7. An EPIC Data Processing and Analysis Primer

While a variety of analysis packages can be used for the following steps, the SAS was designed for the basic reduction and analysis of XMM-Newton data (extraction of spatial, spectral, and temporal data); therefore, it will be used here for demonstration purposes.

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.

At this point, it is assumed that you have downloaded the data from the HEASARC archive onto a Hera server, standard or anonymous Hera is running (see §4.2), and you have prepared the data for processing (see §6). Throughout this chapter, as in §6, we will use the Lockman Hole dataset with ObsID 0123700101, though any dataset will suffice.

7.1 Rerun the Pipeline

If a dataset is more than a year old, it was probably processed with older versions of CCF and SAS prior to archiving, so the pipeline should be rerun to generate event files with the latest calibrations. The MOS has two pipeline tasks, emchain and emproc, while the PN has one, epproc for the PN. The two MOS tasks produce the same output, so which one to use is entirely a matter of the user's personal preference.

Verify that the working directory PROC is highlighted, then:

1)

In the Available Tools panel, under ``XMM-SAS'', call emproc or emchain to repipeline the MOS data, and epproc to repipeline the PN data. Note that for emchain, the default is to process both the MOS1 and MOS2 cameras; users who want to analyze only one camera will need to change the ``Instrument to analyze'' parameter.

2)

In the task pop-up window, click ``Run''.

By default, none of these tasks keep any intermediate files they generate. Emchain maintains the naming convention described in §5.3.3. Emproc and epproc designate their output event files with ``Evts.ds''; ``*ImagingEvts.ds'', ``*TimingEvts.ds'', and ``*BurstEvts.ds'' denote the imaging mode, timing, and burst mode event lists, respectively. In either case, you may want to name the new files something easy to type. Right-clicking on a file will give you the option to rename it.

Once the new event files have been obtained, the analysis techniques described below can be used. We will refer to the new event files as mos1.fits, mos2.fits, and pn.fits.

7.2 Examine and Analyze Data

The heraXmmselect GUI, in the ``experimental'' folder of ``XMM-SAS'', 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.

An event list from the MOS1 can be loaded by highlighting the event file mos1.fits in the PROC directory, then double-clicking on heraXmmselect. A Table Selection window will pop up to confirm your selection; clicking ``Go'' will start the task.

7.2.1 Create and Display an Image

To create an image in sky coordinates with heraXmmselect,

1)

Check the square boxes to the left of the ``X'' and ``Y'' entries.

2)

Click on the ``Image'' button near the bottom of the page. This brings up the evselect GUI (see Figure 7.1).

3)

In the imageset box (under the Image tab), enter the name of the output file, in this case, image.fits.

4)

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 written to the file image.fits, and is automatically displayed with POW. It can also be downloaded to your local machine and viewed with ds9; see Figure 7.2.

Figure 7.1: The evselect GUI.

Figure 7.2: The MOS1 image, displayed in fv.

7.2.2 Create and Display a Light Curve

To create a light curve with heraXmmselect,

1)

Check the circle 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 7.1).

3)

Click on the ``Lightcurve'' tab and change the ``timebinsize'' to a reasonable amount, e.g. 10 or 100 s. Change the name of the output file in the ``rateset'' box to, say, mos1_ltcrv.fits.

4)

Click on the ``Run'' button at the lower left corner of the evselect GUI.

The resultant light curve is written to the file mos1_ltcrv.fits and is automatically displayed in POW; see Fig. 7.3.

Figure 7.3: The light curve.

7.2.3 Applying Standard Filters the Data

Whether using the GUI or the command line, the needed filtering ``expressions'' for the MOS and PN are, respectively:

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

and

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

If the PN data is timed, then the PATTERN parameter should be set to 4:

(PATTERN $<=$ 4)&&(PI in [200:15000])&&#XMMEA_EP.

The first two expressions will select good events with PATTERN in the 0 to 12 range, and the last will select events with PATTERN between 0 and 4. 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.

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 pipelined Lockman Hole data for the MOS1 is 44Mb; the quality-filtered list is 29Mb, and when the temporal filter is applied, the final ``clean'' event list is only 7Mb!

To filter the data with heraXmmselect,

1)

Enter the filtering criteria in the ``Selection Expression'' area at the top of the heraXmmselect GUI:

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

2)

Click on the ``Filtered Table'' box at the lower left of the heraXmmselect GUI.

3)

In the General tab, change the filteredset parameter, the output file name, to something useful, e.g., mos1_filt.fits

4)

Click ``Run''.

7.2.4 Applying Time Filters the Data

Sometimes, it is necessary to filter 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.

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.

There are two ways to filter on time: with an explicit reference to the TIME or RATE parameters in the filtering expression, or by creating a secondary Good Time Interval (GTI) file with the task tabgtigen. Both procedures are described below. For the example data, we will filter by time, though you can just as easily filter by rate.

To explicitly define the TIME or RATE parameters, make a light curve and display it, as demonstrated in §7.2.2 and plotted in Figure 7.3. There is a very large flare toward the end of the observation, so the syntax for the time selection is (TIME $<=$ 7.32273e7). However, there is also a small flare within an otherwise good interval. A slightly more comlicated expression to remove it would be: (TIME $<=$ 7.32273e7)&&!(TIME IN [7.32219e7:7.32238e7]). The syntax &&(TIME $<$ 7.32273e7) includes only events with times less than 7.32273e7, and the ``!'' symbol stands for the logical ``not'', so use &&!(TIME in [7.32219e7:7.32238e7]) to exclude events in the time interval 7.32219e7 to 7.32238e7.

If combined with the standard filtering expression (see §7.2.3), the full filtering expression would then be:

(PATTERN $<=$ 12)&&(PI in [200:12000])&&#XMMEA_EM
$$ &&(TIME $<=$ 7.32273e7) &&!(TIME in [7.32219e7:7.32238e7])

This expression can then be used to filter the original event file, as shown in §7.2.3, or only the times can be used to filter the file that has already had the standard filters applied.

To filter on time using a secondary GTI file, make the file by using the same time filtering parameters as determined above and the tabgtigen task:

1)

Call tabgtigen from the Available Tools panel and enter the light curve, mos1_ltcrv.fits as the input data set. (There is no need to quit heraXmmselect.)

2)

Edit the name of table to hold resulting GTI list; here, we will use gtiset.fits.

3)

In the text box directly below (``Boolean expression controlling the GTI creation''), enter the filtering expression, (TIME $<$ 7.32273e7) &&! (TIME in [7.32219e7:7.32238e7])

4)

Click ``Run'' in the lower left corner.

Now, apply the new GTI file with heraXmmselect:

1)

In heraXmmselect, load the filtered event file by going to File $\rightarrow$ New Table and entering mos1_filt.fits.

2)

In the ``Selection Expression'' box, type GTI(gtiset.fits,TIME).

3)

Click on the ``Filtered Table'' box at the lower left of the heraXmmselect GUI.

4)

Change the filteredset parameter, the output file name, to something useful; here, we will use mos1_filt_time.fits.

5)

Click ``Run''.

7.3 Source Detection

The task edetect_chain is a metatask that does nearly all the work involved with EPIC source detection. It can take as input arbitrary combinations of images from different energy bands and different EPIC instruments, with up to three instruments and 5 energy bands. However, users should be aware that that the binning and WCS keywords in all images must be identical. Edetect_chain is comprised of seven straightforward tasks that can also be run by hand. Edetect_chain requires input files to be generated and prepared using the tasks atthkgen and evselect; the task emosaic, while not necessary for source detection, does provide a nice mosaicked image for display purposes. Fortunately, these are all quick and straightforward.

In the example below, source detection is done on images in bands, 500 - 1000 eV and 4500 - 12000 eV, which correspond to bands 2 and 5 of the 3XMM Catalogue, for all three detectors. The source count rates are converted into fluxes through the energy converstion factors (ECFs) for each detector and energy band. The ECFs depend on the pattern selection and filter used during the observation and are given in units of $10^{11}$ cts cm$^{2}$/erg. Interested users can find all of the bands listed in Table 1 of the Catalogue. The ECFs are in Table 8 of the Catalogue.)

The example uses the filtered event files from all three cameras, which can be produced in §7.2.4.

First, make the attitude file:

1)

Call atthkgen in the Available Tools panel.

2)

Verify the name of the working directory, and enter the desired output file name, for example, attitude.fits.

3)

Click ``Run''.

Next, make the band 2 and band 5 images with evselect. We'll start with the band 2 image in the MOS1.

1)

Select the event file (mos1_filt_time.fits) and run heraXmmselect.

2)

In the ``Selection Expression'' box, enter our filtering parameters: (FLAG == 0)&&(PI in [500:1000]). Check the X and Y boxes, and click ``Image''.

3)

In the ``Image'' tab, check the withimageset box, and enter the desired output image name, for example, mos1-b2.fits. Set xcolumn to X and ycolumn to Y. Set binning to binSize, ximagebinsize and yimagebinsize to 82.

4)

Click ``Run''.

For band 5, just change the filtering expression to (FLAG == 0)&&(PI in [4500:12000]). We will refer to the output as mos1-b5fits.

The procedure is similar for the MOS2 and PN. Note that, because we are using these images for the specific purpose of source detection, the image binning that we would normally use for MOS (22, corresponding to 1.1 arcsec) must be adjusted to match that of PN (82, corresponding to 4.1 arcsec). We will assume the output images are named mos1-b2.fits, mos2-b2.fits, pn-b2.fits, mos1-b5.fits, mos2-b5.fits, and pn-b5.fits.

Now we can run edetect_chain:

1)

Call the task edetect_chain. Enter the list of images: mos1-b2.fits mos1-b5.fits mos2-b2.fits mos2-b5.fits pn-b2.fits pn-b5.fits Directly below, enter the names of the event files: mos1_filt_time.fits mos2_filt_time.fits pn_filt_time.fits. Enter the name of the attitude file made by the task atthkgen, attitude.fits. Set the lower PI values (in eV) for the input images: 500 4500 500 4500 500 4500, and do the same for the higher values: 1000 12000 1000 12000 1000 12000). The energy conversion factors (ECFs) for this example are 1.70 0.15 1.71 0.15 7.87 0.58. Set the flag to use out-of-time events in esplinemap to no.

2)

Click ``Run''.

7.3.1 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 §7.3.3 and §7.3.4 for a more detailed discussion).

First, make an image of the filtered file, mos1_filt_time.fits, as described in §7.2.1). Download it and display it with ds9, then click on an object whose spectrum you wish to extract. Adjust the extraction region until you are satisfied with it. For this example, we will choose the source at (26165.75, 22816.25) and set the extraction radius to 400.

To extract the source spectrum,

1)

Open mos1_filt_time.fits in heraXmmselect.

2)

In the ``Selection Expression'' box, enter ((X,Y) in CIRCLE(26165.75,22816.2,400)) && (FLAG==0)

3)

Click the circle next the PI column.

4)

Click on ``OGIP Spectrum''.

5)

In the ``General'' page, check keepfilteroutput and withfilteredset. In the filteredset box, enter the name of the event file output, in this case, mos1_filt_time_source.fits.

6)

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_source_pi.fits. Confirm that withspecranges is checked. Set specchannelmin to 0. Set specchannelmax to 11999 for the MOS, or 20479 for the PN.

7)

Click ``Run''.

A plot of the counts per channel will be displayed automatically in fv. (The flux per energy cannot be calculated until the response matrix and ancillary files are made, see §7.3.5.)

When extracting the background spectrum, follow the same procedures, but change the extraction area. For example, make an annulus around the source; this can be done using two circles, each defining the inner and outer edges of the annulus, then change the filtering expression (and output file name) as necessary. For our example, we will set the filtering expression to ((X,Y) in CIRCLE(26165.75,22816.2,1200))&&!((X,Y) in CIRCLE(26165.75,22816.2,400)) && (FLAG==0). The event file output will be mos1_filt_time_bkg.fits and the spectrum file will be mos1_bkg_pi.fits

7.3.2 Determine the Spectrum Extraction Areas

The source and background region areas can now be found. 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.

To find the source extraction areas,

1)

Call backscale in the Available Tools panel and enter the spectrum, mos1_source_pi.fits as the input data set.

2)

Directly beneath, enter the file containing the bad pixel extensions, i.e., the event file from which it was extracted, mos1_filt_time.fits. Confirm that the option to correct for bad pixels in the source box is set to ``yes''.

3)

Click ``Run''.

The procedure is the same for the background extraction area.

7.3.3 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.) Note that this procedure requires as input the event files created when the spectrum was made.

The output of epatplot is a postscript file, which may be viewed with viewers such as gv, containing two graphs describing the distribution of counts as a function of PI channel; see Figure 7.4.

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). If the lower plot shows the model distributions for single and double events diverging significantly from the observed distributions, then the source is piled up.

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. In contrast, plots from two different observations are shown in Figure 7.4 and 7.5. In Figure 7.4, the source is bright enough to provide statistics (and a good fit) at energies above about 1.5 keV. Figure 7.5 shows a plot of a very bright source which is strongly affected by pileup. Note the severe divergence between the model and the observed pattern distribution.

To check for pile up,

1)

Highlight the name of the filtered event list that was made when the spectrum was extracted, mos1_filt_time_source.fits. In the the Available Tools window, call epatplot.

2)

In the epatplot window, confirm that the event file is in the ``Name of input events file'' text box. Next to ``Whether to determine background subtracted pattern fractions'', click ``yes'', and in the text box just above, enter the name of the background spectrum event list (mos1_filt_time_bkg.fits). Enter the name of the output file; for this example, we will use mos1_pat.ps. Directly below, click ``yes'' next to ``Use plotfile name from parameter''.

3)

Click ``Run''.

The postscript file can be copied to the user's local machine and viewed there.

Figure 7.4: The output of epatplot for a very faint source without pileup. Note that in the lower plot, for energies less than $\sim$ 1500 eV, there are too few X-rays for epatplot to model.

Figure 7.5: 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.

7.3.4 My Observation is Piled Up! Now What?

There are a few ways to deal with pile up. 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.

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

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

In order to do spectral analysis, it is necessary to find the instrument's repsonse as a function of energy and PI channel. This is done by reformating the detector response and energy bounds information and correcting for instrumental effects, and writing the result to the Redistribution Matrix File (RMF). The following assumes that an appropriate source spectrum, named mos1_source_pi.fits, has been extracted as in §7.3.1.

To make the RMF,

1)

Call rmfgen from the Available Tools panel.

2)

Enter the name of the output dataset, mos1_rmf.fits. In the ``Counts spectrum file'' text box, enter the spectrum file name, mos1_source_pi.fits.

3)

Click ``Run''.

Now use the RMF, spectrum, and event file to make the ancillary file.

To make the ARF,

1)

Call arfgen in the Available Tools panel.

2)

Enter the name of the input spectrum, mos1_source_pi.fits, the input response set, mos1_rmf.fits, and the output ancillary response set, mos1_arf.fits.

3)

Confirm that ``Correct for bad pixels in source box'' is set to ``yes'', and next to ``File containing bad pixel extensions'', enter the name of the event file from which the spectrum was extracted, mos1_filt_time.fits. Next to ``Get ebins from response set'', click ``yes''.

4)

Click on ``Run''.

At this point, the spectrum is ready to be analyzed, so we can prepare the spectrum for fitting.

7.3.6 Prepare the Spectrum

With the source and background spectra now extracted and the RMF and ARF created, we will do some simple spectral fitting. SAS does not include fitting software, so HEASoft packages will be used, and all fitting tasks will be called from the Command Window.

Nearly all spectra will need to be binned for statistical purposes. The procedure grppha, located in the HEASARC folder in the Available Tools window, provides an excellent mechanism to do just that. While some users may prefer the Hera GUI, it is often easier and more useful to simply enter the commands in the Command Window, especially as only one command may be entered at a time using the GUI interface. (With the GUI, the commands are entered in the two ``GRPPHA'' text boxes, and the last command entered must be exit to avoid a hung session.) In light of this, only instructions for the Command Window are shown here.

The following commands not only group the source spectrum for Xspec but also associate the appropriate background and response files for the source.

In the Command Window, type

grppha

and edit the parameters and file names as appropriate:

      Please enter PHA filename[] mos1_source_pi.fits ! input spectrum file name
      Please enter output filename[] mos1_grp.fits    ! output grouped spectrum
      GRPPHA[] chkey BACKFILE mos1_bkg_pi.fits        ! include the background spectrum
      GRPPHA[] chkey RESPFILE mos1_rmf.fits           ! include the RMF
      GRPPHA[] chkey ANCRFILE mos1_arf.fits           ! include the ARF
      GRPPHA[] group min 25                           ! group the data by 25 counts/bin
      GRPPHA[] exit

Upon exiting, the output file mos1_grp.fits will appear in your working directory. Next, use Xspec to fit the spectrum by typing:

xspec

A POW window will pop up and display the spectrum later on. Edit the parameters and file names as appropriate:

      XSPEC> data mos1_grp.fits      ! input data
      XSPEC> ignore 0.0-0.2,6.6-**   ! ignore unusable energy ranges, in keV
                                     ! set a range appropriate for the data
      XSPEC> model wabs(pow+pow)     ! set spectral model to two absorbed power laws
      1:wabs:nH> 0.01                ! set model absorption column density to 1.e20
      2:powerlaw:PhoIndex> 2.0       ! set the first model power law index to -2.0
      3:powerlaw:norm>               ! use the default model normalization
      4:powerlaw:PhoIndex> 1.0       ! set the second model power law index to -1.0
      5:powerlaw:norm>               ! use the default model normalization
      renorm                         ! renormalize the model spectrum
      XSPEC> fit                     ! fit the model to the data
      XSPEC> setplot energy          ! plot energy along the X axis
      XSPEC> plot ldata ratio        ! plot two panels with the log of the data and
                                     !     the data/model ratio values along the Y axes
      XSPEC> exit                    ! exit Xspec

Figure 7.6 shows the fit to the spectrum.

Figure 7.6: The fitted spectrum of the Lockman Hole source.

7.4 Timing Analysis

This section will demonstrate some basic timing analysis of EPIC image-mode data using the Xronos analysis package. For this exercise, the central source from the observation of G21.5-09 (Obs ID 0122700101) is used. These examples assume that the source's lightcurve has been made as in § 7.2.2, but with timebinsize set to 1 and makeratecolumn set to no; the name of this file assumed to be source_ltcrv.fits.

For the aficionado, the task barycen can be used for the barycentric correction of the source event arrival times.

The Xronos tools can be access through the Hera GUI by clicking on XRONOS in the Available Tools panel.

Figure 7.7: Light curve for the source analyzed as in § 7.2.2.

Figure 7.8: Power spectrum density for the source.

To make a binned lightcurve with the Xronos GUI:

1)

Call lcurve from the Available Tools panel.

2)

Enter the Series 1 filename, in this case source_ltcrv.fits. Set ``Name of the window file'' to '-' (no quotes), ``Newbin Time or negative rebinning'' to 500, and the ``Number of Newbins/Interval'' to 430. Enter the name of the output file; for this example we will call it lightcurve_binned.fits. Next to ``Do you want to plot your results?'', select ``No''.

3)

Click ``Run''.

The output can be viewed with fv by right-clicking on the filename and selecting the ``Edit/Display File'' option. Output is shown in Figure 7.7.

To make a binned lightcurve with Xronos in the Command Window:

lcurve nser=1 cfile1='source_ltcrv.fits' window=- dtnb=500 nbint=450
$$ outfile='lightcurve_binned.fits' plot=no

where

nser - number of time series
cfile1 - filename first series
window - name of window file (if a subset of the time series is required)
dtnb - bin size (time)
nbint - number of bins per interval
outfile - output file name (FITS format light curve)
plot - plot flag

The output can be viewed with fv by right-clicking on the filename and selecting the ``Edit/Display File'' option. Output is shown in Figure 7.7.

To calculate power spectrum density with the Xronos GUI:

1)

Call powspec from the Available Tools panel.

2)

Enter the Series 1 filename, in this case source_ltcrv.fits. Set ``Name of the window file'' to '-' (no quotes); next to ``Newbin Time or negative rebinning'', enter 100; for the number of newbins/interval, enter 300; for the number of intervals/frame, enter INDEF. Next to ``Rebin results?'', enter 5; next to ``Do you want to plot your results?'', select ``No''. Enter the name of the output file, in this case, power.fits.

3)

Click ``Run''.

The output can be viewed with fv by right-clicking on the filename and selecting the ``Edit/Display File'' option. Output is shown in Figure 7.8.

To calculate power spectrum density with Xronos in the Command Window, type:

powspec cfile1='source_ltcrv.fits' window=- dtnb=100.0 nbint=300
$$ nintfm=INDEF rebin=5 plot=no outfile='power.fits'

where

cfile1 - filename first series
window - name of window file (if a subset of the time series is required)
dtnb - bin size (time)
nbint - number of bins per interval
nintfm - number of intervals in each power spectrum
rebin - rebin factor for power spectrum (0 for no rebinning)
plot - plot flag
outfile - output file name (FITS format power spectrum)

The output can be viewed with fv by right-clicking on the filename and selecting the ``Edit/Display File'' option. Output is shown in Figure 7.8.

To search for periodicities in the time series with the Xronos GUI:

1)

Call efsearch from the Available Tools panel.

2)

Enter the Series 1 filename, in this case source_ltcrv.fits. Set ``Name of the window file'' to '-' (no quotes); next to ``Epoch'', enter INDEF. Set ``Period'' to 20, ``Phasebins/period'' to 10, ``Number of Newbins/Interval'' to INDEF, ``Number of periods to search'' to 100, and ``Resolution for period search'' to INDEF. Next to ``Do you want to plot your results?'', select ``No''. Enter the name of the output file, in this case, efsearch.fits.

3)

Click ``Run''.

To search for periodicities in the time series with Xronos in the Command Window, type:

efsearch cfile1=source_ltcrv.fits window=- sepoch=INDEF dper=20 nphase=10
$$ nbint=INDEF nper=100 dres=INDEF plot=no outfile=autocor.fits

where

cfile1 - filename first series
window - name of window file (if a subset of the time series is required)
sepoch - value for epoch used for phase zero when folding the time series
dper - value for the period used in the folding
nphase - number of phases per period
nbint - number of bins per interval
nper - number of sampled periods during search
dres - sampling resolution of search
plot - plot flag
outfile - output file name (FITS format)

To calculate the autocorrelation for a time series with the Xronos GUI:

1)

Call autocor from the Available Tools panel.

2)

Enter the Series 1 filename, in this case source_ltcrv.fits. Set ``Name of the window file'' to '-' (no quotes). Set ``Newbin Time or negative rebinning'' to 24, ``Number of Newbins/Interval'' to 2048; ``Number of Intervals/Frame'' to INDEF; and ``Rebin results?'' to 0. Next to ``Do you want to plot your results?'', select ``No''. Enter the name of the output file, in this case, autocor.fits.

3)

Click ``Run''.

To calculate the autocorrelation for a time series with Xronos in the Command Window, type:

autocor cfile1=source_ltcrv.fits window=- dtnb=24.0 nbint=2048 nintfm=INDEF
$$ rebin=0 plot=no outfile=auto.fits

where

cfile1 - filename first series
window - name of window file (if a subset of the time series is required)
dtnb - bin size (time)
nbint - number of bins per interval
nintfm - number of intervals to be summed in each autocorrelation function
rebin - rebin factor for autocorrelation function (0 for no rebinning)
plot - plot flag
outfile - output file name (FITS format autocorrelation spectrum)

To calculate statistical quantities for a time series with the Xronos GUI:

1)

Call lcstats from the Available Tools panel.

2)

Enter the Series 1 filename, in this case source_ltcrv.fits. Set ``Name of the window file'' to '-' (no quotes). Set ``Newbin Time or negative rebinning'' to 6, and ``Number of Newbins/Interval'' to 8192.

3)

Click ``Run''.

The statistics will be printed in the Command Window.

To calculate statistical quantities for a time series with Xronos in the Command Window, type:

write the output to an ASCII file fname. (Leave off the $>$ fname to write the results to the screen.)

lcstats cfile1=source_ltcrv.fits window=- dtnb=6.0 nbint=8192

where

cfile1 - filename first series
window - name of window file
dtnb - integration time (binning)
nbint - number of bins
fname - output file name