Parametrization of Λ is generally done using predetermined weight factors for the different contributions , but λ is treated as a user-adjustable parameter. To allow for a fully automatized parametrization without adjustable parameter, one option would be an algorithmic parameter adjustment . Alternatively, it has been suggested to set λ such that the regularization term in Equation 6 equals the norm of the experimental noise . Since the UP regularization is rather insensitive to the exact parameter selection , the latter option represents a simpler and still robust choice. It has been successfully implemented not only for DRT , but also for applications with exponential kernel, which is more problematic in terms of noise enhancement in case of underregularization than the Debye kernel . As a test criterion for a correct parametrization, it must be ensured, for example by testing with simulated data, that the inverted distribution does not show smaller line widths than the ideal result.