MOPEX Online Manual

MOPEX Overview

MOPEX was developed at the Spitzer Science Center to process Spitzer Basic Calibrated Data (BCD) from the point at which the users download the files from the archive through to final point source extraction. While it is primarily designed to take Spitzer data as input, it is a general purpose tool that can be applied to any images in standard FITS format (see MOPEX Input Images for input image requirements).

How MOPEX Works

MOPEX is a set of Perl wrapper scripts that call C++ and C modules. For the GUI, the Perl scripts were translated into Java. All of the Perl scripts can be examined by the user and are located in the MOPEX installation directory, under mopex/bin/.

Each of the scripts runs a series of individual "modules" in sequence, and each module carries out a specific part of the data reduction. Depending on the options you wish to use when processing your data, some of these modules need to be turned on or off when running the scripts. For example, some users will wish to use outlier rejection to remove the cosmic rays from their final mosaic, in which case the modules that carry out the outlier rejection must be switched on when running the Mosaicking script.

In the command-line version of MOPEX, the perl scripts are run on the command line, and use input parameter files called "namelists" (*.nl) to control the modules that are to be run, the input and output file names and directories, and all of the input parameter values. In the GUI, parameters are set by entering values into each module window, and then modules may be run one at a time or as an entire pipeline.

MOPEX comes with example template namelists in the subdirectory cdf/ (also available from the main MOPEX webpage), and a sample data set is available from the MOPEX download page.

Basic Processing Steps in MOPEX

Here we outline the steps in a typical MOPEX task: combining a stack of FITS images into a mosaic and doing point-source photometry.

1. Data Input: MOPEX will read in the data frames, the associated uncertainty frames, and the associated bad pixel masks from user-created lists in ASCII format.
2. Fiducial Image Frame (FIF) Generation: a unified grid coordinate system is generated, which defines a common grid onto which the input images will be projected. The spatial boundaries of the FIF include all of the input images.
3. Background Matching: The Overlap script (overlap.pl) performs background matching between overlapping input frames. An additive correction is calculated for each images in the input stack in order to bring them to a common background level (not a zero-background level - see Median Filtering below), so avoiding "patchy" mosaics. This process does not affect the photometry of the detected sources.
4. Image Interpolation: The image interpolation module projects the input images onto the FIF and interpolates the input pixel values to the output array. Users can define the pixel size of the output array. This step corrects for the optical distortion in the input images.
5. Outlier Detection and RMask Generation: A primary feature of MOPEX is outlier rejection. Four methods are available to use both spatial and temporal information to identify and mask outliers (e.g. cosmic rays). The combined outlier information can be stored in a single outlier mask -- the RMask -- which is then used by MOPEX to mask outliers when creating the mosaic.
6. Mosaic Creation: The interpolated images are averaged to produce a co-added mosaic image. There are several weighting schemes available.
7. Median Filtering: Mosaic images can be median-filtered to produce a background-subtracted image.
8. Point Source Extraction: The APEX script performs multi-frame (APEX) and single-frame (APEX One Frame) point source extraction. These scripts include non-linear matched filtering for point-source detection, image segmentation for detecting blends, point source fitting for position and flux estimation, and aperture photometry. The point sources are fit with a Point Response Function (PRF).

A reduction such as this will result in the following output files: a single mosaicked image of all the input images, a coverage mosaic showing how many frames were combined to produce each pixel in the output mosaic, an uncertainty mosaic showing the uncertainties at each point in the mosaic, and a table of extracted flux densities for every point source detected in the field of view. These are just the most commonly used output products - the user has the option to request the generation of many more.

Additional Functionality

While the reduction process described above includes the most commonly-used processing stages, there are many more functions available in MOPEX to carry out a more specialised reduction. See both Introducing the Major Scripts in MOPEX and Appendix 1: Full List of MOPEX Scripts for more details.

Expected Input

MOPEX only requires a list of FITS images for basic mosaicking, but the additional files listed below will generally prove helpful. For Spitzer data, all of the masks and calibration files can be downloaded with your data using Leopard. The permanently-damaged pixel masks and standard PRFs are available in the mopex/cal directory that comes with your MOPEX installation. Template namelists can be found in the mopex/cdf directory.

• A set of FITS data images
• Associated status masks (DCE Status Masks), flagging bad pixels in each individual data frame
• Associated uncertainty images
• Relevent masks of permanent detector artifacts (PMasks)
• A user-generated "namelist" file specifying the input parameters to be used during the mosaicking process