Spin orientation preference may point to a previously unknown influence of the strong nuclear force—and a way to measure its local fluctuations.
Machine learning techniques track turbulent blobs in millions of frames of video from tokamak experiments.
Physicists use a detector under an Italian mountain to search for rare nuclear processes to explain why our Universe has more matter than antimatter.
Study reveals that initial state conditions set up particle flow patterns, helping zero in on key properties of matter that mimics the early universe.
Researchers combined crystallographic data and computational studies to investigate plutonium-ligand bonding within a hybrid material construct.
Researchers use particle-resolved model simulations to quantify errors in simulations’ simplified optical properties.
Suppression of a telltale sign of quark-gluon interactions indicates gluon recombination in dense walls of gluons.
Quantum interference between dissimilar particles offers new approach for mapping gluons in nuclei, and potentially harnessing entanglement.
Physicists show that black holes and dense state of gluons—the “glue” particles that hold nuclear matter together—share common features.
Experiment shows that even large, old, and presumably stable stores of soil carbon are vulnerable to warming and could amplify climate change.
Powerful statistical tools, simulations, and supercomputers explore a billion different nuclear forces and predict properties of the very-heavy lead-208 nucleus.
Nuclear physicists test whether next generation artificial intelligence and machine learning tools can process experimental data in real time.
