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This report is an outcome of the workshop AI for Nuclear Physics held at Thomas Jefferson National Accelerator Facility on March 4-6, 2020. The workshop brought together 184 scientists to explore opportunities for Nuclear Physics in the area of Artificial Intelligence. The workshop consisted of plenary talks, as well as six working groups. The report includes the workshop deliberations and additional contributions to describe prospects for using AI across Nuclear Physics research.
The low-energy, long-lived isomer in $^{229}$Th, first studied in the 1970s as an exotic feature in nuclear physics, continues to inspire a multidisciplinary community of physicists. Using the nuclear resonance frequency, determined by the strong and
Atomic physics techniques for the determination of ground-state properties of radioactive isotopes are very sensitive and provide accurate masses, binding energies, Q-values, charge radii, spins, and electromagnetic moments. Many fields in nuclear ph
Using QCD calculations of the cross section of inclusive dijet photoproduction in Pb-Pb ultraperipheral collisions in the LHC kinematics as pseudo-data, we study the effect of including these data using the Bayesian reweighting technique on nCTEQ15,
Progress in nuclear physics is driven by the experimental observation that requires state of the art detectors to measure various kinematic properties, such as energy, momentum, position etc. of the particles produced in a nuclear reaction. Advances
Using the data on coherent $J/psi$ photoproduction in Pb-Pb ultraperipheral collisions (UPCs) obtained in Runs 1 and 2 at the Large Hadron Collider (LHC), we determined with a good accuracy the nuclear suppression factor of $S_{Pb}(x)$ in a wide rang