Speaker
Description
The necessary improvement of evaluated nuclear data for nuclear applications development is possible through new and high quality experimental measurements.
In particular, improving (n, n') cross-section evaluations for faster neutrons than in current reactors is a goal of interest for new reactor fuels.
Our group at CNRS-IPHC has been running an experimental program to measure (n, n' γ) cross-section using prompt gamma-ray spectroscopy and neutron energy determination by time-of-flight, recording and analyzing data for 182,184,186W, 232Th, 233,235,238U [1,2].
From the partial transition measurements, the total (n, n') cross-section has to be inferred, either by summing individual contributions [3] (a method usually valid only up to a certain neutron energy), or by constraining reaction models [4,5]. This interpretation work is made difficult in (the usual) cases when not all the transitions going to the ground state could be measured. If that happens, one has to rely on filling the missing information by models or guess, reducing the accuracy of the final computed cross-section.
Here we propose a new method, involving training a Neural Network on a calculated data set and using it to predict the (n, n') cross-section from the experimental (n, n' γ) ones. This allows a quick combination of models and experimental data.
After detailing the method and checks performed for consistency, some test cases will be presented. Potential benefits, as well as the identified weakness, and future application will be discussed.
- “What can we learn from (n, x n γ) cross sections about reaction mechanism and nuclear structure ?”, by Kerveno, Maëlle and Dupuis, Marc and Borcea, Catalin and Boromiza, Marian and Capote, Roberto and Dessagne, Philippe and Henning, Greg andHilaire, Stéphane and Kawano, Toshihiko and Negret, Alexandra and Nyman, Markus and Olacel, Adina and Party, Eliot and Plompen, Arjan and Romain, Pascal and Sin, Mihaela. ND 2019 : International Conference on Nuclear Data for Science and Technology (2019). 10.1051/epjconf/202023901023 https://hal.archives-ouvertes.fr/hal-02957494
- “From γ emissions to (n,xn) cross sections of interest : The role of GAINS and GRAPhEME in nuclear reaction modeling”, by Kerveno, M. and Bacquias, A. and Borcea, C. and Dessagne, Ph. and Henning, G. and Mihailescu, C. and Negret, A. and Nyman, M. and Olacel, A. and Plompen, M. and Rouki, C. and Rudolf, G. and Thiry, C. in European Physical Journal A 51, 12 (2015). 10.1140/epja/i2015-15167-y https://hal.archives-ouvertes.fr/hal-02154831
- Olacel, A., Borcea, C., Dessagne, P., Kerveno, M., Negret, A., & Plompen, A. J. M. (2014). Neutron inelastic cross-section measurements forMg24. Physical Review C, 90(3). doi:10.1103/physrevc.90.034603
- “How to produce accurate inelastic cross sections from an indirect measurement method ?”, by Kerveno, Maëlle and Henning, Greg and Borcea, Catalin and Dessagne, Philippe and Dupuis, Marc and Hilaire, Stéphane and Negret, Alexandru and Nyman, Markus and Olacel, Adina and Party, Eliot and Plompen, Arjan in EPJ N - Nuclear Sciences & Technologies 4, (2018). 10.1051/epjn/2018020 https://hal.archives-ouvertes.fr/hal-02109918
- Negret, A., Sin, M., Borcea, C., Capote, R., Dessagne, P., Kerveno, M., … Rouki, C. (2017). Cross-section measurements for the Fe57(n,nγ)Fe57 and Fe57(n,2nγ)Fe56 reactions. Physical Review C, 96(2). doi:10.1103/physrevc.96.024620
