Last modified: 24 October 2019

URL: https://cxc.cfa.harvard.edu/csc/data_products/persrc/obs/pha3.html

Per-Observation Detections PHA Files (pha3.fits)

The PHA spectrum file consists of a single FITS format file for each source region of each observation that records the low resolution PI spectrum of the events extracted from the source region, resulting from catalog processing. The PI spectrum is constructed using weighted responses for the areas of the detector over which the source region dithers. A low resolution PI spectrum of the events extracted from the background region is recorded in the FITS HDU (CXC Data Model block) immediately following the source PHA spectrum HDUs.

FITS HDUs (CXC Data Model blocks):

SPECTRUM1 source and background spectrum SPECTRUM2 background spectrum MASK pixel mask used as part of source spectrum extraction MASKn possible duplicate MASK blocks produced during processing GTIn good time interval for a given chip ID
[CAUTION]
Caution

The CSC PHA spectrum files (pha3) can be used from Sherpa, which will automatically recognise the source and background components when the file is read in. If you are using the XSPEC X-ray spectral fitting package then the following limitations may apply (depending on the version of XSPEC in use):

  • The file must be uncompressed otherwise the background data will not be read in.
  • With older versions of XSPEC the file must be split into separate source and background files, e.g. with the dmcopy tool:
    ciao% dmcopy pha3.fits"[SPECTRUM1]" src_spec.fits
    ciao% dmcopy pha3.fits"[SPECTRUM2]" bkg_spec.fits
    

Here is the structure of an example spectrum from CSC 2.0:

unix% dmlist acisf04994_000N021_r0002_pha3.fits.gz blocks
 
--------------------------------------------------------------------------------
Dataset: acisf04994_000N021_r0002_pha3.fits.gz
--------------------------------------------------------------------------------
 
     Block Name                          Type         Dimensions
--------------------------------------------------------------------------------
Block    1: PRIMARY                        Null        
Block    2: SPECTRUM1                      Table         4 cols x 1024     rows
Block    3: GTI3                           Table         2 cols x 2        rows
Block    4: GTI2                           Table         2 cols x 4        rows
Block    5: GTI1                           Table         2 cols x 3        rows
Block    6: GTI0                           Table         2 cols x 4        rows
Block    7: GTI6                           Table         2 cols x 4        rows
Block    8: MASK                           Image      Byte(267x375)
Block    9: SPECTRUM2                      Table         4 cols x 1024     rows
Block   10: MASK2                          Image      Byte(267x375)

and the columns in the SPECTRUM1 block (this also holds for the SPECTRUM2 block):

unix% dmlist "acisf04994_000N021_r0002_pha3.fits.gz[spectrum1]" cols
 
--------------------------------------------------------------------------------
Columns for Table Block SPECTRUM1
--------------------------------------------------------------------------------
 
ColNo  Name                 Unit        Type             Range
   1   CHANNEL              channel      Int4           1:1024               PI
   2   PI                   chan         Real8          1.0:     1024.0      pulse invariant energy of event
   3   COUNTS               count        Int4           -                    Counts
   4   COUNT_RATE           count/s      Real8          0:+Inf               Rate

The following set of Sherpa commands load the data, apply a grouping of 20 counts per group over the energy range 0.5 to 7 keV, and then plot the background-subtracted data (see the figure below).

unix% sherpa
-----------------------------------------------------
Welcome to Sherpa: CXC's Modeling and Fitting Package
-----------------------------------------------------
CIAO 4.11 Sherpa version 1 Wednesday, December 5, 2018

Python 3.5.4 (default, Oct 15 2018, 13:47:46) 
Type 'copyright', 'credits' or 'license' for more information
IPython 6.5.0 -- An enhanced Interactive Python. Type '?' for help.

IPython profile: sherpa

sherpa In [1]: %matplotlib
Using matplotlib backend: TkAgg

sherpa In [2]: set_xlog()

sherpa In [3]: set_ylog()

sherpa In [4]: load_pha('acisf04994_000N021_r0002_pha3.fits.gz')
read ARF file acisf04994_000N021_r0002_arf3.fits
read RMF file acisf04994_000N021_r0002_rmf3.fits
read background file acisf04994_000N021_r0002_pha3.fits

sherpa In [5]: notice(0.5, 7)

sherpa In [6]: tabstops = ~get_data().mask

sherpa In [7]: group_counts(1, num=20, tabStops=tabstops)

sherpa In [8]: subtract()

sherpa In [9]: plot_data()

Source Region PI

[example of source region PI]
[Print media version: example of source region PI]

Source Region PI

Example of a source region PI spectrum.

The per-observation PHA FITS file is named: 〈i〉〈s〉〈obs〉_〈obi〉N〈v〉[_〈c〉]_〈r〉_pha3.fits

where 〈i〉 is the instrument designation, 〈s〉 is the data source; 〈obs〉 is the observation identification; 〈obi〉 is the observation interval identification; 〈v〉 is the data product version number; 〈c〉 is the cycle; and 〈r〉 is the region ID, formatted as "rnnnn", where nnnn is the 4-digit region number, formatted with leading zeros. The optional discriminator identified in square brackets is included only for ACIS alternating exposure (interleaved) mode observations.


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