Normalization and the AUTOBK Algorithm

Learning about background removal

The primary function of ATHENA is to import and process XAS data. In the broadest sense, this task takes three steps:

• Import raw data and convert it to μ(E).

• Normalize the data so that the measurement is independent of the details of the sample or the detector setup.

• Determine the background function and subtract it from the data to make χ(k).

Of course, there are many other details, such as calibration, alignment, and deglitching. Those will be discussed in detail in later sections of the document. In this section, we will cover the details of the normalization algorithm and the AUTOBK background removal algorithm. Special attention will be payed to the most important background removal parameters.

For many measured μ(E) spectra, ATHENA will do a good job of normalizing data and removing the background using its default parameters. In other situations -- noisy data, data with large white lines, data which terminate in the appearance of another edge -- user intervention is required. for those situations it is important that you understand well how the various parameters in the background removal section of the main window affect the data.