Nuclear physicists test whether next generation artificial intelligence and machine learning tools can process experimental data in real time.
Particles choose partners for short-range correlations differently when farther apart in light nuclei versus when packed closer together in heavy nuclei.
As machine learning tools gain momentum, a review of machine learning projects reveals these tools are already in use throughout nuclear physics.
New measurements show the proton’s electromagnetic structure deviates from theoretical predictions.
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.
Adding a little oxygen to particle accelerator structures may make them more efficient and easier to build.
A recent measurement of the neutron-rich “skin” around lead nuclei reveals new details of neutron behavior and the dynamics of neutron stars
Nuclear theorists demonstrate a new method for computing the strengths of subatomic interactions that include up to three particles.
If physicists can find it, color transparency in protons could offer new insight into the particles that build our universe.
A new machine learning system diagnoses particle accelerator component issues in real-time.
A result 20 years in the making: Most precise measurement yet of the lifetime of the charge-neutral pion that keeps protons and neutrons together.
