frame Number (Time Ordered)
80
100
91
Sky Brightness and Stability

Sky Brightness & Stability are dramatically
different on clear and partly cloudy days
– A robust measure of quality
– For the telescope operator & for the data user
– The pipeline monitors sky brightness for every
frame received.
– Sky Brightness and Stability will be used by
the pipeline as quality metrics
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T rying to Observe When Partly Cloudy
1400
Cloudy 9/2
Clear 10/2
Counts per Second
1200
1000
800
600
400
200
0
20
40
60
80
100
120
140
160
Frame Number (30 Second Exposures)
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Sky Dip Measurements

Measure the sky brightness vs airmass
 Compare with cold load (LN2 target)
 Measurements show:
– Sky is very dark
– Emission from telescope dominant at A.M. < 2
• System emissivity is of order 15%
– Uncertainty in “ambient load” dominates calculation
– Baffle tube issue is resolved (LN2 signal small)
7/1/2016
94
Skydip Measurements -- 18 March 99
Ambient T emperature = 218 K
500
Counts per Second
400
300
200
Sky Emission (~ 45 Counts/Airmass)
Telescope Emission (~100 counts/sec)
100
LN2 target (20 counts/sec)
0
0
2
4
6
8
10
12
Airmass
7/1/2016
95
As Night Falls...


Emission from both
sky and telescope are
observed to decrease
No indication of “stray
heat” problems
Further gains still
expected as night falls
Sky Dips at 242K and 216K
700
600
skydip@242K
skydip@216K
500
Counts per Second

400
300
200
100
0
0
1
2
3
4
5
6
Airmass
7/1/2016
96
Photometric Stability


Ice buildup on mirror
identified last season
as a limiting factor
Observing protocol
now to point away
from incoming wind
Photometric stability
is improved
Repeated Photometric Measurements
T wo bright standard stars; delta airmass = 0.34
(over a period of hours)
14.4
Photometric Zero Point (Magnitudes)

14.3
HR2015
HR2015
14.2
14.1
14
HR2451
13.9
0
5
10
15
20
25
30
35
40
Single Short Exposures
7/1/2016
97
7/1/2016
98
The IR Challenge

Within the last quarter century, infrared detectors have evolved from
individual discrete devices to high tech aggregates of millions of pixels.
 The scientific drivers for yet more pixels have kept apace - already
several projects are dependent on multiple 2K X 2K arrays to reach their
goals and next generation facilities envision focal planes paved with
detector tiles.
 To service such focal plane composites requires sophisticated control of
multiple devices, but management of the digitized data flow off the focal
plane through the data pipeline to the investigator and the archive looms
equally large.
7/1/2016
99
Science/Technology Drivers



Astronomy continues to move in the direction of larger telescopes, higher
spatial/spectral resolution instrumentation, and larger image fields:
– Science of scale requires measurement of large areas of the sky at depth
– Science of change requires commensurate measurements over time
– Indentification and census of rare objects requires mining of large areas
– Need for sample completeness and statistical accuracy requires measurement of
multiple sources at the same time
Scarcity & high cost of observing resources demands an observing environment that:
– reliably delivers accurately calibrated, repeatable observations
– must be flexible - optimized to meet the diverse needs of the science
– must be adaptable to accommodate changing needs
– is capable of relentless operation with high efficiency
Science data flow requires:
– uniformity in acquiring, processing, and archiving image data
– rapid turnaround of very large data volumes
– optimal coupling with data processing, instrument, and telescope systems
7/1/2016
100