Fission with Exotic Nuclei

Project Overview

Nuclear fission is a key mechanism involved in the synthesis of heavy elements in the Cosmos and is the primary explanation for the stability of superheavy elements. Nevertheless, our knowledge of fission remains extremely fragmented. Most experiments have been conducted only on a tiny number of stable actinide nuclei and are often incomplete, leading to gaps in our basic understanding of the process. For many radioactive isotopes, basic fission data such as the charge or mass distribution of the fragments is unknown. These gaps cannot always be filled by simulation alone. Common fission models contain too many free parameters and lack predictive power. In contrast, the fundamental theory of fission under development at Lawrence Livermore National Laboratory (LLNL) is much more predictive, but its current computational cost is too high to be used extensively for data evaluations. A unique window of opportunity to resolve these limitations has recently opened: the U.S. nuclear science community is ramping up major experimental programs at the Facility for Rare Isotope Beams (FRIB), the DOE flagship facility in low-energy nuclear science, and techniques from machine learning have shown great potential to simplify the use of a fundamental, quantum-mechanical theory of fission.

This project has two components. On the experimental side, we acquired and deployed at the High Intensity Gamma-ray Source (HIGS) facility a new dual Frisch-Grid ionization chamber to measure correlated fragment-mass, kinetic energy, and angular distributions of fission fragments from induced fission. This new device was used to perform measurements of charge, mass and total kinetic energy of fission fragments in the photofission of 238U and eight gamma-ray beam energies between 6.2 and 13 MeV, which allowed extracting high-precision independent yields for this reaction. The device was also used to perform measurements of the same quantities in the neutron-induced fission of 234U with monoenergetic beams of energy between 5 and 8 MeV. In parallel, we collaborated with a team at Commissariat à l'énergie atomique et aux énergies alternatives (CEA) to perform a series of measurements of fission yields in inverse kinematics for the two isotopes of 236U and 240Pu. The experiment took place at the Grand Accélérateur National d'Ions Lourds in France in June 2023. The deployment of the VAMOS spectrometer with a new array called PISTA allowed determining the excitation energy of the fissioning system within 1 Mega-electronvolts.

The second component of the project involved using deep neural networks to build fast and reliable emulators of our current fission models. In an invited paper published in Frontier in Physics, we showed that autoencoders could successfully compress nuclear wavefunctions in nuclear density functional theory. We achieved a dimensionality reduction of the order of two orders of magnitude while keeping the error in the total energy to less than 0.01%.

Mission Impact

The need for high-precision fission data, especially in short-lived minor actinides out of reach of standard reaction techniques, is a strategic priority for the NACS division. Such data play an important role in enhancing the fundamental understanding of aging nuclear weapons systems or maintaining and enhancing the safety, security, and effectiveness of the U.S. nuclear weapons stockpile. They are also one of the most important diagnostic tools for Nuclear Forensics. Our work has improved the LLNL evaluation pipeline by further integrating theoretical and experimental efforts on fission. It strengthened and expanded the partnership with the statistics and data science groups of the Computing directorate. The knowledge of inverse kinematics reaction acquired during this project is truly unique in the U.S. and will position LLNL at the forefront of nuclear science activities at FRIB. More broadly, we have developed new science and technology tools and capabilities to meet future national security challenges.

Publications, Presentations, and Patents

Verriere, M., N. Schunck, I. Kim, P. Marevic, K. Quinlan, M.N. Go, D. Regnier, and R.D. Lasseri. "Building surrogate models of nuclear density functional theory with Gaussia processes and autoencoders." Front. Physics. 10. 2022.  https://doi.org/10.3389/fphy.2022.1028370