About the Chandra Grating Data Archive and Catalog

The Chandra Grating Data Archive and Catalog (TGCat) is a browsable interface to analysis-quality spectral products (binned spectra and corresponding response files). TGCat makes it easy to find observations of a particular object, type of object, or type of observation, to quickly assess the quality and potential usefulness of the spectra with pre-computed graphics or custom-generated plots of binned and combined counts or fluxe spectra. Spectra, responses, event files, and summary products may be downloaded as a package.

TGCat runs standard CIAO tools, but also includes customized extractions for non-standard cases to refine the zeroth order position or to use regions appropriate for extended sources. Non-standard extractions details are provided in "verification and validation" comments for users.

Most public grating observations are available and new ones are added soon after they are released. See the list of of observations not included for exceptions. Many of the observations currently in this list will be included when we add enhanced processing for more difficult cases (multiple sources, very extended sources).

Please consider using the spectrum and responses (PHA, ARF, and RMF files) from TGCat in your analysis.

Get Started

Sample ObsID used: 1801 (LETG/HRC-I, PKS2155-304)

File types needed: evt2; pha2; asol1; bpix1; dtf1

If you created a new bad pixel file by running the New Observation-Specific HRC Bad Pixel File thread, make sure that you have set up ardlib to use the same bad pixel file.

Determine Orders

An ARF needs to be calculated for each order in the observation. We can use Prism to examine the pha2 file and determine how many orders there are:

unix% prism hrcf01801N005_pha2.fits &

The tg_m column indicates the order of the observation (+/- 1, +/- 2, +/- 3) and the tg_part column indicates the grating (1 = HEG, 2 = MEG, 3 = LEG). Figure 1 shows the two rows for the LETG observation. HRC-I cannot resolve orders; +/- 1 refer to the total positive- and negative-side counts respectively.

Figure 1: HRC-I/LETG dataset in prism

[Version: full-size]

Figure 1: HRC-I/LETG dataset in prism

Compute the Aspect Histogram (asphist)

The aspect solution files are used to create a binned histogram detailing the aspect history of the observation:

unix% punlearn asphist
unix% pset asphist infile=@pcad_asol1.lis
unix% pset asphist outfile=asphist.fits
unix% pset asphist evtfile="hrcf01801N005_evt2.fits[chip_id=0]"
unix% pset asphist dtffile=hrcf01801_000N005_dtf1.fits
unix% asphist
Aspect Solution List Files (@pcad_asol1.lis): 
Aspect Histogram Output File (asphist.fits): 
Event List Files (hrcf01801N005_evt2.fits[chip_id=0]): 
Live Time Correction List Files for HRC (hrcf01801_000N005_dtf1.fits): 

# asphist (CIAO 4.2): WARNING: skipping 8 livetime correction records
  (from time: 82336842.920992 to time: 82337011.020998)

In many cases, there will be more than one aspect solution file (pcad_asol1.fits) for an observation. All the files must be input to the infile parameter, either as a list or a stack. Here we use:

unix% cat pcad_asol1.lis 
pcadf082337011N003_asol1.fits

In many cases there will be more than one asol1.fits file for an observation. All the files must be input to the infile parameter, either as a list or as a stack (see ahelp stack for more information). Here we use:

The content of the parameter file may be checked using plist asphist.

Get Source Position (dmlist)

The source position is required as one of the inputs to mkgarf. This information can be easily obtained from the PHA2 file with dmlist:

unix% dmlist "hrcf01801N005_pha2.fits[SPECTRUM][cols x,y]" opt=data rows=1
 
 
--------------------------------------------------------------------------------
Data for Table Block SPECTRUM
--------------------------------------------------------------------------------
 
ROW    X            Y           
 
     1       16500.17968750     16366.7568359375

The source in this example is located at (16500.17968750, 16366.7568359375).

Run mkgarf

Now we have all the information needed to run mkgarf. We will have to run the tool twice, once for each row in the PHA file:

unix% punlearn mkgarf
unix% pset mkgarf outfile=1801_-1_LEG_garf.fits
unix% pset mkgarf order=-1
unix% pset mkgarf asphistfile="asphist.fits[ASPHIST]"
unix% pset mkgarf obsfile="hrcf01801N005_evt2.fits[EVENTS]"
unix% pset mkgarf engrid="grid(rmf.fits[cols ENERG_LO,ENERG_HI])"
unix% pset mkgarf detsubsys=HRC-I grating_arm=LEG 
unix% pset mkgarf sourcepixelx=16500.17968750 sourcepixely=16366.7568359375
unix% mkgarf
Aspect Histogram File (include extension) (asphist.fits[ASPHIST]): 
Output File Name (1801_-1_LEG_garf.fits): 
Enter Grating order (-1): 
Source X Pixel (16500.17968750):
Source Y Pixel (16366.7568359375):
Energy grid spec (grid(rmf.fits[cols ENERG_LO,ENERG_HI])): 
Name of fits file with obs info (include extension) (hrcf01801N005_evt2.fits[EVENTS]): 
NONE or Name of fits file with order sorting info (NONE): 
Detector Name (HRC-I): 
Enter Grating Arm (HEG|MEG|LEG) (LEG): 
NONE, or name of ACIS window mask file (NONE): 

unix% pset mkgarf order=1
unix% pset mkgarf outfile=1801_1_LEG_garf.fits
unix% mkgarf
Aspect Histogram File (include extension) (asphist.fits[ASPHIST]): 
Output File Name (1801_1_LEG_garf.fits): 
Enter Grating order (1): 
Source X Pixel (16500.17968750):
Source Y Pixel (16366.7568359375):
Energy grid spec (grid(rmf.fits[cols ENERG_LO,ENERG_HI])): 
Name of fits file with obs info (include extension) (hrcf01801N005_evt2.fits[EVENTS]): 
NONE or Name of fits file with order sorting info (NONE): 
Detector Name (HRC-I): 
Enter Grating Arm (HEG|MEG|LEG) (LEG): 
NONE, or name of ACIS window mask file (NONE): 

The content of the parameter file may be checked using plist mkgarf.

Fitting

At this point, you should have the spectra, gARFs, and gRMFs necessary for fitting the data. The Fitting Grating Data thread shows how to load the data and responses, define a model, and fit the spectra.

In order to use Gaussian statistics to fit a model to a dataset, it is often necessary to "group" the data - i.e. combine channels until you have enough counts. Before fitting the data in Sherpa, read the Grouping a Grating Spectrum thread for more information.