Measuring Line Parameters with an HETG/ACIS-S Spectrum
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Sherpa Threads (CIAO 4.4 Sherpa v1)
Overview
Synopsis:
After having created or downloaded a set of PHA2 and response files (RMFs and ARFs), for an HETG/ACIS-S observation, perform a simple fit to one of the line features present in the spectrum. This thread takes the user through a calculation of the error bars on the line normalizations, positions, and widths, and shows how to calculate the line equivalent widths.
For those wishing to perform their analysis with XSPEC, the PHA2 file must first be split into individual type 1 PHA files, and then grouped to increase the signal-to-noise in each spectral bin. The Grouping a Grating Spectrum CIAO thread shows how to do this, and the procedure is also included within this thread.
Run this thread if:
You are working with an HETG/ACIS-S data set, and want to begin a simple analysis of the data.
Related Links:
Last Update: 22 Jan 2012 - reviewed for CIAO 4.4 (no changes)
Contents
- Data Preparation
- Load the Spectrum and Responses
- Group and Filter the Data
- Defining the Source Models
- Fitting
- Adding Lines to the Model and Fitting Again
- Examine the Fit Results
- Calculate the Equivalent Width of a Line and Continuum Flux
- Scripting It
- Summary
- History
- Images
Data Preparation
This thread makes use of archival data products for Vela X-1, downloaded from TGcat. They were obtained by performing a "Search by Name" on Vela X-1 (using the SIMBAD name resolver), then choosing "view -> file table" for the target, and clicking the download arrow next to each filename. These files were unzipped and placed in the directory in which the data analysis is to be performed.
Note that we are not considering any background files. For HETG/ACIS-S observations the background is suppressed by the process of order sorting. That is, an event at a given location along the grating arms must fall into a narrow range of energies (as determined by the CCD) in order for it to be considered a valid dispersed photon. Most background events do not meet these "order sorting" criteria. For this and many other HETG/ACIS-S observations, the first simple analysis can be performed without reference to the background. (Detailed analysis of low S/N spectra likely would require proper consideration of the background. See the Fitting Grating Data thread for an example of analysis including background data).
Those using either Sherpa or ISIS to perform their analysis can use the data files directly in the analysis; skip to the "Load the Spectrum and Responses" section.
XSPEC users, however, must split and group the spectra before the analysis as described here.
Load the Spectrum and Responses
Start a Sherpa session:
unix% sherpa ----------------------------------------------------- Welcome to Sherpa: CXC's Modeling and Fitting Package ----------------------------------------------------- CIAO 4.4 Sherpa version 1 Friday, December 2, 2011 sherpa>
We begin by reading the PHA2 file that contains the grating spectra. This file contains 12 spectra, 3 spectral orders each for both positive and negative order HEG and MEG spectra. They are stored in the order: HEG -3, -2, -1, 1, 2, 3; MEG -3, -2, -1, 1, 2, 3. By default, Sherpa will read in all 12 spectral files in this order.
We are only interested, however, in the first order spectra (positive and negative), therefore we use a Data Model-type filter to select the third and fourth, ninth, and tenth rows of the PHA2 file (HEG -/+1 and MEG -/+1, respectively).
sherpa> load_pha("pha2[#row=3,4,9,10]") WARNING: systematic errors were not found in file 'pha2[#row=3,4,9,10]' statistical errors were found in file 'pha2[#row=3,4,9,10]' but not used; to use them, re-read with use_errors=True read background_up into a dataset from file pha2[#row=3,4,9,10] read background_down into a dataset from file pha2[#row=3,4,9,10] Multiple data sets have been input: 1-4
Alternatively, one could have used a filter to select all negative and postitive first order spectra via the TG_M keyword:
sherpa> load_pha("pha2[TG_M=-1,1]")
Note that associated background spectra (two each for each spectrum, one from either side of the gratings arm) will be read in with the source spectra. However, unless the subtract command is called, or unless one creates a background model, they will not be part of the fitting process. For the remainder of this thread, we ignore these background spectra.
We now read in the associated ARF files and assign them to their corresponding data sets, then do the same for the RMF files.
sherpa> load_arf(1,"heg_-1.arf") sherpa> load_arf(2,"heg_1.arf") sherpa> load_arf(3,"meg_-1.arf") sherpa> load_arf(4,"meg_1.arf") sherpa> load_rmf(1,"heg_-1.rmf") sherpa> load_rmf(2,"heg_1.rmf") sherpa> load_rmf(3,"meg_-1.rmf") sherpa> load_rmf(4,"meg_1.rmf")
Group and Filter the Data
Sherpa allows one to bin the spectra during the analysis session; see the Sherpra thread Changing the grouping scheme of a data set within Sherpa for details. We bin each of the spectra to have a minimum of 20 counts per energy channel.
sherpa> group_counts(1,20) WARNING: grouping flags have changed, noticing all bins sherpa> group_counts(2,20) WARNING: grouping flags have changed, noticing all bins sherpa> group_counts(3,20) WARNING: grouping flags have changed, noticing all bins sherpa> group_counts(4,20) WARNING: grouping flags have changed, noticing all bins
For the purposes of this analysis, we are interested in fitting the line near 6.4 keV. We restrict the energy range to 5-7 keV for all four spectra using the notice command, since the same energy filter is to be applied to all data set IDs; the notice_id command is available for filtering data sets individually.
sherpa> notice(5.,7.) sherpa> show_filter() Data Set Filter: 1 5.0096-6.9904 Energy (keV) Data Set Filter: 2 4.9721-6.9910 Energy (keV) Data Set Filter: 3 4.9999-9.5411 Energy (keV) Data Set Filter: 4 5.0154-9.5411 Energy (keV)
The spectra can all be plotted in the same figure using the plot command. We select all four plots to be considered "current" so that changing the axis will be applied to all of them, and then we choose the y-axes to be logarithmic.
sherpa> plot("data",1,"data",2,"data",3,"data",4) sherpa> current_plot("all") sherpa> log_scale(Y_AXIS) sherpa> print_window("all",["format","eps"])
The plot, shown in Figure 1, is also saved in EPS format to the file "all.eps".
![[Thumbnail image: The HEG and MEG spectra are plotted one above the other.]](1.png)
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Figure 1: Plot of the +1 and -1 HEG and MEG spectra
The HEG (upper) and MEG (lower) plots with -1 order on the left and and the +1 order on the right. The plots are displayed with a logarithmic scale and have been filtered to the range 5.0-7.0 keV. Note that the MEG data includes a data point above 9 keV, as the lower bound of this energy bin falls below the 7 keV cutoff of the notice/notice_id command.
The MEG spectra do not have very good statistics in the region of interest; therefore, we delete these data sets from the current session before proceeding with the fits.
sherpa> delete_data(3) sherpa> delete_data(4)
The data sets currently being considered in the analysis can be viewed with the show_data, show_bkg, and show_all commands:
sherpa> show_data() Data Set: 1 Filter: 5.0096-6.9904 Energy (keV) Noticed Channels: 7597-7888 name = pha2[#row=3,4,9,10] channel = Float64[8192] counts = Float64[8192] staterror = None syserror = None bin_lo = Float64[8192] bin_hi = Float64[8192] grouping = Float64[8192] quality = Float64[8192] exposure = 83150.1366728 backscal = 1.0 areascal = 1.0 grouped = True subtracted = False units = energy rate = True plot_fac = 0 response_ids = [1] background_ids = [1, 2] RMF Data Set: 1:1 name = heg_-1.rmf detchans = 8192 energ_lo = Float64[8192] energ_hi = Float64[8192] n_grp = Int16[8192] f_chan = UInt32[8192] n_chan = UInt32[8192] matrix = Float64[841124] offset = 1 e_min = Float64[8192] e_max = Float64[8192] ARF Data Set: 1:1 name = heg_-1.arf energ_lo = Float64[8192] energ_hi = Float64[8192] specresp = Float64[8192] bin_lo = Float64[8192] bin_hi = Float64[8192] exposure = 83149.4231809 Data Set: 2 Filter: 4.9721-6.9910 Energy (keV) Noticed Channels: 7588-7891 name = pha2[#row=3,4,9,10] channel = Float64[8192] counts = Float64[8192] staterror = None syserror = None bin_lo = Float64[8192] bin_hi = Float64[8192] grouping = Float64[8192] quality = Float64[8192] exposure = 83150.1366728 backscal = 1.0 areascal = 1.0 grouped = True subtracted = False units = energy rate = True plot_fac = 0 response_ids = [1] background_ids = [1, 2] RMF Data Set: 2:1 name = heg_1.rmf detchans = 8192 energ_lo = Float64[8192] energ_hi = Float64[8192] n_grp = Int16[8192] f_chan = UInt32[8192] n_chan = UInt32[8192] matrix = Float64[841124] offset = 1 e_min = Float64[8192] e_max = Float64[8192] ARF Data Set: 2:1 name = heg_1.arf energ_lo = Float64[8192] energ_hi = Float64[8192] specresp = Float64[8192] bin_lo = Float64[8192] bin_hi = Float64[8192] exposure = 83150.6981615
Defining the Source Models
We will first fit a broad continuum to the spectra, without any lines. The XSPEC powerlaw model (xspowerlaw) is chosen for the continuum. It is assigned as the source for data set 1 and given the model name "powr"; the model name is then used to assign the same source model to data set 2.
sherpa> set_source(1,xspowerlaw.powr) sherpa> set_source(2,powr) sherpa> show_source() Model: 1 xspowerlaw.powr Param Type Value Min Max Units ----- ---- ----- --- --- ----- powr.phoindex thawed 1 -2 9 powr.norm thawed 1 0 1e+24 Model: 2 xspowerlaw.powr Param Type Value Min Max Units ----- ---- ----- --- --- ----- powr.phoindex thawed 1 -2 9 powr.norm thawed 1 0 1e+24
Fitting
The fit statistic is set to be Χ2 with the data counts acting as the variance (i.e., a Gaussian approximation to simple Poisson statistics). Both data sets are fit simultaneously.
sherpa> set_stat("chi2datavar") sherpa> fit() WARNING: data set 1 has associated backgrounds, but they have not been subtracted, nor have background models been set WARNING: data set 2 has associated backgrounds, but they have not been subtracted, nor have background models been set Data Sets = 1, 2 Method = levmar Statistic = chi2datavar Initial fit statistic = 5.98398e+08 Final fit statistic = 321.803 at function evaluation 77 Data points = 56 Degrees of freedom = 54 Probability [Q-value] = 9.16795e-40 Reduced statistic = 5.95931 Change in statistic = 5.98397e+08 powr.phoindex -1.36276 powr.norm 1.98057e-05
At any time, the results of the last fit can be displayed using the show_fit command. The fit yields the following results:
sherpa> show_fit() Optimization Method: LevMar name = levmar ftol = 1.19209289551e-07 xtol = 1.19209289551e-07 gtol = 1.19209289551e-07 maxfev = None epsfcn = 1.19209289551e-07 factor = 100.0 verbose = 0 Statistic: Chi2DataVar Chi Squared with data variance Fit:Data Sets = 1, 2 Method = levmar Statistic = chi2datavar Initial fit statistic = 5.98398e+08 Final fit statistic = 321.803 at function evaluation 77 Data points = 56 Degrees of freedom = 54 Probability [Q-value] = 9.16795e-40 Reduced statistic = 5.95931 Change in statistic = 5.98397e+08 powr.phoindex -1.36276 powr.norm 1.98057e-05
The results of these fits can then be plotted. Here we choose to display just the data and fits, not the residuals, and use logarithmic y-axes.
sherpa> plot("fit",1,"fit",2)
sherpa> current_plot("all")
sherpa> log_scale(Y_AXIS)
sherpa> print_window("pl",["format","eps"])
The plot, shown in Figure 2, is also saved in EPS format to the file "pl.eps".
![[Thumbnail image: The two spectra are plotted one above the other with the fit overlaid in red.]](2.png)
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Figure 2: Continuum Fit to the Data
The -1 HEG order is in the upper plot and the +1 HEG order is in the lower plot. The plots are displayed with a logarithmic scale and have been filtered to the range 5.0-7.0 keV.
Adding Lines to the Model and Fitting Again
We start by creating a Gaussian model component for the feature at 6.4 keV, named "g1" and set the Gaussian normalization to 1.e-4.
The create_model_component command is used to establish the model component before setting the complex source expressions.
sherpa> create_model_component("xsgaussian","g1")
sherpa> g1.norm=1.e-4
We create a model with a power law continuum plus the Gaussian line components, examine the source definition for one of the data sets (they are identical), then fit the data.
sherpa> set_source(1,powr+g1) sherpa> set_source(2,powr+g1) sherpa> show_source(1) Model: 1 (xspowerlaw.powr + xsgaussian.g1) Param Type Value Min Max Units ----- ---- ----- --- --- ----- powr.phoindex thawed -1.36276 -2 9 powr.norm thawed 1.98057e-05 0 1e+24 g1.linee thawed 6.5 0 1e+06 keV g1.sigma thawed 0.1 0 10 keV g1.norm thawed 0.0001 0 1e+24 sherpa> fit() WARNING: data set 1 has associated backgrounds, but they have not been subtracted, nor have background models been set WARNING: data set 2 has associated backgrounds, but they have not been subtracted, nor have background models been set Data Sets = 1, 2 Method = levmar Statistic = chi2datavar Initial fit statistic = 366.776 Final fit statistic = 53.3692 at function evaluation 64 Data points = 56 Degrees of freedom = 51 Probability [Q-value] = 0.383285 Reduced statistic = 1.04645 Change in statistic = 313.407 powr.phoindex -0.91256 powr.norm 4.18787e-05 g1.linee 6.3945 g1.sigma 0.00956475 g1.norm 0.000184658
![[Thumbnail image: The two spectra are plotted one above the other with the fit overlaid in red.]](3.png)
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Figure 3: Continuum Fit to the Data
The -1 HEG order is in the upper plot and the +1 HEG order is in the lower plot. The fit is overlaid in red. The plots are displayed with a logarithmic scale and have been filtered to the range 5.0-7.0 keV.
Examine the Fit Results
Plotting out our results (Figure 3), we see that we have a good fit to the spectra, including a line component. We can now explore the errors in the line parameters using the conf() command to run the Sherpa confidence routines. As is common in X-ray astronomy, we search for the 90% confidence level values for one interesting parameter (i.e., the variation of the parameter of interest that when refitting the remaining unfrozen parameters yields a change in the chi^2 value of 2.706). This corresponds to an error bar of 1.64 sigma (1 sigma being the 68% confidence interval), and set this as the default error value in the confidence routines via the "set_conf_opt" command. We then run conf for the three parameters of each of the Gaussian model components: line energy, line width, and line normalization.
sherpa> set_conf_opt("sigma",1.64)
sherpa> conf(g1.linee,g1.sigma,g1.norm)
WARNING: data set 1 has associated backgrounds, but they have not been subtracted, nor have background models been set
WARNING: data set 2 has associated backgrounds, but they have not been subtracted, nor have background models been set
g1.LineE lower bound: -0.00189496
g1.Sigma lower bound: -0.00318065
g1.LineE upper bound: 0.00190252
g1.norm lower bound: -1.86834e-05
g1.Sigma upper bound: 0.00283839
g1.norm upper bound: 1.86914e-05
Data Sets = 1, 2
Confidence Method = confidence
Fitting Method = levmar
Statistic = chi2datavar
confidence 1.64-sigma (89.8995%) bounds:
Param Best-Fit Lower Bound Upper Bound
----- -------- ----------- -----------
g1.LineE 6.3945 -0.00189496 0.00190252
g1.Sigma 0.00956475 -0.00318065 0.00283839
g1.norm 0.000184658 -1.86834e-05 1.86914e-05
We see that the line position is determined to approximately 0.002 keV, i.e., an equivalent velocity of 80 km/s. This is essentially the limit of the HEG spectral resolution. The line position is also 0.005 keV lower in energy than expected, equivalent to a 230 km/sec redshift.
The line width is <0.012 keV (i.e., <560 km/sec), but is likely >0.006 keV (i.e., >280 km/sec). Again, these are near the limits of the capabilities of the HEG detector, but far better than the limits one could place with CCD detectors.
Finally, we see that the line normalization is determined to within +/-10%.
We can now plot the data, fits, and residuals (i.e., four separate plots), all on one figure. We select the first and second plots (i.e., the data and fits, plots "plot1" and "plot2") to have logarithmic y-axes. We then print the resulting figure (Figure 4) to an eps file, sherpa_one_line.eps:
sherpa> plot("fit",1,"fit",2,"delchi",1,"delchi",2)
sherpa> current_plot("plot1")
sherpa> log_scale(Y_AXIS)
sherpa> current_plot("plot2")
sherpa> log_scale(Y_AXIS)
sherpa> print_window("sherpa_one_line",["format","eps"])
![[Thumbnail image: plot of the -/+1 HEG spectra and fit with residuals]](4.png)
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Figure 4: Fit and Residuals to the Spectral Line
The HEG -1 order (left) and +1-order (right) displayed with a logarithmic scale on the data plots and linear scale on the residual plots.
To explore whether adding another, broader line component changes the results any, we create a second Gaussian component, and call it "g2". We then define a model consisting of a power law plus two Gaussian components, and apply it to both data sets. We change the normalization of the second Gaussian to be somewhat smaller, then fit the data.
sherpa> create_model_component("xsgaussian","g2")
sherpa> g2.norm=1.e-4
sherpa> set_model(1,powr+g1+g2)
sherpa> set_model(2,powr+g1+g2)
sherpa> fit()
WARNING: data set 1 has associated backgrounds, but they have not been subtracted, nor have background models been set
WARNING: data set 2 has associated backgrounds, but they have not been subtracted, nor have background models been set
Data Sets = 1, 2
Method = levmar
Statistic = chi2datavar
Initial fit statistic = 157.779
Final fit statistic = 47.2091 at function evaluation 461
Data points = 56
Degrees of freedom = 48
Probability [Q-value] = 0.505171
Reduced statistic = 0.983524
Change in statistic = 110.57
powr.phoindex 1.70723
powr.norm 0.00326821
g1.linee 6.39445
g1.sigma 0.00889841
g1.norm 0.000180087
g2.linee 6.59744
g2.sigma 0.437574
g2.norm 0.000152164
sherpa> conf()
WARNING: data set 1 has associated backgrounds, but they have not been subtracted, nor have background models been set
WARNING: data set 2 has associated backgrounds, but they have not been subtracted, nor have background models been set
g1.Sigma lower bound: -0.00347012
g1.LineE lower bound: -0.00190839
g1.norm lower bound: -1.9232e-05
powr.PhoIndex lower bound: -3.06511
g1.LineE upper bound: 0.00191331
powr.PhoIndex upper bound: 13.0636
g1.Sigma upper bound: 0.00295635
g2.LineE -: WARNING: The confidence level lies within (6.311181e+00, 6.316044e+00)
g2.LineE lower bound: -0.283825
powr.norm -: WARNING: The confidence level lies within (5.159324e-05, 5.260262e-05)
powr.norm lower bound: -0.00321611
g1.norm upper bound: 1.92016e-05
powr.norm +: WARNING: The confidence level lies within (7.299438e-02, 7.578733e-02)
powr.norm upper bound: 0.0711226
g2.norm -: WARNING: The confidence level lies within (1.866468e-05, 1.886660e-05)
g2.norm lower bound: -0.000133398
g2.norm upper bound: 0.000986215
g2.LineE upper bound: 0.864753
g2.Sigma -: WARNING: The confidence level lies within (1.105401e-01, 1.105225e-01)
g2.Sigma lower bound: -0.327043
g2.Sigma upper bound: 1.27637
Data Sets = 1, 2
Confidence Method = confidence
Fitting Method = levmar
Statistic = chi2datavar
confidence 1.64-sigma (89.8995%) bounds:
Param Best-Fit Lower Bound Upper Bound
----- -------- ----------- -----------
powr.PhoIndex 1.70723 -3.06511 13.0636
powr.norm 0.00326821 -0.00321611 0.0711226
g1.LineE 6.39445 -0.00190839 0.00191331
g1.Sigma 0.00889841 -0.00347012 0.00295635
g1.norm 0.000180087 -1.9232e-05 1.92016e-05
g2.LineE 6.59744 -0.283825 0.864753
g2.Sigma 0.437574 -0.327043 1.27637
g2.norm 0.000152164 -0.000133398 0.000986215
The inclusion of the second line component does not greatly affect the conclusions about the parameters of the first line component. The line centroid is the same with comparable error bars. The uncertainty on the line width has increases slightly, as has the uncertainty on the line normalization.
We plot the results for this fit in a manner identical to the one Gaussian line fit (Figure 5).
sherpa> plot("fit",1,"fit",2,"delchi",1,"delchi",2)
sherpa> current_plot("plot1")
sherpa> log_scale(Y_AXIS)
sherpa> current_plot("plot2")
sherpa> log_scale(Y_AXIS)
sherpa> print_window("sherpa_two_line",["format","eps"])
![[Thumbnail image: the HEG +/-1 order two component fit]](5.png)
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Figure 5: HEG +/-1 Order Two Gaussian Component Fit
The HEG -1-order (left) and +1-order (right) displayed with a logarithmic-scale on the data plots and linear-scale on the residual plots.
Given that the extra component adds little to the description of the data, we drop it from the model and refit the data with a single line, resulting in the same parameters from before.
sherpa> set_model(1,powr+g1) sherpa> set_model(2,powr+g1) sherpa> fit() WARNING: data set 1 has associated backgrounds, but they have not been subtracted, nor have background models been set WARNING: data set 2 has associated backgrounds, but they have not been subtracted, nor have background models been set Data Sets = 1, 2 Method = levmar Statistic = chi2datavar Initial fit statistic = 121.691 Final fit statistic = 53.3692 at function evaluation 99 Data points = 56 Degrees of freedom = 51 Probability [Q-value] = 0.383285 Reduced statistic = 1.04645 Change in statistic = 68.3214 powr.phoindex -0.91258 powr.norm 4.18773e-05 g1.linee 6.39451 g1.sigma 0.00956386 g1.norm 0.000184658
Calculate the Equivalent Width of a Line and Continuum Flux
Rather than merely presenting the line normalizations, we can instead calculate the line equivalent width. The line equivalent width is an often used measure for the line strength relative to the underlying continuum. Sherpa has a function for evaluating the equivalent width, eqwidth. The required inputs are the source model absent the line feature of interest and the source model with the line feature of interest.
Optionally, the data set number for which the equivalent width will be calculated can also be given as an input, but here as we apply the same model and parameters to both data sets, this is not necessary. There are also optional 'lo' and 'hi' arguments for restricting the calculation of the equivalent width to a subset of the full energy or wavelength range of a data set.
sherpa> eqwidth(powr,powr+g1)
0.81100924079102432
The units of the equivalent width are the same as the units of the x-axis, in this case, keV. Thus the line equivalent width is ~811 eV, which indicates a very strong line. That is, there is as nearly as much flux in the line as there is in the continuum over the 6.0-7.0 keV region.
We can verify this by calculating fluxes directly, using the calc_photon_flux and calc_energy_flux commands. We first calculate these fluxes from the full model (continuum plus line) over just the 6.0-7.0 keV range. The returned fluxes are in units of photons/cm2/sec and ergs/cm2/sec, respectively.
sherpa> calc_photon_flux(6.,7.) 0.00041605430036275367 sherpa> calc_energy_flux(6.,7.) 4.3076189648505557e-12
Deleting the power law continuum component, we calculate the flux values for just the line component. We do this by changing the model with set_model to only the Gaussian component without refitting.
sherpa> set_model(1,g1)
sherpa> set_model(2,g1)
sherpa> calc_photon_flux(6.,7.)
0.00018465756
sherpa> calc_energy_flux(6.,7.)
1.8918411620618855e-12
Over this one keV bandwidth, the line contributes a fraction, ~1.84/(4.16-1.84)~0.79, to the photon flux. This is approximately consistent with the equivalent width calculation of 811 eV.
Scripting It
The file fit.py is a Python script which performs the primary commands used above; it can be executed by typing execfile("fit.py") on the Sherpa command line.
The Sherpa script command may be used to save everything typed on the command line in a Sherpa session:
sherpa> script(filename="sherpa.log", clobber=False)
(Note that restoring a Sherpa session from such a file could be problematic since it may include syntax errors, unwanted fitting trials, etcetera.)
The CXC is committed to helping Sherpa users transition to new syntax as smoothly as possible. If you have existing Sherpa scripts or save files, submit them to us via the CXC Helpdesk and we will provide the CIAO/Sherpa 4.4 syntax to you.
Summary
As shown above, working with gratings spectra is fundamentally no different than working with CCD spectra. Aside from the fact that gratings data will often begin with a PHA2 file containing multiple spectra, rather than a type 1 PHA file containing a single spectrum, the analysis paths can be very much the same for both types of spectra. (If one is performing analysis in either Sherpa or ISIS, the PHA2 file can be used directly, whereas for XSPEC one has to use the intermediate step of creating type 1 PHA files in order to be able to bin the data.) The data are read in, response matrices are assigned, models are defined and fit, and error bars on the parameters are determined. Line fluxes and equivalent widths can be calculated easily. Gratings spectra can even be slightly less complicated than CCD spectra in that ACIS-S/HETG spectra often do not require a background. (Again, order sorting of the spectra often ensures that the background is negligible.)
History
| 01 Apr 2009 | new for Sherpa 4.1 |
| 11 Jan 2010 | updated for CIAO 4.2 |
| 13 Jul 2010 | updated for CIAO 4.2 Sherpa v2: removal of S-Lang version of thread. |
| 22 Jan 2012 | reviewed for CIAO 4.4 (no changes) |
