Related papers: Programs Enabling Deep Technology Transfer from Na…
We are in the midst of quantum hype with some excessive claims of quantum computing potential, many vendors' and even some research organizations' exaggerations, and a funding frenzy for very low technology readiness level startups.…
Particle physics or High Energy Physics (HEP) studies the elementary constituents of matter and their interactions with each other. Machine Learning (ML) has played an important role in HEP analysis and has proven extremely successful in…
This proceeding covers tools and technologies at our disposal for scientific data preservation and shows that this extends the scientific reach of our experiments. It is cost-efficient to warehouse data from completed experiments on the…
We investigate the prospects to systematically improve generic effective field theory-based searches for new physics in the top sector during LHC run 2 as well as the high luminosity phase. In particular, we assess the benefits of high…
Machine Learning Potentials (MLPs) can enable simulations of ab initio accuracy at orders of magnitude lower computational cost. However, their effectiveness hinges on the availability of considerable datasets to ensure robust…
In the current era of AI transforming the research-education environment of physics, variety of issues and concerns arise. The KITP program "Generative AI for High and Low Energy Physics'' offered a discussion session on this, and here…
Fermilab is executing a technology development program to develop a compact yet powerful electron accelerator. We are leveraging R&D breakthroughs in SRF cavities, cost-effective radio-frequency sources, modern cryo-coolers, and high…
For many decades High Energy Physics (HEP) instrumentation has been concentrated on detectors of ionizing radiation -- where the energy of incident particles or photons is sufficient to create mobile charge in gas, liquid, or solid…
This report summarizes insights from the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science, which convened more than 40 experts from national…
The scientific community is currently witnessing an expensive and worldwide race to achieve the highest possible light intensity. Within the next decade this effort is expected to reach nearly $10^{24}\,\mathrm{W}/\mathrm{cm^2}$ in the lab…
Tensor network methods are becoming increasingly important for high-energy physics, condensed matter physics and quantum information science (QIS). We discuss the impact of tensor network methods on lattice field theory, quantum gravity and…
This paper discusses experimental techniques and considerations associated with the transition to high repetition-rate experiments in High Energy Density Physics (HEDP). We particularly highlight approaches to experimentation that become…
Excitonic models of light-harvesting complexes, where the vibrational degrees of freedom are treated as a bath, are commonly used to describe the motion of the electronic excitation through a molecule. Recent experiments point toward the…
An ensemble of electrons trapped above superfluid helium offers a paradigm system for investigating and controlling collective charge dynamics in low-dimensional electronic matter. Of particular interest is the ability to spatially control…
This study introduces a novel transformer model optimized for large-scale point cloud processing in scientific domains such as high-energy physics (HEP) and astrophysics. Addressing the limitations of graph neural networks and standard…
The European Laboratory Directors Group (LDG) was mandated by CERN Council in 2021 to oversee the development of an Accelerator R&D Roadmap. To this end, a set of expert panels was convened, covering the five broad areas of accelerator R&D…
Near-field magnetic resonance wireless power transfer (WPT) technology has garnered significant attention due to its broad application prospects in medical implants, electric vehicles, and robotics. Addressing the challenges faced by…
In High Energy Physics (HEP), analysis metadata comes in many forms -- from theoretical cross-sections, to calibration corrections, to details about file processing. Correctly applying metadata is a crucial and often time-consuming step in…
The Superfacility model is designed to leverage HPC for experimental science. It is more than simply a model of connected experiment, network, and HPC facilities; it encompasses the full ecosystem of infrastructure, software, tools, and…
A Hamiltonian based approach using spatially localized projection operators is introduced to give precise meaning to the chemically intuitive idea of the electronic energy on a quantum subsystem. This definition facilitates the study of…