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Though being seemingly disparate and with relatively new intersection, high energy nuclear physics and machine learning have already begun to merge and yield interesting results during the last few years. It's worthy to raise the profile of…
The pieces of the hypernuclear strangeness violating potential due to the pseudoscalar meson exchanges are derived using methods which were successfully applied to hyperon nonleptonic decays. The estimates are tested by comparison with…
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is…
Machine learning potentials have revolutionised the field of atomistic simulations in recent years and are becoming a mainstay in the toolbox of computational scientists. This paper aims to provide an overview and introduction into machine…
The development of supergravity unified models and their implications for current and future experiments are discussed.
Charm physics has been established at the LHC based on several high-precision measurements. The future of charm physics at the LHC experiments is discussed in detail. The bulk of the charm physics programme will be performed by LHCb and the…
We analyze the physics opportunities that would be made possible by upgrades of CERN's proton accelerator complex. These include the new physics possible with luminosity or energy upgrades of the LHC, options for a possible future neutrino…
We are at the verge of a new impact from hypernuclear experiments planned or already operative at various laboratories all over the world. The complementary of these different experimental approaches to hypernuclei provides a wide basis for…
By using a suitable set of the surface energy coefficient, nuclear radius, and universal function, the original proximity potential 1977 is modified. The overestimate of the data by 4 % reported in the literature is significantly reduced.…
Neural networks have become popular in many fields of science since they serve as promising, reliable and powerful tools. In this work, we study the effect of data augmentation on the predictive power of neural network models for nuclear…
Present and future neutrino experiments at accelerators are mainly concerned with understanding the neutrino oscillation phenomenon and its implications. Here a brief account of neutrino oscillations is given together with a description of…
This article presents an overview of neutrino physics research, with highlights on the physics goals, results and interpretations of the current neutrino experiments and future directions and program. It is not meant to be a comprehensive…
We review the prospects for studies of the top quark at the LHC.
The effects of the theoretical uncertainties in the description of neutrino-nucleus cross sections for supernova neutrino energies are investigated.
After a very brief review of twentieth century elementary particle physics, prospects for the next century are discussed. First and most important are technological limits of opportunities; next, the future experimental program, and finally…
Future lepton colliders will have a rich top quark physics program which would add to our understanding of this interesting quark and promise an ultimate precision on the experimental knowledge of top-quark mass. We present possible…
We discuss the physics potential and the experimental challenges of an upgraded LHC running at an instantaneous luminosity of 10**35 cm-2s-1. The detector R&D needed to operate ATLAS and CMS in a very high radiation environment and the…
I review progress that has been made in nuclear astrophysics over the past few years and summarize some of the questions that remain. Topics selected include solar neutrinos, supernovae (the explosion and associated nucleosynthesis),…
A directed R&D program is presently underway in the U.S. to evaluate the designs and technologies required to provide muon-based high energy physics (HEP) accelerator capabilities. Such capabilities have the potential to provide unique…
Recent years have witnessed the fast development of machine learning potentials (MLPs) and their widespread applications in chemistry, physics, and material science. By fitting discrete ab initio data faithfully to continuous and…