This example, was prepared for the HST
Calibration Workshop (Mutchler, M. 2010, HST
Calibration Workshop), using data from the HST
program 12050 (HH 901), but can be used for any mosaic data sets from the HST
Wide Field Camera 3 infrared channel (WFC3/IR). A complete and well-documented set of drizzled output images, prepared by the author for all UVIS and IR filters, are available as High Level Science Products (HLSP) in the Multimission Archive at STScI (MAST):
table (MDRIZTAB) sets parameters for pipeline drizzling, but the user can often produce more optimal results offline by setting parameters tailored to the characteristics of a speciﬁc data set, as illustrated here. Data quality flags are generated by steps within the WFC3 calibration pipeline (calwf3
), and are stored in the DQ arrays of the flt
images used as input for MultiDrizzle
. All of the flagged pixels would be excluded during drizzling, but by setting bits (summing any flag types to be included), the user can control which pixels are excluded. For example, the user may set bits=4864
to include pipeline-rejected pixels (ﬂag=4096), saturated pixels (ﬂag=256), and dust motes (ﬂag=512) for an IR data set that did not employ a dither pattern large enough to span them.
The pipeline parameters are generic values set to run on every WFC3 image. Thus
the pipeline drz
images should be considered quick-look products. Using the basic WFC3 pipeline calibrations (bias, dark, ﬂat), and the “on-the-ﬂy” (OTFR) pipeline-calibrated archival flt
images from the HST
archive (MAST) as input users can run MultiDrizzle offline. A common reason for reprocessing is to take advantage of subsampled data sets that can be drizzled offline to smaller pixel scales, using a smaller pixel scale and drop fraction (pixfrac), to fully extract all the spatial information contained in them. Similarly, mosaic data sets must be drizzled offline to align and combine images from many different visits (various pointings or different epochs).
The sample commands below are shown at a PyRAF prompt (so IRAF commands
can also be used), but MultiDrizzle
can also be run with python syntax (see the MultiDrizzle Handbook section 5.4). De-archive a set of calibrated flt
images using data from the HST
program 12050 (HH 901): typically using the same instrument modes and filter, with exposure times within 20% of each other. Make an input list in your working directory:
Download the latest distortion reference table, and specify it’s path name in your
image headers (IDCTAB keyword). You may wish to make your own local iref directory and add reference files there:
and the core drizzle task (Fruchter & Hook, 2002) are available in the STSDAS dither package. Load multidrizzle and set parameters as suggested below. Parameters can be saved in a “par” file to record and reuse them later, or a command script can be used to quickly make parameter changes and iteratively reprocess images. The initial run produces the single sci
images needed to measure shifts.
uses the World Coordinate System (WCS) information in each image header to align the images. However, any data set including images taken in different visits or epochs will have small misalignments between visits, due to errors in the cataloged positions of the different guide stars used. Objects in the overlaps between different pointings can be used to measure “delta” shifts and rotations in each image or update the World Coordinate System in each image. These shifts and rotations can then be used to register the images for drizzle combination. Initial shifts can be quickly measured by visually selecting a few ideal objects (e.g. stars which are unsaturated and uncontaminated by cosmic rays) in the undistorted single_sci
images produced by the driz_separate
step. The results must be put into a shiftfile with the following format:
After applying the initial x,y shifts (in the 2nd and 3rd columns above), tweakshifts
or geomap can be run on the single_sci
images to further reﬁne the shifts, and also solve for small rotational or scale offsets (the 3rd and 4th columns in the shift file).
The following are a key subset of MultiDrizzle
parameters optimized for an IR mosaic dataset, with output at both the native detector pixel scale, and an enhanced scale:
Although this observing program did not employ an optimally subsampling dither
pattern, much of the nebula resides in the overlap areas with extra sampling, so the following is an example of drizzling to enhance resolution. The smaller pixel scale is 62% of the detector pixel scale, and happens to be conveniently twice the UVIS scale. Also note the smaller pixfrac “drop size” and alternate kernel:
|pyraf> multidrizzle input=’@list_flt_f128n’
output=’hh901x_wfc3_f128n’ final_scale=0.07920 final_pixfrac=0.8 final_kernel=’gaussian’
To visually verify the quality of your drizzled output, blink your drz_sci
, and drz_sum
images (Figure 4.3
) to look for signs of bad rejections (too much or too little), misregistration, or other signs that some parameters may need to be adjusted. The median image produced by MultiDrizzle is also worth inspecting: it is used to identify cosmic rays and other bad pixels to be rejected, so it should look almost as good as the final drizzled image. The rms of the exposure weight map (drz
weight) should typically be under 30% of the mean, to ensure photometric consistency throughout the image. Several trial and error iterations of are typically required to arrive at optimal results.
Meanwhile, users may also find the examples included in Chapter 6 of the MultiDrizzle Handbook
(Fruchter & Sosey et al. 2009) useful. The examples developed for the ACS CCDs in Section 6.2 may be used as a guide for reprocessing images from the WFC3 UVIS detector. Example 1 describes the steps required to improve the final drizzle sampling, Example 2 describes how to refine the image alignment (especially important for images obtained in separate visits), and Example 3 describes how to fine-tune the cosmic-ray flagging and create an image mosaic. For WFC3-IR images, the NICMOS example in Section 6.3 may be used as a guide for reprocessing.