Neutron Drip Line in the Ca Region from Bayesian Model Averaging.

Area of Science:

• Nuclear physics
• Nuclear structure research
• Heavy isotopes

Background:

• The region of heavy calcium isotopes is critical for testing nuclear physics concepts.
• Recent discoveries of neutron-rich nuclei (around ^{60}Ca) and mass measurements (for ^{55-57}Ca) offer new insights into binding energy.

Purpose of the Study:

• To evaluate the impact of recent experimental data on the extent of the nuclear landscape.
• To predict bound nuclides between Silicon (Si) and Titanium (Ti) using global mass models and machine learning.

Main Methods:

• Utilized global mass models and statistical machine learning for predictions.
• Employed Bayesian model averaging with Gaussian-process-based extrapolations.
• Introduced posterior probability (p_{ex}) for nuclei stability against neutron emission.

Main Results:

• Extrapolations for nuclear drip-line locations show consistency across models, despite variations in raw predictions.
• ^{68}Ca is predicted to have a high probability (≈76%) of being bound to two-neutron emission.
• ^{61}Ca shows a significant probability (≈46%) of decaying via neutron emission.

Conclusions:

• Recent experimental data and global mass models provide a consistent view of the nuclear landscape's extent.
• Predictions offer quantified certainty regarding the stability of exotic heavy calcium isotopes.
• The study refines our understanding of the limits of nuclear binding in neutron-rich regions.