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Model defects are known to cause biased nuclear data evaluations if they arenot taken into account in the evaluation procedure. We suggest a method toconstruct prior distributions for model defects for reaction models usingneighboring isotopes of $^{56}$Fe as an example. A model defect is usually afunction of energy and describes the difference between the model predictionand the truth. Of course, neither the truth nor the model defect areaccessible. A Gaussian process (GP) enables to define a probabilitydistribution on possible shapes of a model defect by referring to intuitivelyunderstandable concepts such as smoothness and the expected magnitude of thedefect. Standard specifications of GPs impose a typical length-scale andamplitude valid for the whole energy range, which is often not justified, e.g.,when the model covers both the resonance and statistical range. In thiscontribution, we show how a GP with energy-dependent length-scales andamplitudes can be constructed from available experimental data. The proposedconstruction is inspired by a technique called dynamic time warping used, e.g.,for speech recognition. We demonstrate the feasibility of the data-drivendetermination of model defects by inferring a model defect of the nuclearmodels code TALYS for (n,p) reactions of isotopes with charge number between 20and 30. The newly introduced GP parametrization besides its potential toimprove evaluations for reactor relevant isotopes, such as $^{56}$Fe, may alsohelp to better understand the performance of nuclear models in the future.

A first sketch: Construction of model defect priors inspired by dynamic time warping