MOPEX User's Guide - IRSA

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The modules required for Overlap are: • Fiducial Image Frame (unless the file FIF.tbl already exists; §4.3.4) • Mosaic Interpolate (§4.3.7) • Compute Overlap Correction (§4.3.8) MOPEX User’s Guide If there are bright objects in the field that could affect the estimate of the background level, MedFilter (§4.3.5) and Detect (§4.3.6) should be added and the Mask Bright Objects option in the Mosaic Interpolate module should be checked. Together, these will detect and ignore bright objects when calculating background levels. Make sure to check the "Apply Overlap Correction" box in the Overlap Correction module to apply the correction to your input images. Overlap-corrected images can be used as the input to the Mosaic pipeline. 2.5.2 Mosaic (mosaic.pl) The Mosaic pipeline (Chapter 5) 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 (that produced the BCDs that you download from the archive), further rejection is recommended. The basic mosaicking modules that you need, not including outlier rejection, are: • Fiducial Image Frame (§5.6.4) • Mosaic Interpolate (§5.6.8) • Mosaic CoAdder (§5.6.19) • Mosaic Combine (§5.6.20) 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): • Mosaic Coverage (§5.6.11) • Mosaic RMask (§5.6.16) • Mosaic Reinterpolate (§5.6.17) Overview of MOPEX 14 Which Modules Should I Choose?
MOPEX User’s Guide Choosing the right kind of outlier rejection (§8.2) also requires some care. Rejection schemes utilize either temporal information (rejecting sources that do not 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), multiframe temporal outlier re jection (§8.2.2) can be a good choice. To add multiframe temporal outlier rejection, use the Mosaic Outlier module (§5.6.14). When using multiframe 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 box outlier rejection (§8.2.4) (include module Mosaic Box Outlier, §5.6.15) or dual spatialtemporal outlier rejection (§8.2.3) are preferred. Dual outlier rejection requires the following modules: • MedFilter (§5.6.6) • Detect (§5.6.9) • Mosaic Projection (§5.6.10) • Mosaic Dual Outlier (§5.6.12) • Level (§5.6.13) Multiple outlier rejection methods can be employed, and all can be set to contribute to the RMask by setting the RMask Fatal Bit Pattern (see §8.11: Fatal Mask Bit Patterns). The Mosaic RMask module (§5.6.16) can also use the dual outlier rejection results as a check on the temporal outlier identifications (the Refine Outlier option). 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 §5.5 “Mosaic Pipeline Stages.” 2.5.3 APEX Single Frame (apex_1frame.pl) Source extraction can be performed on the mosaic image using APEX Single Frame mode (Chapter 6). This takes a single image (together with an optional coverage map and optional uncertainty file) as input. It can be used independently or as a follow-on to the Mosaic pipeline. A typical APEX Single Frame task might include background subtraction, noise estimation, nonlinear filtering, point source detection, point source fitting and flux estimation, and aperture photometry. These are carried out by the following modules: • Detect MedFilter (§6.5.8) • Point Source Probability (§6.5.9) Overview of MOPEX 15 Which Modules Should I Choose?
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MOPEX User’s Guide Point Source D
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Figure 6.1: Overview of the main pr
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Relevant Pipelines: All PURPOSE INP
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NICE = 1 save_namelist = 1 MOPEX Us
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