Impact of tensor forces on quasifission product yield distributions
Liang Li, Lu Guo, K. Godbey, and A. S. Umar

TL;DR
This study uses microscopic TDHF theory to analyze how tensor forces affect quasifission product yields in heavy-ion collisions, revealing that tensor interactions influence shell effects and fragment distributions depending on the interaction parametrization.
Contribution
It provides new insights into the role of tensor forces in quasifission processes and compares different effective interaction parametrizations to understand their impact on fragment distributions.
Findings
Tensor forces affect shell effects in quasifission yields.
Different interaction parametrizations show varying shell effect prominence.
Shell effects are sensitive to specific regions of the tensor coupling constant space.
Abstract
We employ the microscopic time-dependent Hartree-Fock (TDHF) theory to study the 48Ca+249Bk and 48Ti+238U systems, taking into account the dependence on orientation for deformed nuclei and full range of impact parameters. By analyzing fragment distributions of neutron and proton numbers, we assess the influence of different isoscalar and isovector tensor coupling constants of the effective nucleon-nucleon interaction. The quasifission yield distributions of 48Ca + 249Bk collision system utilizing SLy5t and T31 parametrizations exhibit more pronounced spherical shell effects compared to those using SLy5, T44 and T62 sets. Furthermore, within each parametrization group, the distributions for SLy5t and T31 are closely aligned, as are those for SLy5, T44, and T62. Similarly, the yield distributions for the 48Ti + 238U system using SLy5t and T31 also reflect the more pronounced spherical…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsFinancial Risk and Volatility Modeling · Complex Systems and Time Series Analysis
