Deconvolution is a mathematical technique used to extract detailed information from complex data sets. In the context of catalysis, it involves separating overlapping signals to identify individual components, such as different catalytic sites or reaction intermediates. This process is crucial for understanding the mechanisms and efficiency of catalytic reactions.