Machine Learning Takes Hold in Nuclear Physics
As machine learning tools gain momentum, a review of machine learning projects reveals these tools are already in use throughout nuclear physics.
As machine learning tools gain momentum, a review of machine learning projects reveals these tools are already in use throughout nuclear physics.
The results of parity-violating electron scattering experiments PREX and CREX suggest a disagreement with global nuclear models.
A theoretical analysis of recent findings in neutron star research suggests the possibility of a phase transition in these stars’ interiors.
New measurements show the proton’s electromagnetic structure deviates from theoretical predictions.
Deblurring, practiced in optics, can reveal three-dimensional features of nuclear collisions.
Nuclear physicists find that the internal structures of protons and neutrons may be altered in different ways inside nuclei.
A first-of-its-kind measurement of the rare calcium-48 nucleus found a neutron-rich “thin skin” around a core of more evenly distributed protons and neutrons.
Colliding gold nuclei at various energies enables scientists to investigate phases of nuclear matter and their possible co-existence at a critical point.
Researchers study the energy and angular dependence of how neutrons scatter off materials to improve reactor safety and efficiency.
Scientists measure the proton’s electric and magnetic polarizabilities using the High Intensity Gamma Ray Source (HIGS).
The observation of a resonance in the beryllium-11 nucleus suggests that the proton emission from beryllium-11 is a two-step process rather than a dark matter decay channel.
Experiments confirm the NUCLEI collaboration’s predictions of the existence of the tetraneutron.