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Subsections


7. An EPIC Data Processing and Analysis Primer (Imaging Mode, GUI)

So, you've received an XMM-Newton EPIC data set. What are you going to do with it? After checking what the observation consists of (see § 3.2), you should note when the observation was taken. If it is a recent observation, it was likely processed with the most recent calibrations and SAS, and you can immediately start to analyze the Pipeline Processed data. However, if it is more than a year old, it was probably processed with older versions of CCF and SAS prior to archiving, and the pipeline should be rerun to generate event files with the latest calibrations.

As noted in Chapter 4, a variety of analysis packages can be used for the following steps. However, as the SAS was designed for the basic reduction and analysis of XMM-Newton data (extraction of spatial, spectral, and temporal data), it will be used here for demonstration purposes. SAS will be required at any rate for the production of detector response files (RMFs and ARFs) and other observatory-specific requirements. (Although for the simple case of on-axis point sources the canned response files provided by the SOC can be used.)

NOTE: For PN observations with very bright sources, out-of-time events can provide a serious contamination of the image. Out-of-time events occur because the read-out period for the CCDs can be up to $\sim6.3$% of the frame time. Since events that occur during the read-out period can't be distinguished from others events, they are included in the event files but have invalid locations. For observations with bright sources, this can cause bright stripes in the image along the CCD read-out direction. For a more detailed description of this issue, check: http://www.mpe.mpg.de/xray/wave/xmm/cookbook/preparation/index.php

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 analysis is taking place in the PROC directory, and the CCF data are in the directory CCF.

If your data are recent, you need only to gunzip the files and prepare the data for processing (see §5. Feel free to skip the discussion on repipelining (§7.1) and proceed to later discussions. In any case, for simplicity, it is recommended that you change the name of the unzipped event file to something easy to type. For example, an MOS1 event list:

cp PPS/PiiiiiijjkkM1SlllMIEVLI0000.FTZ PROC/mos1.fits

where

iiiiiijjkk - observation number
lll - exposure number within the observation

Various analysis procedures are demonstrated using the Lockman Hole SV1 dataset, ObsID 0123700101, which definitely needs to be repipelined. The following procedures are applicable to all XMM-Newton datasets, so it is not required that you use this particular dataset; any observation should be sufficient.

If you simply want to have a quick look at your data, the ESKYIM files contain EPIC sky images in different energy bands whose ranges are listed in Table 3.3. 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). In addition, the image of the total band pass for all three EPIC detectors is also provided in PNG format which can be displayed with a web browser. Also, the PP source list is provided in both zipped FITS format (readable by fv) and as an HTML file.

For detailed descriptions of PP data nomenclature, file contents, and which tasks can be used to view them, see Tables 3.2 and 3.3. For detailed descriptions of ODF data nomenclature and file contents, see Table 3.1.


7.1 Rerun the Pipeline

We assume that the data was prepared and environment variables were set according to §5, the GUI has been invoked (see §5.3), and we are in our working directory, ``PROC''.

From the upper window of the GUI, select emproc to process the MOS data, and epchain or epproc to process the PN data. Double-clicking the task will bring up pop-up windows that will allow you to change the (many) parameters; however, for most cases, the default settings are fine, so just click "Run".

If the dataset has more than one exposure, a specific exposure can be accessed with epchain by setting the exposure parameter in the ``General'' tab to the exposure number.

To create an out-of-time event file for your PN data, toggle the parameter withoutoftime on the Epevents tab to ``yes''.

By default, none of these tasks keep any intermediate files they generate. Epchain maintains the naming convention described in §3.3.3. Emproc and epproc designate their output event files with ``*ImagingEvts.ds''. In any case, you may want to name the new files something easy to type. For example, to rename one of the new MOS1 event files output from emproc, type

mv 0070_0123700101_EMOS1_S001_ImagingEvts.ds mos1.fits

Figure 7.1: The xmmselect GUI.

\includegraphics[scale=0.5]{xmmselect.ps}

Remember that tasks place output files in whatever directory you happened to be in when the SAS GUI was called, so either open and close the GUI in the directory where you want the output or move the files to the directory they should be in.


7.2 An Introduction to xmmselect

The task xmmselect is used for many procedures in the GUI. Like all tasks, it can easily be invoked by starting to type the name and pressing enter when it is highlighted.

When xmmselect is invoked a dialog box will first appear requesting a file name. You can either use the browser button or just type the file name in the entry area, ``mos1.fits:EVENTS'' in this case. To use the browser, select the file folder icon; this will bring up a second window for the file selection. Choose the desired event file, then the ``EVENTS'' extension in the right-hand column, and click ``OK''. The directory window will then disappear and you can click ``Run'' on the selection window.

When the file name has been submitted the xmmselect GUI (see Figure 7.1) 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.

Figure 7.2: The evselect GUI.

\includegraphics[scale=0.5]{evselect.ps}


7.3 Create and Display an Image

To create an image in sky coordinates by using the xmmselect, call xmmselect and load the event file as in §7.2. Then,

1)
Check the square boxes to the left of the ``X'' and ``Y'' entries.
2)
Click the ``Image'' button near the bottom of the page. This brings up the evselect GUI (see Figure 7.2).
3)
In the imageset box, enter the name of the output file, in this case, image.fits.
4)
Click 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 written to the file image.fits, and is automatically displayed using ds9 (see Figure 7.3).

Figure 7.3: The MOS1 image, displayed in ds9.

\includegraphics[scale=0.5]{mos1-lh-im.ps}


7.4 Applying Standard Filters the Data

The filtering expressions for the MOS and PN are:

(PATTERN $<=$ 12)&&(PI in [200:12000])&&#XMMEA_EM

and

(PATTERN $<=$ 12)&&(PI in [200:15000])&&#XMMEA_EP

The first two expressions will select good events with PATTERN in the 0 to 12 range. The PATTERN value is similar the GRADE selection for ASCA data, and is related to the number and pattern of the CCD pixels triggered for a given event.The PATTERN assignments are: single pixel events: PATTERN == 0, double pixel events: PATTERN in [1:4], triple and quadruple events: PATTERN in [5:12].

The second keyword in the expressions, PI, selects the preferred pulse height of the event; for the MOS, this should be between 200 and 12000 eV. For the PN, this should be between 200 and 15000 eV. This should clean up the image significantly with most of the rest of the obvious contamination due to low pulse height events. Setting the lower PI channel limit somewhat higher (e.g., to 300 eV) will eliminate much of the rest.

Finally, the #XMMEA_EM (#XMMEA_EP for the PN) filter provides a canned screening set of FLAG values for the event. (The FLAG value provides a bit encoding of various event conditions, e.g., near hot pixels or outside of the field of view.) Setting FLAG == 0 in the selection expression provides the most conservative screening criteria and should always be used when serious spectral analysis is to be done on the PN. It typically is not necessary for the MOS.

It is a good idea to keep the output filtered event files and use them in your analyses, as opposed to re-filtering the original file with every task. This will save much time and computer memory. As an example, the Lockman Hole data's original event file is 48.4 Mb; the fully filtered list (that is, filtered spatially, temporally, and spectrally) is only 4.0Mb!

To filter the data using xmmselect,

1)
Enter the filtering criteria in the ``Selection Expression'' area at the top of the xmmselect window:
(PATTERN $<=$ 12)&&(PI in [200:12000])&&#XMMEA_EM
2)
Click on the ``Filtered Table'' box at the lower left of the xmmselect GUI.
3)
Change the evselect filteredset parameter, the output file name, to something useful, e.g., mos1_filt.fits
4)
Click ``Run''.


7.5 Create and Display a Light Curve

Sometimes, it is necessary to use filters on time in addition to those mentioned above. This is because of soft proton background flaring, which can have count rates of 100 counts/sec or higher across the entire bandpass.

It should be noted that the amount of flaring that needs to be removed depends in part on the object observed; a faint, extended object will be more affected than a very bright X-ray source.

To determine if our observation is affected by background flaring, we can make a light curve with xmmselect. Load the event file as shown in § 7.2. Then,

1)
Check the round box to the left of the ``Time'' entry.
2)
Click on the ``OGIP Rate Curve'' button near the bottom of the page. This brings up the evselect GUI (see Figure 7.2).
3)
Click on the ``Lightcurve'' tab and change the ``timebinsize'' to a reasonable amount, e.g. 10 or 100 s. In the ``rateset'' textbox, enter the name of the output file, for example, mos1_ltcrv.fits.
4)
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 7.4). It can also be viewed using fv; on the command line, type

or, alternatively,

fv mos1_ltcrv.fits &

In the fv pop-up window, the RATE extension will be available in the second row (index 1, as numbering begins with 0). Select ``PLOT'' from this row, and select the column name and axis on which to plot it.

Figure 7.4: The light curve, displayed in fv.

\includegraphics[scale=0.5]{LockmanHole_0123700101_ltcrv_fv.eps}


7.6 Applying Time Filters the Data

Taking a look at the light curve, we can see that there is a very large flare toward the end of the observation and two much smaller ones in the middle of the exposure.

There are several ways to filter an event file: on TIME, with an explicit reference to the TIME parameter in the filtering expression or by creating a secondary Good Time Interval (GTI) file, or on RATE, which requires making a new GTI file. New GTI files are easily made with the task tabgtigen or gtibuild. These are discussed in detail below. Any of these methods will produce a cleaned file, so which one to use is a matter of the user's preference.

Filter on RATE With tabgtigen

Examining the light curve shows us that during non-flare times, the count rate is quite low, about 1.3 ct/s, with a small increase at 7.3223e7 seconds to about 6 ct/s. We can use that to generate the GTI file by calling tabgtigen from the SAS GUI and loading the event file mos1_filt.fits and its EVENTS in the manner as shown in §7.2. Then,

1)
Edit the output file name, the gtiset parameter; here, we will use gtiset.fits.
2)
Enter the filtering expression, (RATE <= 6).
3)
Click "Run".

The new GTI file can be applied with xmmselect. With the mos1_filt.fits event file loaded,

1)
In the "Selection Expression" box, type GTI(gtiset.fits,TIME).
2)
Click on the "Filtered Table" box at the lower left of the xmmselect GUI.
3)
Change the evselect filteredset parameter, the output file name, to
$   $ something useful; here, we will use mos1_filt_time.fits.
4)
Click "Run".

Filter on TIME With tabgtigen

Alternatively, we could have chosen to make a new GTI file by noting the times of the flaring in the light curve and using that as a filtering parameter. The big flare starts around 7.32276e7 s, and the smaller ones are at 7.32119e7 s and 7.32205e7 s. The expression to remove these would be (TIME $<=$ 73227600)&&! (TIME IN [7.32118e7:7.3212e7])&&(TIME IN [7.32204e7:7.32206e7]). The syntax &&(TIME $<$ 73227600) includes only events with times less than 73227600, and the "!" symbol stands for the logical "not", so use &&!(TIME in [7.32118e7:7.3212e7]) to exclude events in that time interval. To use these filtering parameters, call tabgtigen from the SAS GUI and load the event file mos1_filt.fits and its EVENTS extension, in the manner as shown in §7.2. Then,

1)
Edit the output file name, the gtiset parameter; here, we will use gtiset.fits.
2)
Enter the filtering expression, (TIME $<=$ 73227600)&&!(TIME IN
$   $ [7.32118e7:7.3212e7])&&!(TIME IN [7.32204e7:7.32206e7])
.
3)
Click "Run".

The new GTI file can be applied with xmmselect. With the mos1_filt.fits event file loaded,

1)
In the "Selection Expression" box, type GTI(gtiset.fits,TIME).
2)
Click on the "Filtered Table" box at the lower left of the xmmselect GUI.
3)
Change the evselect filteredset parameter, the output file name, to
$   $ something useful; here, we will use mos1_filt_time.fits.
4)
Click "Run".

Filter on TIME With gtibuild

This task requires a text file as input. In the first two columns, enter 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. Each good (or bad) time interval should get its own line. In the example case, we would write in our ASCII file (named gti.txt):

0 73227600 + # Good time from the start of the observation...
7.32118e7 7.3212e7 - # but without a small flare here,
7.32204e7 7.32206e7 - # and here.

and proceed to gtibuild. Invoke the task, then

1)
Enter the name of the text file for the file parameter.
2)
Enter the output name in the table parameter; we will use gtiset.fits
3)
Click "Run".

Filter on TIME by Explicit Reference

Finally, we could have chosen to forgo using tabgtigen altogether, and simply filtered on TIME with the standard filtering expression, seen in §7.4. In that case, the full filtering expression would be:

(PATTERN $<=$ 12)&&(PI in [200:12000])&&#XMMEA_EM&&
$   $ (TIME $<=$ 73227600)&&!(TIME IN [7.32118e7:7.3212e7])&&!(TIME IN [7.32204e7:7.32206e7])

This expression can then be used to filter the original event file, or only the times can be used to filter the file that has already had the standard filters applied. To do this, load the filtered event file mos_filt.fits in xmmselect by going to "File $\rightarrow$ New Table" at the top of the window. Then,

1)
In the "Selection Expression" box, enter (TIME $<=$ 73227600) &&!
$   $ (TIME IN [7.32118e7:7.3212e7]) &&! (TIME IN [7.32204e7:7.32206e7])
2)
Click on the "Filtered Table" box at the lower left of the xmmselect GUI.
3)
Change the evselect filteredset parameter, the output file name, to
$   $ something useful; here, we will use mos1_filt_time.fits.
4)
Click "Run".


7.7 Source Detection with edetect_chain

The edetect_chain task does nearly all the work involved with EPIC source detection. It can process up to three intruments (both MOS cameras and the PN) with up to five images in different energy bands simultaneously. All images must have identical binning and WCS keywords. For this example, we will perform source detection on MOS1 images in two bands (``soft'' X-rays with energies between 300 and 2000 eV, and ``hard'' X-rays, with energies between 2000 and 10000 eV) using the filtered event file produced in §7.6.

We will start by generating some files that edetect_chain needs: an attitude file and images of the sources in the desired energy bands, with the image binning sizes as needed according to the detector. For the MOS, we'll let the binsize be 22.

First, make the attitude file by calling atthkgen. Then,

1)
Verify that timestep is set to 1. Set the atthkset keyword to the desired output file name, for example, attitude.fits.
2)
Click ``Run''.

Next, make the soft and hard X-ray images. We'll also make an image that includes both bands, for display purposes. Call evselect, then

1)
In the ``General'' tab, set the ``Table'' parameter to the MOS1 event file name (mos1_filt_time.fits). Confirm that ``Filtertype'' is set to ``expression'', and in the ``Expression'' text area, type (FLAG == 0)&&(PI in [300:2000]).
2)
In the ``Image'' tab, check the withimageset box, and enter the desired output image name; we will use mos1-s.fits. Set xcolumn to X and ycolumn to Y. Set Binning to binSize, ximagebinsize to 22, and yimagebinsize to 22.
3)
Click ``Run''.

Follow the same procedure to make the hard X-ray image, changing the output name to mos1-h.fits and the filtering expression to (FLAG == 0)&&(PI in [2000:10000]). For our combined band image, we'll set the output name to mos1-all.fits and the filtering expression to (FLAG == 0)&&(PI in [300:10000]).

Now we can run edetect_chain. Call the task, and then

1)
In the ``0'' tab, in the imagesets text area, type: mos1-s.fits mos1-h.fits In the eventsets area, enter the names of the event file: mos1_filt_time.fits. In the attitudeset area, enter the name of the attitude file made by the task atthkgen (attitude.fits). Set the pimin keyword to the minimum PI values (in eV) for the input images by typing: 300 2000, and do similar for the maximum values for pimax (2000 10000). Set the likemin parameter to 10, witheexpmap to yes, ecf to 0.878 0.220.
2)
In the ``1'' tab, set eboxl_list to eboxlist_l.fits and eboxm_list to eboxlist_m.fits.
3)
In the ``2'' tab, set esp_withootset to no and eml_list to emllist.fits.
4)
Click ``Run''.

The energy conversion factors (ECFs) convert the source count rates into fluxes. The ECFs for each detector and energy band depend on the pattern selection and filter used during the observation. For more information, please consult the calibration paper ``SSC-LUX-TN-0059'', available at the XMM-Newton Science Operations Center or see Table 8 in the 3XMM Catalogue User Guide. Those used here are derived from PIMMS using the flux in the 0.1-10.0 keV band, a source power-law index of 1.9, an absorption of $0.5\times10^{20}$.

We can display the results of eboxdetect using the task srcdisplay and produce a region file for the sources. Call srcdisplay, then

1)
Set boxlistset to emllist.fits. Confirm that withimageset is checked, and set imageset to mos1-all.fits. Check withregionfile, and set regionfile to regionfile.txt. Confirm that sourceradius is set to 0.01.
2)
Click ``Run''.

Figure 7.5 shows the MOS1 image overlayed with the detected sources.

Figure 7.5: MOS1 count image with the detected sources.

\includegraphics[scale=0.5]{LockmanHole_0123700101_sources.eps}


7.8 Extract the Source and Background Spectra

Throughout the following, please keep in mind that some parameters are instrument-dependent. The parameter specchannelmax should be set to 11999 for the MOS, or 20479 for the PN. Also, for the PN, the most stringent filters, (FLAG==0)&&(PATTERN<=4), must be included in the expression to get a high-quality spectrum.

For the MOS, the standard filters should be appropriate for many cases, though there are some instances where tightening the selection requirements might be needed. For example, if obtaining the best-possible spectral resolution is critical to your work, and the corresponding loss of counts is not important, only the single pixel events should be selected (PATTERN==0). If your observation is of a bright source, you again might want to select only the single pixel events to mitigate pile up (see §7.9 and §7.10 for a more detailed discussion).

To extract the source spectrum, load the filtered file mos1_filt_time.fits into xmmselect if it isn't already loaded. Then,

1)
Make an image (see §7.3). It will be displayed automatically in a ds9 window.
2)
In the ``Image'' tab, enter the name fothe output file in the imageset box; we will use mos1_image.fits. Different binnings and other selections can be invoked, but the defaults are reasonable for a basic image.
3)
Click ``Run''. The image will be displayed automatically in a ds9 window.
4)
Click on the object whose spectrum you wish to extract. This will produce a circle (extraction region), centered on the object. The circle's radius can be changed by clicking on it. Adjust the size and position of the circle until you are satisfied with the extraction region. We will use the source at x=26188.5 and y=22816.
5)
Click on ``2D Region'' in the xmmselect GUI. This transfers the region information into the ``Selection Expression'' text area, for example, ((X,Y) IN circle(26188.5,22816,300)). Click the round button next the PI column on the xmmselect GUI, then click on ``OGIP Spectrum''. This will bring up the evselect GUI.
6)
In the ``General'' tab, check keepfilteroutput and withfilteredset. In the filteredset box, enter the name of the event file output. We will use mos1_filtered.fits.
7)
Select the ``Spectrum'' tab of the evslect GUI to set the file name and binning parameters for the spectrum. Confirm that withspectrumset is checked. Set spectrumset to the desired output name, in this case, mos1_pi.fits. Confirm that withspecranges is checked. Confirm that specchannelmin is 0 and specchannelmax is 11999 for the MOS, or 20479 for the PN.
8)
Click ``Run''.

The background spectrum can be extracted following the same method, setting the region to an annulus around the source: ((X,Y) in CIRCLE(26188.5,22816.5,1500))&&!((X,Y) in CIRCLE(26188.5,22816.5,500)). We will call the filtered event file bkg_filtered.fits and the output spectrum bkg_pi.fits.


7.9 Check for Pile Up

Depending on how bright the source is and what modes the EPIC detectors are in, event pile up may be a problem. Pile up occurs when a source is so bright that incoming X-rays strike two neighboring pixels or the same pixel in the CCD more than once in a read-out cycle. In such cases the energies of the two events are in effect added together to form one event. If this happens sufficiently often, 1) the spectrum will appear to be harder than it actually is, and 2) the count rate will be underestimated, since multiple events will be undercounted. To check whether pile up may be a problem, use the SAS task epatplot. Heavily piled sources will be immediately obvious, as they will have a ``hole'' in the center of their image, but pile up is not always so conspicuous. Therefore, we recommend to always check for it.

Note that this procedure requires as input the event file created when the spectrum was made, not the usual time-filtered event file.

To check for pile up, invoke epatplot. Then,

1)
In the ``0'' tab, enter the name of the event file that was made when the spectrum was extracted, mos1_filtered.fits, in the set box. We want to send the output to a ps file, so set useplotfile to yes, and enter the file name in the plotfile box. We will use mos1_epat.ps.
2)
In the ``1'' tab, set withbackgroundset to yes and enter the name of the event file that was made when the background spectrum was made, bkg_filtered.fits.
3)
Click ``Run''.

The output of epatplot is a postscript file, mos1_epat.ps, which may be viewed with viewers such as gv, containing two graphs describing the distribution of counts as a function of PI channel, as seen in Figure 7.6.

A few words about interpretting the plots are in order. The top is the distribution of counts versus PI channel for each pattern class (single, double, triple, quadruple), and the bottom is the expected pattern distribution (smooth lines) plotted over the observed distribution (histogram). The lower plot shows the model distributions for single and double events and the observed distributions. It also gives the ratio of observed-to-modeled events with 1-$\sigma$ uncertainties for single and double pattern events over a given energy range. (The default is 0.5-2.0 keV; this can be changed with the pileupnumberenergyrange parameter.) If the data is not piled up, there will be good agreement between the modeled and observed single and double event pattern distributions. Also, the observed-to-modeled fractions for both singles and doubles in the specified energy range will be unity, within errors. In contrast, if the data is piled up, there will be clear divergence between the modeled and observed pattern distributions, and the observed-to-modeled fraction for singles will be less than 1.0, and for doubles, it will be greater than 1.0.

Finally, when examining the plots, it should noted that the observed-to-modeled fractions can be inaccurate. Therefore, the agreement between the modeled and observed single and double event pattern distributions should be the main factor in determining if an observation is affected by pile up or not.

The source used in our Lockman Hole example is too faint to provide reasonable statistics for epatplot and is far from being affected by pile up. For comparison, an example of a bright source (from a different observation) which is strongly affected by pileup is shown in Figure 7.7. Note that the observed-to-model fraction for doubles is over 1.0, and there is severe divergence between the model and the observed pattern distribution.

Figure 7.6: The output of epatplot for a very faint source without pileup. Note that in the lower plot, there are too few X-rays for epatplot to model.

\includegraphics[scale=0.5]{LockmanHole_0123700101_faint_epat.eps}

Figure 7.7: The output of epatplot for a heavily piled source. In the lower plot, there are large differences between the predicted and observed pattern distribution at energies above $\sim $ 1000 eV.

\includegraphics[scale=0.5]{LockmanHole_0123700101_bright_epat.eps}


7.10 My Observation is Piled Up! Now What?

If you are working with a different (much brighter) dataset that does show signs of pie up, there are a few ways to deal with it. First, using the region selection and event file filtering procedures demonstrated in earlier sections, you can excise the inner-most regions of a source (as they are the most heavily piled up), re-extract the spectrum, and continue your analysis on the excised event file. For this procedure, it is recommended that you take an iterative approach: remove an inner region, extract a spectrum, check with epatplot, and repeat, each time removing a slightly larger region, until the model and observed distribution functions agree. If you do this, be aware that removing too small a region with respect to the instrumental pixel size (1.1'' for the MOS, 4.1'' for the PN) can introduce systematic inaccuracies when calculating the source flux; these are less than 4%, and decrease to less than 1% when the excised region is more than 5 times the instrumental pixel half-size. In any case, be certain that the excised region is larger than the instrumental pixel size!

You can also use the event file filtering procedures to include only those events with PATTERN==0, as these events are less sensitive to pile up than other patterns.


7.11 Determine the Spectrum Extraction Areas

Now that we are confident that our spectrum is not piled up, we can continue by finding the source and background region areas. This is done with the task backscale, which takes into account any bad pixels or chip gaps, and writes the result into the BACKSCAL keyword of the spectrum table. Alternatively, we can skip running backscale, and use a keyword in arfgen below. We will show both options for the curious.

To find the source extraction area explicitly, call backscale and then

1)
In the ``Main'' tab, enter the name of the spectrum, mos1_pi.fits.
2)
In the ``Effects'' tab, confirm that withbadpixcorr is checked, and enter the name of the event file in badpixlocation.
3)
Click ``Run''.

Follow the same steps to find the background spectrum area, changing the input spectrum file to bkg_pi.fits.


7.12 Create the Photon Redistribution Matrix (RMF) and Ancillary File (ARF)

The following assumes that an appropriate source spectrum, named mos1_pi.fits, has been extracted as in §7.8.

To make the RMF,

1)
Invoke the task rmfgen in the SAS GUI.
2)
In the ``Main'' tab, set the spectrumset keyword to the spectrum file name, e.g., mos1_pi.fits. Set the rmfset keyword to the RMF file name, e.g., mos1_rmf.fits.
3)
Click ``Run''.

To make the ARF,

1)
Invoke the task arfgen in the SAS GUI.
2)
In the ``Main'' tab, set the arfset parameter to the ARF file name, for example, mos1_arf.fits. Set the spectrumset parameter to the spectrum file name, in this case, mos1_pi.fits.
3)
In the ``Effects'' tab, confirm that the withbadpixcorr box is checked. Set the badpixlocation keyword to the event file name from which the spectrum was extracted, in this case, mos1_filt_time.fits.
4)
In the ``Calibration'' tab, check the withrmfset box and set the rmfset keyword to the RMF file name, in this case, mos1_rmf.fits.
5)
Click ``Run''.

At this point, the spectrum is ready to be analyzed, so skip ahead to prepare the spectrum for fitting §13.


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Lynne Valencic 2015-10-30