Tricks For Background Fitting
If your data is dirty, as in radial diffraction data in the diamond anvil cell, background fitting can become fairly tricky. Let's be clear, there is not miracle and I do not know any perfect method. Here are three methods you can plan with
Global Polynomial Background
Global polynomial backgrounds can be edited by opening the
If you dataset is clean, with an isotropic background, you will be able to refine your data this way. If your dataset is not fully isotropic, you will be able to get a first average adjustment. This first average adjustment will be improved by adding polynomial individual backgrounds.
Individual Polynomial Backgrounds
If your background is not constant for all spectra, you can add individual background parameters. To do so,
Once background parameters exist, you will need to perform a refinement. Here is how I proceed:
Usually, after a few trials and errors, it works.
If your data is dirty, as in radial diffraction data in the diamond anvil cell, background fitting can become fairly tricky. Interpolated background could be your solution. Usually, it is not recommended to do so in Rietveld refinements (you migh smooth out details that contain real data) but, sometimes, there is not other way.
To do so,
Once you are done, the background will be interpolated between the points you choose. If your data is fairly dirty, this can become very useful.