ACIS Data Preparation
Analysis Guide
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.
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 chandra_repro reprocessing script runs all the data processing threads for ACIS imaging data except for the absolute astrometirc corrections. Everything else up to the filtering data steps of this guide are automated by the script. Refer to "ahelp chandra_repro" for more information.
The following threads are referenced:
- Correcting Absolute Astrometry with reproject_aspect
- Reprocessing Data to Create a New Level=2 Event File
- Setting the Observation-specific Bad Pixel Files
- Filtering Data
- Filtering Lightcurves
- Removing Warm ACIS Data
The threads should be run in the order in which they are presented below.
Thread: Correcting Absolute Astrometry with reproject_aspect
If your science requires combining multiple observations or if you need the best possible source positions, then you may need to apply astrometric offsets to individual observations to get the correct registration. The Correcting Absolute Astrometry with reproject_aspect shows how to determine this offset. Users should use the archived Level 2 event file and apply the correction to the aspect solution file(s).
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 |
| Filtering Light Curves | |
| 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.
| Threads: | Removing Warm ACIS Data |
| Why Topic: | ACIS Focal Plane Temperature |
The ACIS focal plane temperature (FPTEMP) may vary during an observation. Users who have many counts and need to fit line rich spectra may want to consider removing times when the FPTEMP is above the nominal set point temperature.