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Astronomical Data Analysis Software and Systems VII
ASP Conference Series, Vol. 145, 1998
R. Albrecht, R. N. Hook and H. A. Bushouse, e
Ö Copyright 1998 Astronomical Society of the Pacific. All rights reserved.
ds.
The STScI NICMOS Calibration Pipeline
Howard A. Bushouse
Space Telescope Science Institute, Baltimore, MD 21218, Email:
bushouse@stsci.edu
Elizabeth Stobie
University of Arizona, Steward Observatory, Tucson, AZ 85721
Abstract. The NICMOS data reduction and calibration pipeline em­
ploys file formats and software architecture techniques that are new and
di#erent from what has been used for previous HST instruments. This
paper describes the FITS file format used for NICMOS data, which in­
cludes error estimate and data quality arrays for each science image, and
describes the approach used to associate multiple observations of a sin­
gle target. The software architecture, which employs ANSI C language
algorithms and C bindings to IRAF libraries, is also described.
1. File Formats
The NICMOS archival and run­time file format is FITS, with extensive use of
IMAGE and BINTABLE extensions. Each image from NICMOS is represented
by five data arrays that are stored as five IMAGE extensions in a single FITS
file. The five data arrays are: 1) the science (SCI) image from the instrument; 2)
an error (ERR) image giving estimated uncertainties for the SCI image values;
3) a data quality (DQ) array containing bit­encoded flag values that represent
known problem conditions with the SCI image values; 4) an array containing the
number of samples (SAMP) used to derive the SCI values; and 5) an exposure
time (TIME) array giving the total e#ective exposure time for each pixel in the
SCI image.
This group of five data arrays is called an image set, or ``imset'' for short.
NICMOS operation modes that produce multiple readouts of the detector dur­
ing a single exposure have multiple imsets in a single FITS file; one imset per
readout. Multiple observations of a single target are stored in separate FITS
files, but the data are logically associated through the use of an ``association
table'', which is a FITS file containing a single BINTABLE extension, listing
the names and other relevant information for each observation in the associa­
tion. One­dimensional spectra that are extracted from NICMOS grism images
are also stored in a BINTABLE extension of a FITS file. There is never any
image data in the primary header­data unit (HDU) of NICMOS FITS files; all
data are stored in IMAGE or BINTABLE extensions. The primary HDUs only
contain header keywords with information that is relevant to all extensions in
the file.
300

The STScI NICMOS Calibration Pipeline 301
2. Software Design
All of the NICMOS pipeline calibration software has been written in the ANSI
C language. A set of data I/O routines, known as ``hstio'', has also been written
which is used to easily perform the complex mapping between the disk file for­
mats for imsets and their representations as NICMOS­specific C data structures
within the pipeline code. The hstio library, in turn, uses a set of C bindings
to IRAF image I/O libraries to perform the actual file access and data I/O.
C bindings to other IRAF, STSDAS, and TABLES package libraries also pro­
vide access to existing mathematical and analysis algorithms, as well as FITS
BINTABLE data I/O. The algorithms that apply the actual calibration steps
to the science data are completely isolated from and independent of the data
I/O routines. The calibration tasks are built in such a way that they can be
run from either the host operating system level or as an IRAF native task from
within the IRAF cl.
3. Image Associations
Multiple NICMOS observations of a single target field are often obtained for one
of several reasons. Multiple exposures at a single sky position can be obtained
in order to allow for rejection of cosmic ray hits when the images are later
combined by the calibration pipeline. Small­angle dithering of a compact target
within the instrument's field of view is useful for removing the e#ects of defective
pixels and for averaging out residual flat fielding uncertainties. For observations
at longer wavelengths, where the thermal background signal from the telescope
and instrument begins to be significant, it is necessary to alternately ``chop''
the telescope field of view between the target and nearby blank sky positions
in order to obtain measurements of the background signal. For very extended
sources an image of the entire target can be created by ``mosaicing'' individual
observations obtained at many sky positions.
These sets of observations are known as image ``associations''. The associ­
ations are a logical grouping only, in that the individual observations that make
up an association are initially stored and processed separately, but are eventually
combined by the pipeline software into a final product. The individual obser­
vations are first processed, one at a time, by the calnica task, which performs
instrumental calibration, and are then processed as a group by the calnicb
task, which combines the images, measures and removes background signal, and
produces final mosaic images. Information about the association is stored in
an association table, which is a FITS file with a single BINTABLE extension.
The table extension lists the names of the observation datasets contained in the
association and their role in the association (target or background image).
4. CALNICA Pipeline
The NICMOS pipeline calibration is divided into two tasks. The first task,
calnica, applies instrumental calibration to individual exposures, including as­
sociated and non­associated observations. The task is completely data driven
in that all information necessary to accomplish the calibration is read from

302 Bushouse and Stobie
image header keywords. These keywords contain instrumental configuration pa­
rameters, such as camera number, filter name, and exposure time, as well as
``switches'' that indicate which calibration steps are to be performed, and the
names of reference files (e.g., flat fields, dark current images) that are needed
for the calibration.
The major steps performed by calnica include the masking (setting DQ
image values) of defective pixels in the images, the computation of uncertainties
(setting ERR image values) for the image data, which includes both detector
readnoise and Poisson noise in the detected signal, dark current subtraction,
correction for non­linear detector response, flat fielding, and conversion from
detected counts to count rates. Each calibration step that involves the applica­
tion of reference data (e.g., dark subtraction, flat fielding) propagates estimated
uncertainties in the reference data into the ERR images of the data being pro­
cessed.
Another major step performed by calnica is the combination of images gen­
erated by the MultiAccum operational mode of the NICMOS detectors, in which
multiple non­destructive detector readouts are performed during the course of
a single exposure. The images generated by each readout are first individually
calibrated and are then combined into a single, final output image. The image
combination is accomplished by fitting a straight line to the (cumulative) expo­
sure time vs. detected counts data pairs for each pixel, using a standard linear
regression algorithm. Cosmic ray hits in individual readouts are detected and
rejected during this processes by searching for and rejecting outliers from the
fit. The fit and reject process is iterated for each pixel until no new samples
are rejected. The slope of the fit and its estimated uncertainty are stored as the
final countrate and error, respectively, for each pixel in the output image SCI
and ERR arrays. In addition, the number of non­rejected data samples for each
pixel and their total exposure time are stored in the SAMP and TIME arrays,
respectively, of the final image.
5. CALNICB Pipeline
The second phase of the NICMOS pipeline is performed by the calnicb task,
which processes associated observations, each of which must have already re­
ceived instrumental calibration by calnica. This task is also completely data
driven, with processing information coming from image header keywords and
the association table. The three major steps performed by calnicb are: 1)
combine multiple images that were obtained at individual sky positions (if any);
2) measure and subtract the thermal background signal from the images; and
3) combine all the images from a given observing pattern into a single mosaic
image.
Steps 1 and 3, which both involve combining data from overlapping images,
have many common features. First, proper registration of the input images is
accomplished by estimating the image­to­image o#sets from world coordinate
system (WCS) information contained in the image headers, and then applying
a cross­correlation technique to further refine the o#sets. The cross­correlation
uses a ``minimum di#erences'' technique which seeks to minimize the di#erence
in pixel values between two images. Second, shifting the images to their proper

The STScI NICMOS Calibration Pipeline 303
alignments is done by simple bi­linear interpolation. Third, the combining of
pixels in overlap regions uses the input ERR array values to compute a weighted
mean, rejects pixels flagged as bad in the DQ arrays, rejects outliers using itera­
tive sigma clipping, and stores the number of non­rejected pixels and their total
exposure time in the SAMP and TIME arrays, respectively, of the combined
image.
The process of background measurement varies depending on the exact
makeup of the image association that is being processed. If observations of
background sky locations were obtained by using an observing pattern that in­
cludes chopping, then only these images are used to compute the background
signal. If background images were not obtained, then an attempt is made to es­
timate the background from the target images, with an appropriate warning to
the user that the background estimate may be biased by signal from the target.
A scalar background signal level is measured for each image by computing its
median signal level, excluding bad pixels and iteratively rejecting outliers from
the computation. The resulting background levels for each image are then av­
eraged together, again using iterative sigma clipping to reject discrepant values
for individual images. The resulting average value is then subtracted from all
images in the association, both background (if present) and target images. Pro­
visions have also been made for subtracting a background reference image which
could include spatial variations in the background signal across the NICMOS
field of view, but to date no such spatial variations have been seen.
At the end of calnicb processing an updated version of the association
table is created which contains information computed during processing, includ­
ing the o#sets for each image (relative to its reference image), and the scalar
background level computed for each image. An example of such a table for a
``two­chop'' pattern is shown in Table 1. This pattern contains one position
on the target and two separate background positions. Two images have been
obtained at each position. The two images at each position are first combined,
the background is measured from the combined images at each background posi­
tion, the average background is subtracted from all images, and then three final
images are produced, one for the target and one for each background position.
Table 1. Output Association Table.
MEMNAME MEMTYPE BCKIMAGE MEANBCK XOFFSET YOFFSET
(DN/sec) (pixels) (pixels)
n3uw01a1r EXP­TARG no INDEF 0.00 0.00
n3uw01a2r EXP­TARG no INDEF ­0.15 0.00
n3uw01a3r EXP­BCK1 yes 0.28 0.00 0.00
n3uw01a4r EXP­BCK1 no INDEF 0.00 0.20
n3uw01a5r EXP­TARG no INDEF 0.32 0.00
n3uw01a6r EXP­TARG no INDEF 0.00 0.00
n3uw01a7r EXP­BCK2 yes 0.26 0.00 0.00
n3uw01a8r EXP­BCK2 no INDEF 0.00 0.12
n3uw01010 PROD­TARG no INDEF INDEF INDEF
n3uw01011 PROD­BCK1 no INDEF INDEF INDEF
n3uw01012 PROD­BCK2 no INDEF INDEF INDEF