was NICMOS’s initial standard method of combining exposures to create mosaiced images. During this period calnicb
was incorporated into the OPUS pipeline and recommended to users for use on their local machines. However, calnicb
does not correct for the geometric distortion present in NICMOS images, which is small but can be important for some applications (see Section 5.3
for a discussion). Due to this shortcoming calnicb
is no longer the recommended software for creating NICMOS mosaic images. We now recommend the MultiDrizzle
software package which can be run in PyRAF
and includes a geometric distortion correction.
In the following subsections we provide an overview of the MultiDrizzle
task and calnicb
. Even though we no longer recommend the use of calnicb by users on their local machines the software is still present in the OPUS pipeline and so the user may still come across calnicb
outputs while working with NICMOS data. For this and historical reasons we include a brief overview of the calnicb
algorithm but still recommend MultiDrizzle
for all NICMOS mosaicing applications.
At the time of writing of this handbook, the preferred method to
combine and mosaic NICMOS observation is to use the MultiDrizzle
routine in the stsdas.analysis.dither
package. The latest version of this routine now fully support NICMOS data and allows for users to optimally mosaic their data using the drizzle algorithm. MultiDrizzle
can be used to both create a combined mosaic of the available data, further refine the rejection of cosmic rays, and produce images with optimally resampled pixel size to make full use of all the information contained in original dithered data. A full description of how to run MultiDrizzle
with NICMOS data is available in the NICMOS section (Section 6.3) of the MultiDrizzle
task produces the combined, or mosaiced
, image from the multiple images contained in a NICMOS association. The task also performs background subtraction and source identification on the images in the association. It was designed to implement the potentially complex task of image mosaicing in a pipeline fashion, but sometimes a more careful, manual treatment can be worthwhile. As mentioned earlier calnicb
does not include any correction for geometric distortion in NICMOS images, nor does it allow pixel subsampling which may be useful in order to improve image resolution for undersampled images (especially with NIC3). Also, some NICMOS users dithered their observations “manually” using POS-TARG offsets rather than the canned patterns available in Phase II; such data sets will not be linked as associations in the HST
archive. Finally, you may wish to have more interactive control over the various stages of the background subtraction, registration, and coaddition process than calnicb
offers. For all these reasons we recommend that the user employ the MultiDrizzle
|The association table
): this is a table containing the list of members in the association and relevant information on the association type, as given in Table 3.1
The table extension header of the assoc_id_asn.fits
file also contains the keywords which control the background illumination pattern correction (illmcorr). The keywords used are: illmcorr (whether or not the correction is to be performed) and illmfile (reference file name for the illumination correction). These are further discussed later in this Section.
|The input calibrated images (ippssoot_cal.fits
): the science data images which are part of the association, as listed in the first column of the association table. The images are usually the calibrated outputs of calnica
|The support files (ipppssoot_spt.fits
), containing engineering information, so that calnicb
can transfer this information to the output support files.
produces three types of output:
|An updated copy of the association table (assoc_id_asc.fits
): this copy of the assoc_id_asn.fits
file contains additional information about the processing that took place. The assoc_id_asc.fits
file contains four additional columns, listed in Table 3.2
Additional information contained in the header of the assoc_id_asc.fits
table is the mean_bkg keyword, which gives the constant background signal level subtracted from all images in the association.
|One or more output mosaic images (assoc_idn_mos.fits
): the number of output mosaic images depends on the pattern. The target field is always contained in the assoc_id0_mos.fits
file. Patterns which involve chopping onto the sky to produce background reference images result in multiple assoc_idn_mos.fits
files after processing through calnicb
, with the background positions identified by n
=1 to 8.
support file for each assoc_idn_mos.fits
The basic philosophy of the calnicb
algorithm is to remove the background from each image after source identification, to align the images by calculating offsets, and to produce the final mosaic. The processing steps of calnicb
can be summarized as follows:
Header keywords from the input *_cal.fits
images are read and evaluated in order to guide the calnicb
processing. One set of keywords (Table 3.3
) pertains to the association as a whole and therefore are read only once from the first input member image:
A second set of header keywords (Table 3.4
) are specific to each member of the association, and must be read from each input image.
Based on this information, an inventory is taken of which input images
exist, where they belong in the pattern, how many images there are at each pattern position, which images belong to the target field, which ones are from background fields, and to which output mosaic image each input image will contribute. As part of the input process, the appropriate ILLMFILE reference file is loaded. Note, however, that the ILLMFILEs used in this step are dummies, and have no effect on the data. In fact, the ILLMFILEs provided by STScI have their PEDIGREE set to DUMMY, thus forcing the stage to be skipped. The ILLMFILEs were introduced to remove a two-dimensional residual background that may exist due to spatial variations in the thermal emission of the telescope and instrument. However, in practice, it does not appear that there are strong spatial illumination variations requiring such corrections, and therefore the ILLMFILEs used in the processing pipeline are dummies. These files must
be present, however, in order for calnicb
processing to proceed. The step may also be turned off entirely by setting illmcorr to omit in the association table (by default, it is set to perform, even though dummy files are used). In order to disable illmcorr, the keyword must be changed in the association table header itself, not
in the primary image header of the association table FITS file. This must be done with the tables.parkey
task, not with hedit
. See the highlighted note at the end of Section 3.5.2
for how to do this. Ordinarily, it is simplest to just leave illmcorr=perform and use the dummy reference files.
If there is more than one image at any pattern position (NUMITER
> 1), the multiple images at each position are first registered and then combined into a single image. The coordinates (as determined by the WCS keywords) of the first image at a given pattern position are used as a reference for the registration. The offsets to all other images at that pattern position are first computed by comparing their WCS data, and then refined using a cross-correlation
down to a level of 0.15 pixels. The cross-correlation technique employes an algorithm which minimizes the differences between fluxes in the images. The computed offsets, in units of pixels, are recorded in the output association table. After determining the relative offsets, the images are aligned using bilinear interpolation
and are then combined on a pixel-by-pixel basis. The combined pixel values are computed as a weighted mean of all unflagged (i.e., DQ = 0) samples, using the input image ERR values as weights. If three or more samples are present, iterative σ
-clipping is performed to reject outliers. The number of samples used at each pixel and the total integration time are retained.
The source identification step is used for excluding sources when the
background in the images at each pattern position is estimated. The images at each pattern position are searched for pixels suspected to contain signal from a source. The median signal level in the image is computed and pixels that are more than 4.5σ
above the median are considered as candidates. Spurious results, such as pixels containing cosmic-ray hits, are filtered out by searching neighboring pixels and only retaining those candidates that have two or more neighbors that are also above the threshold. The DQ flag of the source-affected pixels is then set to 1024.
A constant background
signal level, which is estimated from the images themselves is removed from the images at each pattern position as follows.
|With dither-only patterns, or with multiple-exposure single pointings,
the median and average deviation of each target image is computed. The result for each image is compared to the background estimate provided by calnica
, which is (in principle) stored in the backest1 header keyword of each image. The value computed by calnicb
is accepted if it is less than 5σ
deviant from that of calnica
, otherwise the calnicb
value is assumed to be biased by the presence of sources and the calnica
value is substituted for it. Note, however, that normally the value of backest1 would be populated by the BACKCALC step of calnica
, which has not been implemented (see Section 3.3.2
). Therefore ordinarily backest1 has value 0.0, and in this case the background value computed by calnicb
is always used, unless the user has manually set backest1 to some non-zero value.
|With dither-only patterns, the user has the option of subtracting the
individual background values computed for each image from themselves rather than computing and subtracting a global mean background value. This option is controlled by the meanbkg
task parameter for calnicb
. The default value, “yes”, indicates that the global mean is to be used.
Mosaic (MOS) images are created for each independent pointing within
the pattern. For example, a combination DITHER-CHOP pattern will produce one mosaic image out of the dithered pattern at each CHOP location on the sky. Each mosaic image is created as follows:
|Pixel values in the mosaic image are populated by combining sam
ples from overlapping images. The individual images are aligned using bilinear interpolation and the value at a given mosaic pixel location is computed from the error-weighted mean of the samples at that location. Samples flagged as bad are excluded and, if three or more samples are present, iterative sigma-clipping is used to reject remaining outliers. The number of samples retained for a given pixel and their total integration time is recorded in the SAMP and TIME images, respectively. If all samples are rejected for a pixel, the mosaic image SCI, ERR, SAMP, and TIME values are set to zero and a combination of all DQ flags is retained.