Machine Learning Techniques Enhance the Discovery of Excited Nuclear Levels in Sulfur-38
Forefront nuclear physics capabilities and machine-learning data analyses combine to generate new information on quantum energy levels in sulfur-38.
Forefront nuclear physics capabilities and machine-learning data analyses combine to generate new information on quantum energy levels in sulfur-38.
Scientists translate predictions of hydrodynamics into experimentally observable particle patterns.
Theoretical calculations and experimental data combine to reduce uncertainty in a key reaction rate in modelling high-energy solar neutrinos.
Neural networks guided by physics are creating new ways to observe the complexities of plasmas.
Perturbing the edge magnetic field of a tokamak produces a counterintuitive response: particles entering the confined region rather than escaping it.
For the first time, scientists successfully track energetic ion flow through space and energy driven by electromagnetic waves in fusion plasmas.
Small rotating magnetic islands in tokamaks flowing at the same speed can couple together to cause disruptive islands that reduce plasma confinement.
Quantum simulations reveal the presence of entanglement among the quarks produced in high energy collisions.
Scientists investigate neutrinoless double beta decay through neutrino mass and the nuclear structure of germanium-76.
A measurement tracking ‘direct’ photons from polarized proton collisions points to positive gluon polarization.
New theoretical work indicates that the future Electron Ion Collider can be used to measure the shape of atomic nuclei.
A new experiment determines the energy available to drive chemical reactions at the interface between an illuminated semiconductor and a liquid solution.