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hello everyone welcome back to the nuclear criticality safety lecture series today were going to discuss the surfer experimental data assimilation or data adjustment method which compares the observed benchmark experiment measurements with the calculated results from modeling and simulation codes to calibrate nuclear data as well see this method can be used to estimate something that is analogous to upper subcritical limits lets say that we have the result of a benchmark critical experiment and the result of a high fidelity monte carlo eigenvalue simulation as you can see these two results are slightly different which is because of the computational bias in our modeling and simulation code since our code is a high fidelity monte carlo code with a minimal method bias and since the fundamental theorem of tsunami states that errors and uncertainty in nuclear data are the main drivers for computational bias and in theory we should be able to adjust our nuclear data in a way that causes