Figure 9.

Extraction of the memory kernel from data. Same as Fig. (5), the monoexponential case, but now the noise is level is raised to 60%. These histograms have less features than their biexponential counterparts so it is easier to extract the memory kernel from such histograms at higher noise levels. The most prominent feature of the memory kernel is, somewhat surprisingly, still the sharp peak at the origin which is correct. However, at such high noise levels, the magnitude of the peak recorded at the origin is far from the theoretical exact answer.