A Boehnlein, M Diefenthaler, N Sato, M Schram… - Reviews of modern …, 2022 - APS
Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear …
W He, Q Li, Y Ma, Z Niu, J Pei, Y Zhang - Science China Physics …, 2023 - Springer
Abstract Machine learning (ML) is becoming a new paradigm for scientific research in various research fields due to its exciting and powerful capability of modeling tools used for …
P Achenbach, D Adhikari, A Afanasev, F Afzal… - Nuclear Physics A, 2024 - Elsevier
Abstract This White Paper presents an overview of the current status and future perspective of QCD research, based on the community inputs and scientific conclusions from the 2022 …
Since the release of the 2015 Long Range Plan in Nuclear Physics, major events have occurred that reshaped our understanding of quantum chromodynamics (QCD) and nuclear …
The borders of the periodic table of the elements and of the chart of nuclides are not set in stone. The desire to explore the properties of atoms and their nuclei in a regime of very large …
The observation of neutrino oscillations and hence non-zero neutrino masses provided a milestone in the search for physics beyond the Standard Model. But even though we now …
We review recent progress and motivate the need for further developments in nuclear optical potentials that are widely used in the theoretical analysis of nucleon elastic scattering and …
This work presents the first Bayesian inference study of the (3+ 1) D dynamics of relativistic heavy-ion collisions and quark-gluon plasma viscosities using an event-by-event (3+ 1) D …
To improve the predictability of complex computational models in the experimentally- unknown domains, we propose a Bayesian statistical machine learning framework utilizing …