CASPIR produces images of the infrared sky in one passband at a time. These observations normally consist of a number of object and sky frames acquired through the execution of a DO file. Typical observing sequences would:
1) Record a few frames of one object with small spatial offsets between frames to counter ghosts and bad pixels, and to improve spatial sampling of the images.
2) Record many frames of the same object with a dither pattern of offsets to build up long exposures.
3) Record spatial mosaics of dithered sets of images with limited overlap between frames to cover large regions of sky.
These observing sequences naturally lead to the definition of a dataset as the set of related observations of a given object in one filter. In the extreme case, a dataset may contain only a single exposure. Different datasets may require different reduction strategies, depending on the nature of the observing sequence employed. The reduction of most datasets will follow the path:
1) Create BIAS and DARK frames, and linearize object and sky frames.
2) Create dome FLAT frames, and remove pixel-to-pixel sensitivity variations.
3) Create background SKY frames, and subtract sky background from object frames.
4) Define relative spatial offsets between each object frame in the dataset.
5) Combine all object frames in a dataset into a single image suitable for analysis, using bad pixel masks to exclude bad pixels.
Users are cautioned that infrared imaging 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, read noise, and pixel-to-pixel sensitivity variations) and especially due to the extreme background-limited nature of most infrared imaging observations. The results at each step in the reduction process should be carefully examined and problems understood before proceeding. Many problems can be solved by the exclusion of bad images from the data sets.
The reduction procedures described here use the local MSSSO CASPIR package running in IRAF. The procedures (and this description) are based heavily on the SQIID package and its documentation (written by Mike Merrill at NOAO), but have been adapted at MSSSO for CASPIR reductions. The CASPIR package is available via ftp to merlin.anu.edu.au. You can retrieve it by typing:
ftp merlin.anu.edu.au log in as `anonymous' use your email address as password cd pub/peter/ get caspir.tar.gz bye
Then put the following lines in your loginuser.cl file.
set caspirdir = ``home$scripts/caspir/'' set caspirdb = ``home$scripts/caspir/database/'' task $caspir = "caspirdir$caspir.cl" caspir
These define the IRAF variables caspirdir and caspirdb to point to your CASPIR package directory and a convenient database directory, respectively, then define the CASPIR package and load it automatically on starting IRAF. You also need to include the line
unlimit descriptors
in your .cshrc file. This lets you handle a larger number of files in forming mosaics.