MOPEX Online Manual
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Which Modules Should I Choose?
As described on the previous pages, once you have identified the perl script(s) needed to process your data, you need to tailor it (them) to carry out the specific data reduction method required for your data. This tailoring is done in two ways - firstly, by choosing which of the available modules to run within each script, and secondly, by fine-tuning those modules by setting the various input parameters.
The number of choices of modules often seems daunting when using MOPEX for the first time. Here we give a short introduction to which modules are needed for the most commonly-used scripts. We strongly recommend that first-time users start by experimenting with the template namelists found in the directory <mopex_dir>/cdf/ or from the main MOPEX webpage, rather than trying to build up the flow in an empty namelist.
Note that the advice given here is very general and may not apply to every aspect of an individual dataset. We do not discuss every option in MOPEX on this page, just some representative choices. It is important to try the software on your own data, and to evaluate its performance at each stage. Detailed explanations of each module can be found later in this manual, or via the quick links from the module names below.
Background Matching (overlap.pl)
The Overlap script performs background matching on the input images. It does not set the background level to zero, rather it adds (or subtracts) a constant to bring it to an average level. This process is recommended for Spitzer data, as there can be variation in the sky level which will lead to a patchy-looking final mosaic if not corrected. The process may not be necessary if the BCDs downloaded from the Spitzer archive have been background-subtracted by the user (with, for example, a median sky) before using MOPEX.
The modules required for Overlap are:
Fiducial Image Frame (unless the file FIF.tbl already exists)
If there are bright objects in the field that could affect the estimate of the background level, MedFilter and Detect (Outlier) should be added. Together, these modules will detect and ignore bright objects when calculating the median background level.
Make sure to set the "apply_overlap_correction" option in the Overlap namelist to apply the correction to your input images. Overlap-corrected images can be used as the input to the mosaicker.
Mosaic (mosaic.pl)
The Mosaic flow has the largest number of modules to choose from, due to the many outlier detection options. While some outliers are flagged during the Spitzer pipeline processing (run by the SSC to produce the BCDs that you download from the archive), further rejection is recommended.
The basic mosaicking modules that you need, not including outlier rejection, are:
If any outlier rejection is employed, MOPEX will create a new status mask, the RMask, and use it to reinterpolate the rejected pixels. To use the RMask capability, you will need to add at least the following modules, plus the modules specific to the chosen outlier rejection scheme (see following discussion):Choosing the right kind of outlier rejection also requires some care. Rejection schemes utilize either temporal information (rejecting sources that don't appear at the same coordinates in every frame) or spatial information (rejecting outliers based on their shape or size), or both. In the case of good coverage per pixel (about 10 or more BCDs per pixel on the sky), temporal outlier rejection can be a good choice. To add temporal outlier rejection, use the Mosaic Outlier module. When using temporal outlier rejection, be careful when setting the rejection thresholds. A three sigma rejection is often too low if the coverage is very high (50 or more). If only shallow coverage is available, then either the dual spatial-temporal outlier rejection or box outlier rejection are preferred. They require the following modules:
Multiple outlier rejection methods can be employed, and all can be set to contribute to the RMask by setting the RMask Fatal Bit Pattern. The RMask module can also use the dual outlier rejection results as a check on the temporal outlier identifications (set the "refine_outlier" flag in the Mosaic namelist). This combination can be useful to prevent the false identification of temporal outliers inside bright sources.
The Mosaic pipeline can produce a number of final outputs. The most basic output files that you will need are the mosaic and its coverage map and uncertainty file (mosaic.fits, mosaic_cov.fits, and mosaic_unc.fits). For more options, see the Mosaic Pipeline pages.
APEX one frame (apex_1frame.pl)
Source extraction can be performed on the mosaic image using APEX single frame mode. This takes a single image (together with a coverage map and optional unertainty file) as input. It can be used independently or as a follow-on to the Mosaic pipeline.
APEX peforms background subtraction (Detect MedFilter and Extract MedFilter), noise estimation (Gaussnoise), non-linear filtering (Point Source Probability), point source detection (Detect), point source estimation (Source Estimate) and aperture photometry (Aperture). If a corresponding uncertainty mosaic is available, Fit Radius should be added to improve the fitting. Unlike the Mosaic pipeline, there are few choices; all of the modules are required.
The output of APEX is the table of extracted sources (extract.tbl), which gives the position and flux for each object.
APEX multiframe (apex.pl)
Source extraction can also be performed in multiframe mode. In this case, sources are detected in the mosaic but PRF-fitting is performed simultaneously on the individual BCDs (after geometric distortion correction); aperture photometry is still performed on the mosaic.The major advantage over apex_1frame is seen in data with intra-pixel variability (e.g. IRAC data). APEX multiframe uses all of the same modules as the single frame mode, but also requires:
If the mosaic pipeline has been run first, FIF, Mosaic CoAdder and Mosaic Combiner can be dropped.
Residual Image Creation (apex_qa.pl)
The MOPEX command-line script apex_qa.pl takes in the list of detected point sources from APEX and subtracts them from the original input image(s) to create the residual image. This is invaluable for testing how well the PRF-fitting is performing. The input and output files depend on whether the script is being run on a stack of images (when using it with apex.pl) or on a single mosaic image (when working with apex_1frame.pl). The APEX introduction page lists the input requirements.
There is only one module available in apex_qa.pl, called Point Source Subtract, but if the trigger mosaic_residual_images is set in the namelist (Multiframe mode only), you must also turn on run_mosaic_interp, run_mosaic_coaader and run_mosaic_combiner and include empty parameter blocks for Mosaic Interpolate and Mosaic Coadd.
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