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Contents
Subsections
6. An RGS Data Processing and Analysis Primer
Before beginning this chapter please consult the ``watchout'' page at
the VILSPA SOC:
- http://xmm.esac.esa.int/sas/7.0.0/watchout/
This web site discusses current and past SAS bugs and analysis issues.
Many files are associated with an RGS dataset, and it is easy to be overwhelmed.
The INDEX.HTM file, and links therein, are viewable with a web browser and
will help you navigate the dataset. The different types of files are discussed in
more detail in Chapter 3.
As ever, it is strongly recommended that you keep all reprocessed data in its own
directory! SAS places output files in whichever directory it is in when a task is
called. Throughout this primer, it is assumed that the Pipleline Processed data are
in the PPS directory, the ODF data (with upper case file names, and uncompressed) are
in the directory ODF, the reprocessing and analysis is taking place in the PROC directory,
and the CCF data are in the directory CCF.
If you have just received your data from the SOC, it has been processed with the
most recent version of SAS, and you should not need to reprocess it (though no
harm is done if you do). However, it is very likely that you will want to filter
your data; in this case, you will need to reprocess it in order to determine the
appropriate filters. Therefore, we recommend that you rerun the pipeline regardless
of the age of your dataset.
But if you decide that reprocessing is unnecessary, you need only to gunzip the
files and rename event files for easier handling. For example, for the RGS1
event list,
- gunzip ODF/*.gz
gunzip PPS/*.gz
mv PPS/PiiiiiijjkkR1lEVENLInmmm.FTZ PPS/PiiiiiijjkkR1lEVENLInmmm.FIT.gz
gunzip PPS/PiiiiiijjkkR1lEVENLInmmm.FIT.gz PPS/r1_evt.fits
where
- iiiiiijjkk - observation number
l - scheduled (S) or unscheduled (U) obseravtion
n - spectral order number
mmm - source number
As noted in Tables 3.2 and 3.3 you can view images of your data. While the
zipped FITS files may need to be unzipped before display in ds9
(depending on the version of ds9), they can be displayed when zipped
using fv (fv is FITS file viewer available in the HEASoft
package). As usual, there are some HTML products to help you inspect the data.
These have file names of
the form:
- PPiiiiiijjkkAAAAAA000_0.HTM
where
-
- iiiiiijjkk - Observation number
-
- jj - observation ID - target number in proposal
-
- kk - observation ID - observation number for target
-
- AAAAAA - Group ID (see Table 3.2)
You will find a variety of RGS-specific files in XMM-Newton
data sets. Generally there are two of each because there are
two RGS instruments. Table 3.3 lists typical file names,
their purpose, the file format, and a list of tools that will enable
the user to inspect their data. Remember that the INDEX.HTM file
will help you navigate.
6.1 Rerunning the pipeline
You will need to reprocess the data before being able to determine
the appropriate filters, but before doing that, you must prepare
the data and initialize SAS. Throughout this primer, we will use the AB Dor
dataset with ObsID 0134520301 available through links at the HEASARC archive.
- 1)
- If you have not already done so, gunzip all gzipped files in
the ODF and PPS directories. If necessary, rename all files in the ODF
directory to upper case. This can be done using the script provided by
the NASA/GSFC XMM-Newton GOF.
- gunzip ODF/*.gz
gunzip PPS/*.gz
- 2)
- Initialize SAS by calling one of the two setup scripts, depending
on which shell you use.
- source /full/path/to/xmmsas_20080701_1801/setsas.csh
or
- . /full/path/to/xmmsas_20080701_1801/setsas.sh
It is strongly recommended that you add a line to your login shell file
to set up an alias to these scripts! Calling the script will deal with most of the
details needed to run SAS, except for three environment variables, which we will set next.
- 3)
- Set the SAS directory pointers. To verify the SAS-specific settings,
use the command env | grep SAS. (For a detailed discussion of SAS
initialization, see Chapter 4.)
- setenv SAS_ODF full/path/to/ODF
setenv SAS_CCFPATH full/path/to/CCF
At this point, the SAS GUI can be run by typing sas & in the window
where the pointers were set. However, since the next few procedures are
very simple, it is faster to just use the command line.
- 4)
- If it doesn't already exist, create a CIF file in the ODF directory
using the SAS task cifbuild (see §4.5.1). If a CIF file
has previously been produced, it is only necessary to rerun cifbuild if
the CCF has changed. Be sure to set the environment parameter afterwards.
- cd ODF
cifbuild
setenv SAS_CCF /full/path/to/ODF/ccf.cif
- 5)
- If it hasn't already been done (don't do it twice), while still
in the ODF directory, prepare the data by using the SAS task odfingest
(see §4.5.2) and setting the environment parameter. It is only necessary
to run it once on any data set and will
cause problems if it is run a second time. If for some reason odfingest
must be rerun, you must first delete the earlier file produced by odfingest
(*SUM.SAS).
- odfingest
setenv SAS_ODF /full/path/to/file/full_name_of_*SUM.SAS
- 6)
- In your ``processing directory'' PROC, run the SAS task rgsproc.
From the command line of a window where SAS has been initialized, enter:
- rgsproc orders='1 2' bkgcorrect=no withmlambdacolumn=yes
where
- orders - dispersion orders to extract
- bkgcorrect - subtract background from source spectra?
- withmlambdacolumn - include a wavelength column in the event file product
This takes several minutes, and outputs 12 files per RGS, plus 3 general use
FITS files. At this point, renaming files to something easy to type is a good idea.
- ln -s *R1*EVENLI*FITS r1_evt1.fits
ln -s *R2*EVENLI*FITS r2_evt1.fits
Once the new event files have been obtained, the analysis techniques described
in §6.3 and later can be used.
6.2 Potentially useful tips for using the pipeline
The pipeline task, rgsproc, is very flexible and can address potential
pitfalls for RGS users. In §6.1, we used a simple set of parameters
with the task, and if this is sufficient for your data, feel free to skip to
§6.3. In the following sections, we will look at the cases of a nearby
bright optical source, a nearby bright X-ray source, and a user-defined source.
6.2.1 A Nearby Bright Optical Source
With certain pointing angles, zeroth-order optical light may be reflected off the
telescope optics and cast onto the RGS CCD detectors. If this falls on an
extraction region, the current energy calibration will require a wavelength-dependent
zero-offset. Stray light can be detected on RGS DIAGNOSTIC images taken before,
during and after the observation. This test, and the offset correction, are not
performed on the data before delivery. To check for stray light and apply the
appropriate offsets use, type
- rgsproc orders='1 2' bkgcorrect=no calcoffsets=yes withoffsethistogram=no
where the parameters are as described in §6.1 and
- calcoffsets - calculate PHA offsets from diagnostic images
- withoffsethistogram - produce a histogram of uncalibrated excess for the user
6.2.2 A Nearby Bright X-ray Source
In the example above, it is assumed that the field around the source contains
sky only. Provided a bright background source is well-separated from the target
in the cross-dispersion direction, a mask can be created that excludes it from
the background region. Here the source has been identified in the EPIC images
and its coordinates have been taken from the EPIC source list which is included
among the pipeline products. The bright neighboring object is found to be the
third source listed in the sources file. The first source is the target:
- rgsproc orders='1 2' bkgcorrect=no withepicset=yes
epicset=PiiiiiijjkkaablllEMSRLInmmm.FTZ exclsrcsexpr='INDEX==1&&INDEX==3'
where the parameters are as described in §6.1 and
- withepicset - calculate extraction regions for the sources contained
in an EPIC source list
- epicset - name of the EPIC source list, such as generated by
emldetect or eboxdetect procedures
- exclsrcsexpr - expression to identify which source(s) should be excluded
from the background extraction region
6.2.3 User-defined Source Coordinates
If the true coordinates of an object are not included in the EPIC source list or
the science proposal, the user can define the coordinates of a new source by typing:
- rgsproc orders='1 2' bkgcorrect=no withsrc=yes srclabel=ABDor srcstyle=radec
srcra=82.185493 srcdec=-65.449329
where the parameters are as described in §6.1 and
- withsrc - make the source be user-defined
- srclabel - source name
- srcstyle - coordinate system in which the source position is defined
- srcra - the source's right ascension in decimal degrees
- srcdec - the source's declination in decimal degrees
Since the event files are current, we can proceed with some simple analysis
demonstrations, which will allow us to generate filters. The following sections
describe the use of SAS tasks using
the both the command line and GUI interfaces, except in cases where one of
the methods is particularly easy. People new to SAS will likely prefer the
GUI, at least at first; however, as they become more familiar with the software
and the keywords, they will probably migrate to the command line, which is
faster. Assuming that the parameter values for any given task are the same,
it does not matter if a task is invoked on the command line or in the GUI;
the output files will be identical. The SAS xmmselect GUI provides a very
simple method for producing and displaying images, spectra, and light curves,
and is the recommended method for extracting data unless large numbers of
sources are being analyzed.
We are now ready to invoke the SAS GUI if we have not already done so.
Make sure that you are in the directory where you want the output to go before
invoking the SAS GUI or any of the SAS tasks on the command line!
- sas &
- 1)
- Invoke the xmmselect GUI from the SAS GUI.
To invoke a task, you need only start typing the task name, and when it is
highlighted, hit a carriage return.
- When xmmselect is invoked a dialog box will first
appear requesting a file name. One can either use the browser button or
just type the file name in the entry area, ``r1_evt1.fits'' in this case.
To use the browser, first click on the file folder icon button on the right
which will bring up a second GUI for the file selection. Double click on
the desired event file in the right-hand column (you may have to open the
appropriate directory first), click on the ``EVENTS'' extension in the
right-hand column (which selects the extension), and then click ``Ok''.
The directory GUI will then disappear and then click ``Run'' on the selection GUI.
- When the file name has been submitted the xmmselect
GUI (Figure 5.2) GUI will appear, along with a dialog box offering to display
the selection expression. The selection expression will include the filtering
done to this point on the event file, which for the pipeline processing
includes for the most part CCD and GTI selections.
6.3.2 Create and Display an Image
Two commonly-made plots are those showing PI vs. BETA_CORR (also known
as ``banana plots'') and XDSP_CORR vs. BETA_CORR.
To create images by using the xmmselect GUI:
- 1)
- Check the square boxes to the left of the ``BETA_CORR'' and
``PI'' entries.
- 2)
- Click on the ``Image'' button near the bottom of the page. This
brings up the evselect GUI (Figure 6.3).
- 3)
- Click on the ``Image'' tab in the evselect GUI.
- 4)
- Confirm that the withimageset box is checked.
- 5)
- In the imageset box, change the output image name from image.ds
to something descriptive, in this case, pi_bc.fits.
- 6)
- Click on the ``Run'' button on the lower left corner of the
evselect GUI.
- Different binnings and other selections can be invoked by accessing
the ``Image'' tab at the top of the GUI. The default settings are reasonable,
however, for a basic image.
- The resultant image is automatically displayed using ds9.
Similarly, plots can be made comparing BETA_CORR to XDSP_CORR.
These two example plots can be seen in Figure 6.4.
To create images by using the task evselect on the command line:
- 1)
- In the window in which SAS was initialized, and in the directory
where you want the output to go, type the following command (all on one line).
Make sure the correct path is given for the event file.
- evselect table=r1_evt1.fits:EVENTS withimageset=yes
imageset=pi_bc.fits xcolumn=BETA_CORR ycolumn=PI
imagebinning=imageSize ximagesize=600 yimagesize=600
where
- table - input event table
- withimageset - make an image
- imageset - name of output image
- xcolumn - event column for X axis
- ycolumn - event column for Y axis
- imagebinning - form of binning, force entire image into
a given size or bin by a specified number of pixels
- ximagesize - output image pixels in X
- yimagesize - output image pixels in Y
- Plots comparing BETA_CORR to XDSP_CORR may be made
in a similar way. The output files can be viewed by using a standard FITS
display, such as ds9 (see Figure 6.4) :
- ds9 pi_bc.fits &
6.3.3 Create and Display a Light Curve
The background is assessed through examination of the light curve. We will extract
a region, CCD9, that is most susceptible to proton events and generally records the
least source events due to its location close to the optical axis. Also, to avoid
confusing solar flares for source variability, a region filter that that removes the
source from the final event list should be used. The region filters are kept in the
source file product P*SRCLI_*.FIT. (For our example data, this would be
P0134520301R1S001SRCLI_0000.FIT).
To create light curves of the observation by using the xmmselect GUI:
- 1)
- Enter the filtering criteria in the ``Selection expression'' box at the top of
the xmmselect GUI:
(CCDNR==9)&&(REGION(P0134520301R1S001SRCLI_0000.FIT:RGS1_BACKGROUND,BETA_CORR,XDSP_CORR))
- 2)
- Check the round box to the left of the time entry.
- 3)
- Click on the ``OGIP Rate Curve'' button near the bottom of the page. This brings
up the evselect GUI (Figure 5.3).
- 4)
- Click on the ``Lightcurve'' tab and confirm that the withrateset box is checked.
- 5)
- Change the timebinsize to a reasonable amount, e.g. 10 or 100 s, and change
the default output file name in the rateset box to something appropriate, in this case,
r1_ltcrv.fits.
- 6)
- Click on the ``Run'' button at the lower left corner of the evselect GUI.
- The resultant light curve is displayed automatically using Grace
(see Figure 6.1).
To create a light curve of the observation by using the task evselect
on the command line:
- 1)
- In the window where SAS was initialized, and in the directory where you want
the output to go, type the following command (all on one line). Make sure the correct
path is given for the event file.
- evselect table=r1_evt1.fits:EVENTS withrateset=yes rateset=r1_ltcrv.fits
maketimecolumn=yes timebinsize=100 makeratecolumn=yes
expression=
'(CCDNR==9)&&(REGION(P0134520301R1S001SRCLI_0000.FIT:RGS1_BACKGROUND,BETA_CORR,XDSP_CORR))'
where
- table - input event table
- withrateset - make a light curve
- rateset - name of output light curve file
- maketimecolumn - control to create a time column
- timebinsize - time binning (seconds)
- makeratecolumn - control to create a count rate column, otherwise a count column will be created
- expression - filtering criteria
- The output file r1_ltcrv.fits can be viewed using dsplot:
- dsplot table=r1_ltcrv.fits x=TIME y=RATE &
where
- table - input event table
- x - column for plotting on the X axis
- y - column for plotting on the Y axis
The light curve is shown in Figure 6.1.
Figure 6.1:
Background event rate from the RGS1 CCD9 chip. The flares are solar
events. The time units are elapsed mission time.
|
6.3.4 Generating the Good Time Interval (GTI) File
Examination of the lightcurve shows that there are two noisy sections,
one between 9.6405e7 and 9.6413e7 seconds, and another between 9.6422e7
and 9.6425e7 seconds. Both show rates well in excess of the normal background
count rate of 0.05 count/second. There are two procedures that make the GTI
file (gtibuild and tabgtigen) that, when applied to the event
file in another run of rgsproc, will excise these sections.
The first method, using gtibuild, requires a text file as input.
In the first two columns, refer to the start and end times (in seconds) that
you are interested in, and in the third column, indicate with either a +
or - sign whether that region should be kept or removed. In the example case,
then, we would write in our ASCII file (named gti.txt):
9.6405e7 9.6413e7 -
9.6422e7 9.6425e7 -
and proceed to the SAS task gtibuild.
To make the GTI with gtibuild in the SAS GUI:
- 1)
- Double-click on the gtibuild task in the SAS GUI.
- 2)
- Enter the name of the text file in the file box.
- 3)
- Enter the name of the output fits file in the table box.
- 4)
- Click ``Run''.
To make the GTI with gtibuild on the command line:
- 1)
- In the window where SAS was initialized, and in the directory
where you want the output to go, type the following command.
- gtibuild file=gti.txt table=gti.fits
where
- file - intput text file
- table - output gti table
To make the GTI with tabgtigen in the SAS GUI:
- 1)
- Double-click on the tabgtigen task.
- 2)
- Enter the name of the lightcurve file in the table box,
in this case, r1_ltcrv.fits.
- 3)
- Enter the name of the output file in the gtiset box, in
this case, gti.fits.
- 4)
- Enter the filtering expression in the expression box. Since
the nominal count rate is about 0.05 count/sec, we will set the upper
limit to 0.2 count/sec: RATE0.2
- 5)
- Click ``Run''.
To make the GTI with tabgtigen from the command line:
- 1)
- In the window where SAS was initialized, and in the directory where
you want the output to go, type the following command.
- tabgtigen table=r1_ltcrv.fits gtiset=gti.fits expression='(RATE0.2)'
where
- table - the lightcurve file
- gtiset - output gti table
- expression - the filtering criteria. Since the nominal
count rate is 0.05 about count/sec, we have set the upper
limit to 0.2 count/sec.
6.3.5 Applying the GTI
Now that we have GTI file, we can apply it to the event file by running rgsproc
again. rgsproc is a complex task, running several steps, with five different entry
and exit points. It is not necessary to rerun all the steps in the procudure, only the
ones involving filtering.
To rerun the pipeline in the SAS GUI:
- 1)
- Double-click on rgsproc in the SAS GUI.
- 2)
- In the ``global'' tab, make sure that the orders box is set for
both orders, 1 2 .
- 3)
- In the ``global'' tab, use the pulldown menus for entrystage and
exitstage to select 3:filter and 5:fluxing, respectively.
- 4)
- In the ``filter'' tab, enter the name of the GTI file, in this case,
gti.fits, in the auxgtitables box.
- 5)
- In the ``spectra'' tab, click on the ``rgsspectrum'' tab, and make sure
that bkgcorrect is set to no.
- 6)
- In the ``angles'' tab, make sure the withmlambda column is set to
yes.
- 7)
- Click on ``Run''.
To rerun the pipeline from the command line:
- 1)
- In the window where SAS was initialized, and in the directory where
you want the output to go, type the following command, all on one line:
- rgsproc orders='1 2' auxgtitables=gti.fits bkgcorrect=no
withmlambdacolumn=yes entrystage=3:filter finalstage=5:fluxing
where
- orders - spectral orders to be processed
- auxgtitables - gti file in FITS format
- bkgcorrect - subtract background from source spectra?
- withmlambdacolumn - include a wavelength column in the event file product
- entrystage - stage at which to begin processing
- finalstage - stage at which to end processing
6.3.6 Creating the Response Matrices (RMFs)
Response matrices (RMFs) are not provided as part of the pipeline product package, so you must create your
own before analyzing data. This can be done with the package rgsrmfgen.
To make the RMFs using the GUI:
- 1)
- Double-click on the rgsrmfgen task in the GUI.
- 2)
- In the spectrumset box, enter the name of the spectrum file; it has
the form *SRSPEC*, and in our case, is P0134520301R1S001SRSPEC1001.FIT.
- 3)
- In the evlist box, enter the name of the event list, r1_evt1.fits.
- 4)
- Set emin to 0.4, emax to 2.5, and rows to 5000.
- 5)
- Set rmfset to the output file name, in this case, r1_o1_rmf.fits.
- 6)
- Click ``Run''.
To make the RMFs from the command line:
- 1)
- In the window where SAS was initialized, and in the directory where you
want the output to go, type the following command (all on one line).
- rgsrmfgen spectrumset=P0134520301R1S001SRSPEC1001.FIT rmfset=r1_o1_rmf.fits
evlist=r1_evt1.fits emin=0.4 emax=2.5 rows=5000
where
- spectrumset - spectrum file
- evlist - event file
- emin - lower energy limit of the response file
- emax - upper energy limit of the response file
- rows - number of energy bins; this should be greater than 3000
- rmfset - output FITS file
6.4 Fitting a Spectral Model
Now that we have a response file, we can fit the spectrum using Xspec.
- 1)
- On the command line, type:
- xspec
Enter the data, background, and response file at the prompts, and edit the fitting
parameters as needed.
XSPEC> data P0136540101R1S001SRSPEC1003.FIT ! input data
XSPEC> back P0136540101R1S001BGSPEC1003.FIT ! input background
XSPEC> resp r1_o1_rmf.fits ! input response file
XSPEC> model wabs*mekal ! set spectral model to absorbed mekal
wabs:nH> 1
mekal:kT> 1
mekal:nH>
mekal:Anbundanc> .4
mekal:Redshift>
mekal:Switch> 0
mekal:norm> 1
XSPEC> renorm
XSPEC> fit
XSPEC> cpd /xw
XSPEC> setplot wave
XSPEC> setplot command window all
XSPEC> setplot command log x off
XSPEC> setplot command wind 1
XSPEC> setplot command r y 1e-5 1.6
XSPEC> setplot command wind 2
XSPEC> setplot command r y -9.99 9.99
XSPEC> plot data residuals
XSPEC> exit
Figure 6.2 shows the fit to the spectrum.
Figure 6.2:
1st order RGS1 spectrum of AB Dor. The fit is an
absorbed single-temperature mekal model. The gap between 10-15Å is
due to the absence of CCD7.
|
6.4.1 Combining RGS1 and RGS2 Spectra
While it is tempting to merge the RGS1 and RGS2 data, or data from different
pointings, to provide a single spectrum with a signal-to-noise improvement over
either individual spectrum, this is strongly discouraged since it results in data
degradation.
The pointings of the two instruments are not identical, resulting in different
dispersion angles and wavelength scales. Separate response files are always
required for each unit. While it is possible to merge spectra and response files,
great care must be taken to account for different exposure times, background
subtractions, error propagation, and so on. However, the resulting response will
always have inferior resolution to the originals. It is therefore simpler and
more accurate to keep data from the two RGS units separate and use both sets to
fit one model in tandem.
- 1)
- On the command line, type:
- xspec
XSPEC>data 1:1 P0136540101R1S001SRSPEC1003.FIT 1:2 P0136540101R1S001SRSPEC2003.FIT
XSPEC>ignore bad
XSPEC>model phabs*mekal
6.5 Approaches to Spectral Fitting
For data sets of high signal-to-noise and low background, where counting statistics
are within the Gaussian regime, the data products above are suitable for analysis
using the default fitting scheme in XSPEC, -minimization. However, for
low count rates, in the Poisson regime, -minimization is no longer suitable.
With low count rates in individual channels, the error per channel can dominate over
the count rate. Since channels are weighted by the inverse-square of the errors during
model fitting, channels with the lowest count rates are given overly-large
weights in the Poisson regime. Spectral continua are consequently often fit
incorrectly, with the model lying underneath the true continuum level.
This will be a common problem with most RGS sources. Even if count rates are large,
much of the flux from these sources can be contained within emission lines, rather
than the continuum. Consequently, even obtaining correct equivalent widths for such
sources is non-trivial. There are two approaches to fitting low signal-to-noise
RGS data, spectral rebinning and maximum-likelihood statistics. The correct approach
would normally be to use an optimization of the two.
6.5.1 Spectral Rebinning
By grouping channels in appropriately large numbers, the combined signal-to-noise
of groups will jump into the Gaussian regime. There are two ways to do this: the
FTOOL grppha, or the RGS pipeline. grppha can group
channels using an algorithm which bins up consecutive channels until a count rate
threshold is reached. This method conserves the resolution in emission lines above
the threshold while improving statistics in the continuum.
- 1)
- On the command line, type the following and edit parameters as needed.
- grppha
> Please enter PHA filename[] P0136540101R1S001SRSPEC1003.FIT
> Please enter output filename[] P0136540101R1S001SRSPEC1003.bin.FIT
> GRPPHA[] group min 30
> GRPPHA[] exit
The disadvantage of using grppha is that, although channel errors are propagated
through the binning process correctly, the errors column in the original spectrum
product is not strictly accurate. The problem arises because there is no good way
to treat the errors within channels containing no counts. To allow statistical fitting,
these channels are arbitrarily given an error value of unity, which is subsequently
propagated through the binning. Consequently, the errors are overestimated in the
resulting spectra.
The other approach, which involves calling the RGS pipeline after it is complete,
bins the data during spectral extraction. The following rebins the pipeline
spectrum by a factor 3.
- 1)
- On the command line, type
- rgsproc orders='1 2' rebin=3 rmfbins=4000 entrystage=4:spectra
finalstage=5:fluxing bkgcorrect=no
where
- orders - dispersion orders to extract
- rebin - wavelength rebinning factor
- rmfbins - number of bins in the response file; this should be
greater than 3000
- entrystage - entry stage to the pipeline
- finalstage - exit stage for the pipeline
One disadvantage of this approach is that you can only choose integer binning
of the original channel size. To change the sampling of the events, the pipeline
must be run from the second stage (``angles'') or earlier.
- 1)
- On the command line, type
- rgsproc orders='1 2' nbetabins=1133 rmfbins=4000 entrystage=2:angles
finalstage=fluxing bkgcorrect=no
where the parameters are as defined previously, and
- nbetabins - number of bins in the dispersion direction; the default is 3400
The disadvantage of using rgsproc, as opposed to grppha, is that the
binning is linear across the dispersion direction. Velocity resolution is lost in the
lines, so the accuracy of redshift determinations will be degraded, transition
edges will be smoothed, and neighboring lines will become blended.
6.5.2 Maximum-Likelihood Statistics
The second method is to replace the -minimization scheme with the Cash
maximum-likelihood scheme (cstat in Xspec) when fitting data. This method is much
better suited to data with low count rates and is a suitable option only if one
is running Xspec v11.1.0 or later. The reason for this is that RGS spectrum files
have prompted a slight modification to the OGIP standard. Because the RGS spatial
extraction mask has a spatial-width which is a varying function of wavelength, it
has become necessary to characterize the BACKSCL and AREASCL parameters
as vectors (i.e., one number for each wavelength channel), rather than scalar keywords
as they are for data from the EPIC cameras and past missions. These quantities map
the size of the source extraction region to the size of the background extraction
region and are essential for accurate fits. Only Xspec v11.1.0, or later versions,
are capable of reading these vectors, so be certain that you have an up-to-date
installation at your site.
One caveat of using the cstat option is that the scheme requires a
``total'' and ``background'' spectrum to be loaded into Xspec. This is in order
to calculate parameter errors correctly. Consequently, be sure not to use the
``net'' spectra that were created as part of product packages by SAS v5.2 or
earlier. To change schemes in Xspec before fitting the data, type:
- XSPEC statistic cstat
6.6 Analysis of Extended Sources
6.6.1 Region masks
The optics of the RGS allow spectroscopy of reasonably extended sources, up
to a few arc minutes. The width of the spatial extraction mask is defined by
the fraction of total events one wishes to extract. With the default pipeline
parameter values, 90% of events are extracted, assuming a point-like source.
Altering and optimizing the mask width for a spatially-extended source may
take some trial and error, and, depending on the temperature distribution of
the source, may depend on which lines one is currently interested in. While
AB Dor is not an extended source, the following example increases the
width of the extraction mask and ensures that the size of the background
mask is reduced so that the two do not overlap.
To adjust the region mask with rgsproc in the SAS GUI:
- 1)
- Double-click on rgsproc in the SAS GUI.
- 2)
- In the ``global'' tab, make sure that the orders box is set
for both orders, 1 2.
- 3)
- In the ``global'' tab, use the pulldown menus for entrystage
and exitstage to select 4:spectra and 5:fluxing, respectively.
- 4)
- In the ``spectra'' tab, in the ``rgsregions'' sub-tab, set both xpsfincl
and xpsfexcl to 99, and pdistincl to 95.
- 5)
- Click ``Run''.
To adjust the region mask with rgsproc from the command line:
- 1)
- Type the following on the command line, in the directory where you want
the output to go:
- rgsproc orders='1 2' entrystage=4:spectra finalstage=5:fluxing bkgcorrect=no
xpsfincl=99 xpsfexcl=99 pdistincl=95
where parameters are as they were decsribed previously, and
- xpsfincl - include this fraction of point-source events inside
the spatial source extraction mask
- xpsfexcl - exclude this fraction of point-source events from
the spatial background extraction mask
- pdistincl - include this fraction of point-source events inside
the pulse height extraction mask
Observing extended sources effectively broadens the psf of the spectrum in the
dispersion direction. Therefore, it is prudent to also increase the width of the
PI masks using the pdistincl parameter in order to prevent event losses.
6.6.2 Fitting spectral models to extended sources
RGS response matrices are consistent for point sources only. Since extended
source spectra are broadened, the simplest way to deal with this problem during
spectral fitting is to reproduce the broadening function, and convolve it across
the spectral model. Xspec v11.2 contains the convolution model rgsxsrc.
It requires two external files to perform the operation:
- 1)
- An OGIP FITS image of the source. The better the resolution of the
image, the more accurate the convolution. For example, if a Chandra image of
the source is available, this will provide a more accurate result than an EPIC image.
- 2)
- An ASCII file containing three lines of input. For this example case,
we will name it xsource.mod. It defines three environment
variables and should look like this example:
- RGS_XSOURCE_IMAGE ./MOS1.fit
- RGS_XSOURCE_BORESIGHT 23:25:19.8 -12:07:25 247.302646
- RGS_XSOURCE_EXTRACTION 2.5
where
- RGS_XSOURCE_IMAGE - path to the source image
- RGS_XSOURCE_BORESIGHT - RA, Dec of the center of the source and PA
of the telescope
- RGS_XSOURCE_EXTRACTION - The extent (in arcmin), centered on the
source, over which you want to construct the convolution function. You
want this ``aperture'' to be larger than the source itself.
To set these environment variables within Xspec execute the command:
- xset rgs_xsource_file xsource.mod
Here is an example. Note that the spectral order is always negative.
- xspec
XSPEC>data P0108460201R1S004SRSPEC1003.FIT
XSPEC>ignore bad
XSPEC>xset rgs_xsource_file xsource.mod
XSPEC>model rgsxsrc*wabs*mekal
rgsxsrc:order>-1
wabs:nH>1
mekal:kT>2
mekal:nH>1
mekal:Abundanc>1
mekal:Redshift>
mekal:Switch>0
mekal:norm>1
XSPEC>renorm
XSPEC>fit
XSPEC>setplot device /xs
XSPEC>setplot wave
XSPEC>setplot command window all
XSPEC>setplot command log x off
XSPEC>plot data residuals
XSPEC>exit
Do you really want to exit? (y)y
Figure 6.3 compares a point source model with an extended source counterpart.
Figure 6.3:
The top figure is a thin, thermal plasma at 2 keV from a point
source. The lower figure is the same spectral model, but convolved by
the MOS1 0.3-2.0 keV spatial profile of a low-redshift cluster.
|
6.6.3 Model limitations
Users should be aware that this method assumes an isothermal source (or
uniform emissivity from line to line in the case of a non-thermal spectrum)
where the spatial distributions of all the lines are identical. In reality,
however, the thermal structure of the source is likely to be more complicated.
The broad-band convolution function may bear little resemblance to the correct
function for particular line transitions.
One way around this problem would be to have a temperature map of the source
to define line emissivity across the source and convolve the model spectrum
accordingly. The RGS instrument team at the Columbia Astrophysics Laboratory
are developing a Monte Carlo code to perform an operation with this effect.
While it is unlikely the code will be publicly available in the near future,
the team welcomes investigators who would be interested in collaboration.
Interested parties are encouraged to contact John Peterson
(jrpeters@astro.columbia.edu).
6.7 In A Nutshell
To summarize, the steps you must take to prepare your data for
analysis are:
- 1)
- Obtain the raw and pipelined data from the XMM archive.
- 2)
- Initialize SAS.
- 3)
- Make the CCF file and ODF summary file.
- 4)
- Rerun the pipeline.
- 5)
- Generate a light curve to determine the appropriate filter for your data.
- 6)
- Make the good time interval (GTI) file.
- 7)
- Apply the GTI file by rerunning the pipeline again.
- 8)
- Generate the response file (RMF).
Next: 7. An OM Data
Up: XMM ABC Guide
Previous: 5. An EPIC Data
Contents
Lynne Valencic
2008-09-02