Last modified: 2 July 2019

URL: https://cxc.cfa.harvard.edu/csc/data_products/index.html

CSC Data Products


In addition to the tabulated properties for each detected source and observation in the Master Sources Table, Per-Observation Detections Table, and Stacked Observation Detections Table a number of file-based Level 3 data products are produced for each master source, detection, stacked observation, and single observation individually, in formats suitable for analysis in CIAO. These include such things as:

  • Source region, background, and PSF images
  • Source region photon event lists
  • Limiting sensitivity maps
  • Source and background light curves
  • Pulse invariant (PI) spectra, Auxiliary Response Files (ARFs), and Redistribution Matrix Files (RMFs)
  • Exposure maps
  • Source lists
  • Bayesian blocks and aperture photometry marginalized probability distributions

These data products are divided in CSCView as follows:

where the "Full-Field" products use the entire field-of-view of an observation; whereas the "Region" products are filtered by a rectangular region of interest around the position of the detection.


Reading the File Contents

While there are many ways to looking in the contents of a FITS file, the native Chandra analysis software, CIAO, uses the dmlist tool. Many L3 data products contain multiple interesting CXC Data Model blocks (FITS HDUs), the block names can be identified by doing:

ciao% dmlist 〈file name〉 blocks

and the column names for a given data block can be identified using the cols option.

ciao% dmlist "〈file name〉[〈block name〉]" cols

omitting the block name in the square brackets will return the column names in the first interesting data block of the file. The values for a column can be listed by specifying a comma-separated string of column names in square brackets and using the data option.

ciao% dmlist "〈file name〉[〈block name〉][cols 〈column name 1〉,〈column name 2〉,...,〈column name X〉]" data

The following ahelp pages provide more information on how the CIAO Data Model can be used to filter, subset, and extract data from these files: ahelp dm; ahelp dmfiltering; ahelp dmregions; and ahelp dmbinning.