Speaker
Description
LA-UR-21-29134
It is a well-known fact that our current nuclear data libraries, among them ENDF/B-VIII.0 [1] in the US, are home to compensating errors that lurk in unconstrained physics spaces [2]. Unconstrained physics spaces arise, on the one hand, due to differential information (from theory and experiments) that is too imprecise to fully describe nuclear data. One the other, this is combined with validating combinations of nuclear data with integral experiments cannot uniquely identify one nuclear data value as driving the difference between simulated and experimental values of these experiments. If we knew where these unconstrained physics spaces occur in nuclear data libraries, we could either build targeted experiments or propose theory developments that help to shine a light on them and, thus, reduce the possibility of compensating errors.
The EUCLID project (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) developed a work-flow [3] that identifies these unconstrained physics spaces by bringing together various experimental values of integral responses (criticality, $\beta_\mathrm{eff}$, reaction rates in critical assemblies, LLNL pulsed spheres neutron-leakage spectra, reactivity coefficients and sub-critical assembly observables) with their simulated counter-parts as well as differential information. This wealth of information is processed by machine learning tools supported by the random forest algorithms and analyzed by human experts [4]. Here, we will show how this work-flow is executed and a few representative examples of where compensating errors could potentially hide in ENDF/B-VIII.0.
Acknowledgments
Research reported in this publication was supported by the U.S. Department of Energy LDRD program at Los Alamos National Laboratory. This work was supported by the US Department of Energy through the Los Alamos National Laboratory. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of the US Department of Energy under Contract No. 89233218CNA000001.
[1] D.A. Brown, M.B. Chadwick, R. Capote et al.,ENDF/B-VIII.0: The 8th Major Release of the Nuclear Reaction Data Library with CIELO-project Cross Sections, New Standards and Thermal Scattering Data,Nucl. Data Sheets 148, 1 (2018).
[2] E. Bauge, G. Belier, J. Cartier et al., Coherent Investigation of Nuclear Data at CEA DAM: Theoretical Models, Experiments and Evaluated Data, The European Physical Journal A 48, 113 (2012).
[3] D. Neudecker, O. Cabellos, A.R. Clark et al., Informing Nuclear Physics via Machine Learning Methods with Differential and Integral Experiments, Phys. Rev. C 104, 034611 (2021).
[4] D. Neudecker, M. Grosskopf, M. Herman et al., Enhancing Nuclear Data Validation Analysis by Using Machine Learning, Nucl. Data Sheet 167, 36 (2021).