Estimating model bias over the complete nuclide chart with sparse Gaussian processes at the example of INCL/ABLA and double-differential neutron spectra

摘要: Predictions of nuclear models guide the design facilities to ensure their safe and efficient operation. Because often do not perfectly reproduce available experimental data, decisions based on predictions may be optimal. Awareness about systematic deviations between data helps alleviate this problem. This paper shows how a sparse approximation Gaussian processes can used estimate model bias over complete nuclide chart at example inclusive double-differential neutron spectra for incident protons above 100\,MeV. A powerful feature presented approach is ability predict energies, angles, isotopes where are missing. The number points that taken into account least in order magnitude of~$10^4$ thanks approximation. applied Li\`ege Intranuclear Cascade Model (INCL) coupled evaporation code ABLA. results suggest process regression viable candidate perform global quantitative assessments models. Limitations philosophical nature (and any other) also discussed.