Hyeonseop Lee | Searcharxiv

arXiv2024

Shell Effects in Quasi-fission in Reactions Forming 226Th Compound Nucleus

Quasi-fission (QF) reactions occur in fully damped heavy-ion collisions without the formation of an equilibrated compound nucleus, leading to the formation of fragments with similar properties as in fission reactions. Similar shell effects are expected to affect fragment formation in both fission and QF. Our purpose is to investigate QF dynamics in different reactions forming the same compound nucleus and search for possible signatures of shell effects in fragment formation. 50Ca+176Yb and 96Zr+130Sn QF reactions are simulated with the time-dependent Hartree-Fock code Sky3D near the Coulomb barrier. Evolutions of the quadrupole (Q20) and octupole (Q30) moments are interpreted in terms of features of the potential energy surface (PES) of the 226Th compound nucleus. Both reactions encounter QF. In 50Ca+176Yb, those only occur at finite angular momenta. In the more symmetric 96Zr+130Sn reaction with stronger Coulomb repulsion in the entrance channel, QF also occurs in central collisions. In agreement with earlier predictions, 50Ca+176Yb encounters partial mass equilibration that is stopped when the heavy fragment reaches Z~54 protons, as in the asymmetric fission mode of 226Th. 96Zr+130Sn encounters an inverse QF also leading to similar fragments as in asymmetric fission. In both systems, QF trajectories in the Q20-Q30 plane follow the asymmetric fission valley of 226Th PES. The observation of an inverse QF is a clear prediction that shell effects have a strong influence in QF. The similarity between fragments formed in asymmetric fission and QF supports the idea that the same shell effects are at play in both mechanisms. Interpreting QF dynamics with PES used in fission is naturally limited by the fact that these PES are usually computed with axial symmetry, no angular momentum and no excitation energy, thus motivating future developments of PES for QF.

nucl-th↗

arXiv2020

Mixed pooling of seasonality for time series forecasting: An application to pallet transport data

Multiple seasonal patterns play a key role in time series forecasting, especially for business time series where seasonal effects are often dramatic. Previous approaches including Fourier decomposition, exponential smoothing, and seasonal autoregressive integrated moving average (SARIMA) models do not reflect the distinct characteristics of each period in seasonal patterns. We propose a mixed hierarchical seasonality (MHS) model. Intermediate parameters for each seasonal period are first estimated, and a mixture of intermediate parameters is taken. This results in a model that automatically learns the relative importance of each seasonality and addresses the interactions between them. The model is implemented with Stan, a probabilistic language, and was compared with three existing models on a real-world dataset of pallet transport from a logistic network. Our new model achieved considerable improvements in terms of out of sample prediction error (MAPE) and predictive density (ELPD) compared to complete pooling, Fourier decomposition, and SARIMA model.

stat.ML↗