Initial default values are 2 to 10 keV for 0 redshift. The energy range redshifted to the observed range must be contained by the range covered by the current data sets which determine the range over which the model is evaluated. Values outside this range will be automatically reset to the extremes. Note that the energy values are two separate arguments and are NOT connected by a dash see parameter ranges in the freeze command description.
The error algorithm is to draw parameter values from the distribution and calculate a luminosity. The parameter values distribution is assumed to be a multivariate Gaussian centered on the best-fit parameters with sigmas from the covariance matrix.
This is only an approximation in the case that fit statistic space is not quadratic. Note that fit must be run before using the error option and that the model cannot be changed using model , editmod , addc , or delc between running the fit and calculating the luminosity error.
Examples: The current data have significant response to data within 1 to 18 keV. Calculate the current luminosity over 6. The simple rules for expressions are : 1. The energy term, or the radius term for mixing model, must be 'e' or 'E' in the expression. Other words, which are not numerical constants nor internal functions, are assumed to be model parameters.
If a convolution model varies with the location on the spectrum to be convolved, the special variable. E may be used to refer to the convolution point. The maximum number of model parameters is The expression may contain spaces for better readability.
Valid types are add, mul, mix, con. Default values are 1. Note that mdefine can also be used to display and delete previously defined models. The command mdefine with no arguments will display the name, type, and expression of all defined models.
A single argument of a model name will display name, type, and expression just for that model. The model name followed by : will delete the specified model. Please use a different name for your model. E : con! E method Set the minimization method. Their meanings are explained under the individual methods. The approach is, however, generic to the spectral analysis of data from all three instruments.
Before continuing, download the required calibration files to a single local directory. Please note that spectral files can also be generated using the WWW inteface webspec , while, if count rates are the only purpose of your feasibility study, pimms is the webtool of choice. Red text designates characters typed by you. Green text desginates screen messages from the package. Extensive on-line help is available typing: help or:?
For this example we will use a simple power law with an exponential high enery cutoff and an additional power law at the highest energies. This appears to be an appropriate model for prompt gamma ray bursts at high energies Band et al.
In this case the new component is added before the first component of the current model. The nH parameter is entered in units of 10 22 atoms cm Again, the nH parameter is entered in units of 10 22 atoms cm Also note that while we do not implement it here, the redden model characterizes Galactic extinction at energies shortward of the Lyman limit and we expect it to be used extensively to model UVOT data with XSPEC.
The zredden model should be used to model extinction with a Galactic reddening law in the host galaxy. Alternative reddening laws are available using the zdust model.
We're going to renormalize this spectrum appropriately to model GRB Therefore the model normalization has to be multiplied appoximately by 1. The newpar command is used to change the values of model parameters. In this case we want to multiply the normalization parameter 7 by 1. If counting statistics are used, the spectra will be randomized according to the count rate of each channel.
Thus all the response files are re-read this might take a while. The energy range covered by the current response matrix may thereby be increased. Examples: Assume that 4 data sets have been read in, the first 2 having channels and the last 2 having 50 channels. Assume also that channels of all four data sets are ignored and that channels of data sets 1 and 2 are ignored.
The first 10 channels of all 4 data sets are noticed. An attempt will be made to notice channels 80 in all 4 data sets as that was the last data set range specified but the result is that only channels will be noticed for data sets 1 and 2, with no change for data sets 3 and 4 as they have no channels greater than No channels are noticed, as these channels were noticed in the beginning. Any of the options counts , data , or ldata can be followed by a second option, which should be one of chisq , delchi , ratio , residuals , or none.
If one of these second options is given, then a two-pane plot is drawn with the first option in the upper pane and the second option in the lower. The fit statistic confidence contours are often drawn based on a relatively small grid i. To fully understand what these plots are telling you, it is useful to know a couple of points concerning how the software chooses the location of the contour lines.
The contour plot is drawn based only on the information contained in the sample grid. For example, if the minimum fit statistic occurs when parameter 1 equal 2.
This could mean that there are no grid points where delta-fit statistic is less than your lowest level which defaults to 1. As a result, the lowest contour will not be drawn. This effect can be minimized by always selecting a steppar range that causes XSPEC to step very close to the true minima. For the above example, using steppar 1 1. The location of a contour line between grid points is designated using a linear interpolation. Since the fit statistic surface is often quadratic, a linear interpolation will result in the lines being drawn inside the true location of the contour.
The combination of this and the previous effect sometimes will result in the minimum found by the fit command lying outside the region enclosed by the lowest contour level. Plot out a grid with three contours with delta fit statistic of 2. Note that it plots the emission measure integrated over each temperature bin not really the DEM which should be emission measure per unit temperature interval.
For the cooling flow models the plot is of the emission measure divided by the mass flow rate. The contributions of the various additive components also are plotted. The contributions to the model of the various additive components are also plotted.
This corresponds to the -f plot beloved of AGN researchers. This plot is not model-independent and your unfolded model points will move if the model is changed. Only currently noticed channels are included in the calculation of the efficiency. The contributions to the model of the various additive components also are plotted. The integrated counts are normalized to one. Takes two arguments specifying the parameters to be plotted.
If one of these arguments is zero then the fit statistic is plotted instead of a parameter. If on then the continue fitting question in fit , steppar , and error will be asked when the number of trials is exceeded.
Also when the number of trials to find the error is exceeded a question will be asked. So to ensure that fitting continues without any questions being asked use the command query yes. Giving the command with no arguments will print the current status enabled or disabled.
Readline is enabled by default. For more information on using readline see Appendix D. If you wish to fit a data set's correction norm individually, then refer only to that data set.
Note : The use of the recornrm command requires that a response and model be defined. It is perhaps best if a preliminary fit is performed. The user then should alternate between fit 's and recornrm 's until a stable solution is achieved.
If the model is not a good fit, this process may not converge. All the files' correction norms are adjusted by a single number. Files 1,2,3, and 5 are adjusted. File 5 the last range input is adjusted by itself.
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