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Last modified: 1 July 2010

URL: http://cxc-newtest.cfa.harvard.edu/ciao4.2/guides/acis_data.html

Analysis Guide: ACIS Data Preparation


It is possible to analyze any Chandra dataset straight out of the box. However, to get scientifically accurate results, there are a number of data processing questions that should be considered. When Chandra data goes through Standard Data Processing (SDP, or the pipeline), the most recently available calibration is applied to it. Since this calibration is continuously being improved, one should check whether there are currently newer files available. Similarly, some science decisions are made during SDP; every user has the option to reprocess the data with different parameters.

[New] The chandra_repro reprocessing script runs all the data processing threads for ACIS imaging data. Everything up to the filtering data steps of this guide are automated by the script. To try the reprocessing script, install the 01 July 2010 contrib tarfile. Refer to "ahelp chandra_repro" for more information.

This guide is designed to help the user decide how an ACIS dataset should be processed and filtered before starting the data analysis stage.

The following threads are referenced:

The threads should be run in the order in which they are presented below.



Thread: Remove the acis_detect_afterglow Correction

An afterglow is the residual change from the interaction of a cosmic ray in a CCD. Some of the excess charge is captured by charge traps and released in a few to a few dozen subsequent frames. If afterglow events are not removed from the data, they can result in the spurious "detection" of faint sources.

Prior to version DS 7.4.0, standard data processing (SDP, aka "the pipeline") used the tool acis_detect_afterglow to flag possible cosmic ray events in the level 1 event file; these are then filtered out in the level 2 event file. It was determined that 3-5 % of the valid source photons may be rejected from diffracted spectra. These rejections, though a small fraction of the total events, are systematic and non-uniform.

A new, more precise method for identifying afterglow events was introduced to SDP at version DS 7.4.0, namely the ACIS hot pixel tools. The afterglow status bits in data that were processed with acis_detect_afterglow must be reset so that they may be properly recomputed.



Thread: New ACIS Bad Pixel File: Identify ACIS Hot Pixels and Cosmic Ray Afterglows

acis_run_hotpix is a script that runs the ACIS hot pixel tools in order; see the About the ACIS Hot Pixel Tools section of the thread for details.

These tools supersede acis_detect_afterglow and replaced that tool in standard data processing in DS 7.4.0. acis_run_hotpix is better because it finds some afterglows that acis_detect_afterglow does not, it also finds pixels that are hot or have bad bias values and it misidentifies far fewer legitimate X-ray events as bad.

It is necessary to reprocess the data with acis_process_events in order to apply the new bad pixel file. This is included in the section: "Reprocessing Data to Create a New Level=2 Event File".



Thread: Reprocessing Data to Create a New Level=2 Event File

The Reprocessing Data to Create a New Level=2 Event File thread generates a new level=2 event file for all possible grating and detector combinations.

This thread also includes grade and status filtering:

If you have been working with a level=1 event file, it needs to be filtered on grade and status to create a level=2 event file. In general, the data is filtered to remove events that do not have a good GRADE or that have one or more of the STATUS bits set to 1.



Thread: Setting the Observation-specific Bad Pixel Files

Although the majority of the calibration files are now contained within the Chandra Calibration Database (CALDB), the observation-specific bad pixel list must still be set by the user. This file will be used by many of the CIAO tools, such as mkarf, mkgarf, and mkinstmap. Setting the bad pixel file ensures that the most accurately known bad pixel list for any observation will consistently be used in the data processing.

It is very important that you know what files are set in your ardlib.par. If you do not set the bad pixel file for your observation, the software will use a generic detector bad pixel file from the CALDB; pixels that are flagged as bad in a specific observation will not get filtered out when using this map. The criteria for a pixel to be flagged are described in the badpix dictionary entry.

Remember to "punlearn" or delete your ardlib.par file after completing analysis of this dataset to ensure that the proper bad-pixel maps are used the next time that ardlib.par is referenced by a tool.



Threads: Filtering Data (S-Lang or Python)

Filtering Light Curves (S-Lang or Python)
Why Topic: Choosing an Energy Filter

The filtering applied to the event file should take into account the analyis goal, whether it is source detection or modeling of pileup. One may also want to filter the data to exclude the events with low or high energies. At the extrema of the spectrum (where the effective area is the smallest), the events may be dominated by background; to maximize the signal to noise ratio, exclude these energy ranges.


Last modified: 1 July 2010