Plotting the Spectrum

With the first fit of your data complete, you should look at the data and model to see how well they match up. Xspec can plot the data and model for you to inspect.

First, however, you will want to change one of Xspec's defaults. By default, Xspec will plot the "channel" of the data on the x-axis. The channel is related to energy of the detected light. However, the channel-to-energy conversion will be different for different detectors, and in the same detector, it may change over time. To make the results more meaningful, you will want to have the plot display photon energy on the x-axis.

To change the plot from "channel" to energy, type the following in the Xspec command window:

setplot energy

Now, to put Xspec into plot mode, type to following:

iplot data

With Xspec in plot mode, you can make the plot prettier by changing the limits on the x-axis and adding color to the data to make the model stand out more. The following commands will do this for you:

Type:

R x 0.5 10
color 2 on 1
plot

Plot of black body model (bottom line, in black) and data (top line, in red) after fit.

The red line is data and the black line is the model. Evaluate the model. does it appear to be a good fit for the data? In other words does it seem to represent the data well? Do they overlap?

If you are able, you may want to print the graph, so you can compare this graph with the final version. (Click here if you need instructions on printing the graph.)

When you are finished tweaking the look of the plot, you will want to leave Xspec's plot environment by typing:

exit

During the rest of this activity, you can update the plot with changes you make to the model and fit using the easyplot command like this:

easyplot 0.5 10.0

The 0.5 and 10.0 in the above command set the x-axis limits.

Answer these questions about your current model and plot:

• Note the chi-squared for this fit. (Found in the Xspec Command Window.)
• How well does the model match the data?
• Are there places where the model matches particularly well? Where?
• Are there places where the model matches particularly poorly? Where?
• Do you think that you have found the best model for these data? Why or why not?
• If not, then describe what features of the data the model seems to be missing.