Marx 5.0.0


Caveats for MARX 5.0


Use of the EDSER Subpixel Algorithm with SAOTrace/Chart Rays

One of the new features in marx 5 is the ability to use the EDSER subpixel algorithm to randomize chip coordinates. This algorithm works by shifting the event position from the center of the pixel by an amount (dx,dy) that depends upon energy and flight grade. However, the values of (dx,dy) in the Chandra CALDB form a small discrete set, not a continuous one. As a result, in the absence of dither the EDSER algorithm will produce a PSF that consists of a number of sharp peaks. It is the dither motion of the telescope that smooths this peakiness out, and as such is an integral part of the EDSER algorithm.

As of January 2012 no publicly released versions of SAOTrace generate dithered rays. However, marx 5.0 has been tested with rays produced by development versions of SAOTrace that do incorporate dither. Until such rays and the corresponding aspect solution files are available, non-dithered SAOTrace rays will require marx2fits to be used with the --pixadj=RANDOMIZE option to avoid the peakiness outlined above.

Spatial Dependence of the Quantum Efficiency

The current version of marx does not incorporate the QE uniformity maps and bad-pixel files that give rise to spatial variation in the QE. Consequently, exposure maps and ARFs created by default in CIAO will be inconsistent with MARX simulations. For simulated observations on ACIS-S3, this difference should be small since spatial variations in the QE are relatively small on this CCD. However, simulated ACIS-I observations will be effected to a greater degree due to the larger QE variations produced by CTI effects.

For users of CIAO 2.3 and higher, the tools mkarf and mkinstmap include the ability to turn off the QE uniformity maps through the use of ardlib qualifiers. For example, a calling sequence like:

unix%  mkarf detsubsys="ACIS-7;uniform;bpmask=0" ......
will produce an ARF on ACIS-7 (ACIS-S3) but with bad pixel processing disabled (bpmask=0) and without the effects of the CALDB non-uniformity files included. The resulting ARF will be consistent with a marx simulation. A similar call to mkinstmap can be used in conjunction with mkexpmap to create exposure maps appropriate for marx simulations. This technique is illustrated on the examples page. Users are refered to the CIAO documentation for more information on mkarf and mkinstmap options.

ACIS Response Functions

CIAO includes a couple of different tools for creating ACIS response matrices (RMFs): mkacisrmf and mkrmf. The mkacisrmf tool is designed for the analysis of CTI corrected data, whereas mkrmf creates an RMF for non-CTI corrected data. The response algorithm implemented in marx is based upon the calibration data used by mkrmf. Hence the PHAs generated by marx for the ACIS detector represent non-CTI corrected values and as such are consistent with the responses generated by mkrmf but not with mkacisrmf. Consequently, users should continue to use the CIAO's mkrmf to create RMFs that are consistent with their marx simulations. More information about using mkrmf in the context of a marx simulation may be found on the examples page.

Alternatively, marxrsp may be used to apply any RMF to a marx simulation with the caveat that the mapping from photon energy to PHA does not vary over the detector.

This page was last updated Jan 24, 2012 by John E. Davis.
Technical questions should be addressed to marx-help at space mit edu.
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