Conception and Software Implementation of a Nuclear Data Evaluation Pipeline

@article{Schnabel2020ConceptionAS,
  title={Conception and Software Implementation of a Nuclear Data Evaluation Pipeline},
  author={Georg Schnabel and Henrik Sjostrand and Joachim Hansson and Dimitri Rochman and Arjan J. Koning and Roberto Capote},
  journal={Nuclear Data Sheets},
  year={2020}
}
View PDF on arXiv
7 Citations

7 Citations

Iterative Bayesian Monte Carlo for nuclear data evaluation

An iterative Bayesian Monte Carlo (iBMC) method for nuclear data evaluation within a TALYS Evaluated Nuclear Data Library (TENDL) framework to find the optimal model and parameter sets that reproduces selected experimental data.

Infrastructure for the new paradigm of nuclear reaction evaluation

G-HyND: a hybrid nuclear data estimator with Gaussian processes

It was demonstrated that an experimental dataset and a physics model-based analytical dataset perform a complementary role in nuclear data generation, and that the generated nuclear data from the hybrid training dataset are more reasonable than only those from the experimental dataset.

Machine Learning in Nuclear Physics

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics,…

Artificial Intelligence and Machine Learning in Nuclear Physics

Current nuclear data needs for applications

Colloquium : Machine learning in nuclear physics

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics,…

References

SHOWING 1-10 OF 127 REFERENCES

Modern Nuclear Data Evaluation with the TALYS Code System

Large scale Bayesian nuclear data evaluation with consistent model defects

A new evaluation scheme has been developed, which is mathematically equivalent to existing methods, but allows the consistent evaluation of dozens of millions of observables.

Nuclear data evaluation methodology including estimates of covariances

Evaluated nuclear data rather than raw experimental and theoretical information are employed in nuclear applications such as the design of nuclear energy systems. Therefore, the process by which such…

Experimental Nuclear Reaction Data Uncertainties: Basic Concepts and Documentation

Evaluation of the covariance matrix of neutronic cross sections with the Backward-Forward Monte Carlo method

A method, based on the Monte Carlo sampling of the model parameter space, that allows for a quantitative estimation the covariance matrix of themodel parameters, as well as that of the derived cross sections of the n+ 89 Y system is proposed.

An Investigation of the Performance of the Unified Monte Carlo Method of Neutron Cross Section Data Evaluation

Statistical analysis of cross sections- : A new tool for data validation

TENDL: Complete Nuclear Data Library for Innovative Nuclear Science and Technology

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

A sparse approximation to Gaussian processes can be used to estimate the model bias over the complete nuclide chart at the example of inclusive double-differential neutron spectra for incident protons above 100\,MeV.

The Global Assessment of Nuclear Data, GANDR

The GANDR project was initiated by the IAEA in 2001 with several ambitious goals, including the eventual provision of assistance to data experts in the planning of new measurements. We describe a…
...