A microscopic description of nuclear fission for r-process nucleosynthesis
Lead Research Organisation:
University of York
Department Name: Physics
Abstract
Although the basic mechanism of nuclear fission has been already explained more than 80 years ago by N. Bohr in terms of quantum tunnelling. It is still not possible to provide a microscopic description of all relevant available fission mechanisms - spontaneous, induced, beta-delayed, photo-fission - to a high level of accuracy.
A microscopic description of nuclear fission does not only imply calculating life-times, but also the statistical distribution of charge and mass in the outgoing fragments as well the total energy of the system shared among them. This information is essential, especially in several astrophysical scenarios, for understanding the patterns observed in abundances of heavy elements in our Universe.
The standard model based on rapid-neutron capture (r-process) to form elements heavier than Iron-56 heavily involves the concept of fission, since it represents the end-point of the mechanism. According to the fission lifetime and the length of neutron flux, fission can produce new seeds that can be used for new r-process events, thus changing the relative abundances of elements. Moreover fission is itself a source of neutron and gamma rays. Such extra neutrons can keep going the r-process even after the external neutron flux has stopped. This mechanism is called fission recycling and it involves very neutron rich nuclei that are not accessible via experimental measurements.
To provide a microscopic description of fission, nuclear energy density functional calculations (NEDF) are used to calculate potential energy surfaces. Given the very complicated structure of these energy surfaces, one needs to perform thousands of NEDF calculations to explore the entire energy landscape. Consequently NEDF fission calculations are not very common and they are restricted to a few specific cases.
Gaussian Process Emulation (GPE), a regression method from machine learning, can be used to reduce the computational burden of these massive calculations. GPE methods are based on very simple mathematical assumptions: a given energy surface in a multidimensional space can be modelled by a global trend term (a polynomial) and some Gaussian fluctuations on top of it.
This project seeks to reduce the computational cost of calculating potential energy surfaces for fission, using GPE, and using other numerical tools to improve the efficiency of the NEDF calculations.
A microscopic description of nuclear fission does not only imply calculating life-times, but also the statistical distribution of charge and mass in the outgoing fragments as well the total energy of the system shared among them. This information is essential, especially in several astrophysical scenarios, for understanding the patterns observed in abundances of heavy elements in our Universe.
The standard model based on rapid-neutron capture (r-process) to form elements heavier than Iron-56 heavily involves the concept of fission, since it represents the end-point of the mechanism. According to the fission lifetime and the length of neutron flux, fission can produce new seeds that can be used for new r-process events, thus changing the relative abundances of elements. Moreover fission is itself a source of neutron and gamma rays. Such extra neutrons can keep going the r-process even after the external neutron flux has stopped. This mechanism is called fission recycling and it involves very neutron rich nuclei that are not accessible via experimental measurements.
To provide a microscopic description of fission, nuclear energy density functional calculations (NEDF) are used to calculate potential energy surfaces. Given the very complicated structure of these energy surfaces, one needs to perform thousands of NEDF calculations to explore the entire energy landscape. Consequently NEDF fission calculations are not very common and they are restricted to a few specific cases.
Gaussian Process Emulation (GPE), a regression method from machine learning, can be used to reduce the computational burden of these massive calculations. GPE methods are based on very simple mathematical assumptions: a given energy surface in a multidimensional space can be modelled by a global trend term (a polynomial) and some Gaussian fluctuations on top of it.
This project seeks to reduce the computational cost of calculating potential energy surfaces for fission, using GPE, and using other numerical tools to improve the efficiency of the NEDF calculations.
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/N50452X/1 | 01/10/2015 | 31/03/2021 | |||
| 1947388 | Studentship | ST/N50452X/1 | 01/10/2017 | 31/03/2021 | Matthew Shelley |
| ST/R505213/1 | 01/10/2017 | 30/09/2021 | |||
| 1947388 | Studentship | ST/R505213/1 | 01/10/2017 | 31/03/2021 | Matthew Shelley |