SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
We conducted theoretical studies in high energy physics beyond the standard model, using lattice gauge theory techniques, with a particular emphasis on supersymmetric fields theories. There are many reasons for formulating and studying such theories using this first principles approach, with the goal in mind of repeating the successes of lattice quantum chromodynamics—which are quite substantial.
This report reflects the work performed at the University of Mississippi under the support of DOE EPSCoR grant DE‐SC0021274 during the period of September 2020 through August 2024, including a one-year, no-cost extension. A summary of the research outcomes is given, with reference to the project goals as stated in the proposal. This successful project supported the mission of the DOE High Energy Physics program by leveraging the complimentary expertise of researchers at the University of Mississippi and Brookhaven National Lab to search for CP violation in charmed baryon decays using data from the Belle II experiment and to provide vital support for Belle II distributed computing.
This document has been prepared as a Snowmass contributed paper by the Public Policy & Government Engagement topical group (CEF06) within the Community Engagement Frontier. The charge of CEF06 is to review all aspects of how the High Energy Physics (HEP) community engages with government at all levels and how public policy impacts members of the community and the community at large, and to assess and raise awareness within the community of direct community-driven engagement of the US federal government (\textit{i.e.} advocacy). The focus of this paper is the potential for HEP community advocacy on topics other than funding for basic research.
Researchers at the Fermi National Accelerator Laboratory (Fermilab) developed extruded plastic scintillator in the late 1990s, which was first used in the D-Zero experiment. Extruded plastic scintillator is currently produced at Fermilab and is used in particle detectors worldwide. The purpose of this CRADA is to transfer the knowledge related to the Fermilab extrusion process to Itasca Plastics, Inc. (Itasca Plastics). Much of this knowledge is contained in documentation that is in the public domain, although it is distributed over several communications (papers, conference records, etc.) and over several years. Under this CRADA Fermilab will assemble the information, provide it to Itasca Plastics and provide limited consulting to complete the knowledge transfer. If the transfer is successful, Itasca Plastics will be able to establish a U.S. commercial manufacturing capability for extruded scintillator material that can be used for high energy physics and commercial applications.
High energy physics in the U.S. has ambitious plans requiring new and improved accelerator technologies, including an upgraded complex at Fermilab for DUNE, next-generation light sources, and the potential of a future collider to be built on U.S. soil. To address these requirements, Fermilab operates the Fermilab Accelerator Science and Technology (FAST) facility, dedicated to accelerator R&D. FAST includes an electron gun and superconducting RF linac (up to 300 MeV), a storage ring, and an upcoming proton source and injector line (up to 2.5 MeV). In addition to the future of Fermilab accelerators, the broad physics program includes general R&D with potential impact across the DOE science program. This colloquium will provide a basic introduction to accelerator technologies and describe the principles of proton and electron accelerators, including the challenges associated with next-generation operations. We will discuss the exciting R&D ongoing at FAST to address the required technological advancements, focusing on Non-Linear Integrable Optics (NIO) for improved beam intensity and Optical Stochastic Cooling (OSC) for improved beam quality, and touching on a wide range of additional ongoing research. I intend to make the content interesting and accessible to those who have never taken any formal courses in accelerator physics.
Geant4 is a widely used simulation toolkit in high-energy physics (HEP), enabling precise modeling of particle interactions with matter. It plays a crucial role in the development and optimization of detector concepts for future HEP programs. With a structured set of examples ranging from basic to advanced, Geant4 provides a comprehensive set of concrete examples for users at different expertise levels. As a critical tool for the HEP community, Geant4 continues to evolve, adapting to emerging computational paradigms and experimental requirements.
The large-aperture, high-gradient Low Beta Quadrupoles for the High Luminosity Large Hadron Collider at CERN are the cornerstones of this collider. They are first-of-the-kind accelerator magnets bringing Nb_3Sn conductor to High Energy Physics colliders. In the US they are being fabricated and tested by the HL-LHC Accelerator Upgrade Project (AUP), and in Europe by CERN. The design was developed by AUP and CERN in close collaboration. The Q1/Q3 cryo-assemblies contain two 4.2 m long magnets (MQXFA) fabricated and by AUP. They are tested vertically before use in a cry-assembly. In this paper we are going to present the main features of the MQXFA design, results of vertical tests, and main lessons learned. We will also discuss lessons learned for future use of Nb_3Sn magnets in HEP colliders.
The Proton Improvement Plan – II (PIP-II) project at Fermi National Accelerator Laboratory (Fermilab) is the first particle accelerator project in the USA to receive significant in-kind contributions from international partners. Partnering countries include the USA, India, Italy, the United Kingdom, France, and Poland. When completed, PIP-II will accelerate protons to 800 MeV, and power the world’s most intense neutrino beam for the Deep Underground Neutrino Experiment (DUNE). Technological advancements for PIP-II across the entire collaboration have significantly matured, with the project currently in the construction phase. The Indian Institutions & Fermilab Collaboration (IIFC) for PIP-II has a 14+ years history. The IIFC R&D phase concluded with the validation of various prototypes and defined the beginning of the construction phase. This presentation highlights the advances of the synergistic PIP-II collaboration. Work supported, in part, by the U.S. Department of Energy, Office of Science, Office of High Energy Physics, under U.S. DOE Contract No. 89243024CSC000002.
Advancing Scientific Understanding in Fusion Energy and Machine Learning This research represented a significant step forward in machine learning (ML) applications for fusion energy experiments. The project integrated advanced data-driven modeling, optimization techniques, and artificial intelligence to enhance the predictive capabilities and operational efficiency of plasma-based fusion systems. Specifically, tasks focused on ML-enhanced diagnostics, operator guidance tools, and predictive modeling helped improve the ability to interpret complex fusion experiments. Key areas of advancement included: 1) data-driven plasma control, i.e., using ML algorithms to optimize experimental conditions and classify plasma behaviors based on historical data; 2) spectroscopy and diagnostics, i.e., applying AI models to extract previously inaccessible insights from experimental spectroscopy data; and 3) configuration mapping and operator guidance, i.e., developing a predictive framework to assist scientists in identifying the most effective experimental parameters, reducing reliance on manual adjustments. By refining these ML-driven techniques, the project contributed to the broader scientific community’s understanding of plasma dynamics and fusion energy viability. Technical Effectiveness and Economic Feasibility The methods investigated demonstrated high technical effectiveness, as reflected in milestones assessing the predictive accuracy, performance, and optimization of fusion configurations. The development of an Operator Guidance Tool (OGT), for example, led to more precise control of plasma conditions by learning from experimental data and offering real-time adjustments. From an economic standpoint, DeFT provided: 1) the ability to reduce trial-and-error experimentation, which lowered operational costs; 2) improved data interpretation methods, which enabled more efficient resource allocation in large-scale fusion research projects; and 3) the automation of key diagnostic tasks, which reduced manual labor and human error, increasing overall efficiency. 13 The final assessments of predictive models and optimization strategies demonstrated that these approaches were scalable and could be implemented across multiple fusion energy research programs. Public Benefit and Societal Impact This project contributed directly to the broader goal of achieving sustainable and commercially viable fusion energy, which had profound implications for clean energy production and climate change mitigation. The integration of AI-driven solutions into fusion research: 1) sped up scientific discovery, accelerating progress towards achieving energy breakthroughs; 2) reduced the cost of experimentation, making fusion research more accessible; and 3) provided a framework for future AI applications in high-energy physics, benefiting adjacent fields like space exploration, material science, and renewable energy. Additionally, by fostering collaborations between AI researchers and plasma physicists, this project promoted interdisciplinary innovation that could lead to broader applications beyond fusion research.
The Long Baseline Neutrino Facility (LBNF) includes at the Fermi National Accelerator Laboratory (Fermilab) site a Beamline which will aim neutrinos towards massive detectors at the Sanford Underground Research Facility (SURF) at South Dakota. The neutrinos will be created by a beam of 60-120 GeV protons extracted from Fermilab’s Main Injector interacting with a target located in the end of the LBNF proton beamline. The design of the proton beamline involves 79 conventional magnets; 23 are corrector magnets. These correctors are of a new design which evolved from those built for the Fermilab Main Injector. Following the production of a prototype corrector magnet by the Institute of High Energy Physics (IHEP) and its testing by both IHEP and Fermilab, IHEP will procure the materials for the 24 (including one spare) corrector magnets needed by the LBNF proton beamline; then, will build, test and certify them
The bias-variance tradeoff is a well-recognized phenomenon in statistics and machine learning. In this talk, I will discuss an extension, dubbed the bias-variance-correlation tradeoff. Roughly speaking, as the flexibility of a model decreases, the correlations in the outputs of a trained model for different inputs increases. Such correlations have implications for several applications of machine learning in high energy physics, e.g., the use generative models for event generation. In particular, I will argue that claims in the literature of data amplification by generative models stem from ignoring important correlations between the model's outputs for different inputs.
This poster relates to a project that is investigating the use of the FABRIC federated testbed to scale-up research infrastructure to develop network functions that use smart (programmable) network equipment to support High Energy Physics (HEP) research. The poster describes preliminary work that involves non trivial packet parsing related to Fermilab DAQ workloads that is implemented on real hardware, and a correctness and performance evaluation.
In this work, we adapt the Convolutional Visual Network (CVN) approach [1] to the ICARUS detector by incorporating TPC stitching methods inspired by NuGraph[8]. The stitching technique used here is unique to ICARUS, designed specifically to handle its distinct detector segmentations. We then retrain the network using ICARUS-specific data. This study underscores the flexibility of deep learning models in high-energy physics and the importance of accounting for detector-specific features when transferring machine learning techniques between experiments. This poster presents the methods, classification performance, and insights gained from applying CVN to ICARUS data.
This poster presents a study on material activation at the Fermilab Long-Baseline Neutrino Facility (LBNF), focusing on understanding how high-energy beams interact with surrounding materials to produce radioactive isotopes. Using a simplified model of the LBNF-20 bunker and the FLUKA Monte Carlo simulation tool, the project quantifies isotope production at key locations inside and outside the shielding structures. The study employs a two-step simulation process, tracking primary and secondary particles, and explores the impact of different geometric and material configurations on activation rates. Results include preliminary comparisons of simulated activation rates and suggest methods for refining geometry, improving accuracy, and efficiently simulating future configurations. This work informs the design and operation of the LBNF facility, aiding in radiation safety and shielding optimization. Future efforts will focus on sample iteration, automated simulations, and detailed residual isotope analyses to further enhance the understanding of material activation in high-energy physics environments.
The National Energy Research Scientific Computing Center (NERSC), as the high-performance computing (HPC) facility for the Department of Energy’s Office of Science, recognizes the essential role of quantum computing in its future mission. In this report, we analyze the NERSC workload and identify materials science, quantum chemistry, and high-energy physics as the science domains and application areas that stand to benefit most from quantum computers. These domains jointly make up over 50% of the current NERSC production workload, which is illustrative of the impact quantum computing could have on NERSC’s mission going forward. We perform an extensive literature review and determine the quantum resources required to solve classically intractable problems within these science domains. This review also shows that the quantum resources required have consistently decreased over time due to algorithmic improvements and a deeper understanding of the problems. At the same time, public technology roadmaps from a collection of ten quantum computing companies predict a dramatic increase in capabilities over the next five to ten years. Our analysis reveals a significant overlap emerging in this time frame between the technological capabilities and the algorithmic requirements in these three scientific domains. We anticipate that the execution time of large-scale quantum workflows will become a major performance parameter and propose a simple metric, the Sustained Quantum System Performance (SQSP), to compare system-level performance and throughput for a heterogeneous workload.
The Zettawatt-Equivalent Ultrashort pulse laser System (ZEUS) is presently operational at the Gerard Mourou Center for Ultrafast Optical Science (CUOS) at the University of Michigan. ZEUS is a significant upgrade of the previous high power laser systems at CUOS and consists of two beamlines thatoperate in perfect synchronization. The 500 TW beamline became operational in 2023, 2 PW operation started in 2025 and full 3 PW power levels will be available in 2026. It is presently the highest power laser system in the US. In this grant the high field science group at CUOS has leveraged this unique high power laser facility to investigate laser wake field acceleration (LWFA) in ultra-high power laser plasma interactions and have shown how this can scale for future electron–positron colliders at high energy. The dual beam experimental configuration enables flexibility for many frontier experiments in laser-driven acceleration research, in particular, enabling extended channelling/acceleration experiments, positron generation/acceleration experiments and proof-of-principle transverse pumping “dephasingless” electron acceleration experiment and theory. LWFA may be able to miniaturize particle accelerators for high energy physics and also enable new sources of ultrafast, extreme brightness and precise x-rays for a wide variety of applications. In laser wake field acceleration, an electron bunch “surfs” on the electron plasma wave (the “wake field”) generated by the ponderomotive force of an intense laser. The plasma wave has a strong longitudinal electric field that stays in phase with the relativistic driver. A relativistic charged particle may, therefore, remain in phase with the accelerating field over long distances and gain ultra-relativistic energies. The accelerating electric field strength that the plasma wave can support can be many orders of magnitude higher than that of conventional accelerators, which makes laser wakefield acceleration an exciting prospect as an advanced accelerator concept. In this research project we have investigated the scaling of this mechanism to laser powers of 2 PW and have measured the x-ray emission and radio frequency emission resulting from the acceleration process. We have also performed theoretical investigation of mechanisms to scale laser driven accelerators to much higher energy using dephasingless acceleration processes.
The Fermilab Accelerator Complex is the largest national user facility in the Office of High Energy Physics (DOE/HEP) program and the only national user facility operating at Fermilab. Fermilab serves as the host to the Long Baseline Neutrino Facility/Deep Underground Neutrino Experiment (LBNF/DUNE), the laboratory’s flagship project for neutrino science that is under construction. LBNF/DUNE will be powered by megawatt beams from an upgraded accelerator, the Proton Improvement Plan II (PIP-II) that will replace the laboratory’s aging linear accelerator with a new one based on superconducting radio-frequency cavities. The Accelerator Controls Operations Research Network (ACORN) Project will support LBNF/DUNE and PIP-II by modernizing the accelerator control system. The project is at the conceptual design phase and looking to achieve Critical Decision 1 (CD-1) later this year. The scope and structure of the project will be presented, along with an overview of how that has changed in the past year. Current design and technology choices will be shared. Specific challenges facing the project will be addressed, along with current thinking on solutions.
High-energy physics experiments studying neutrinos rely heavily on simulations of their interactions with atomic nuclei. Limitations in the theoretical understanding of these interactions typically necessitate ad hoc tuning of simulation model parameters to data. Traditional tuning methods for neutrino experiments have largely relied on simple algorithms for numerical optimization. While adequate for the modest goals of initial efforts, the complexity of future neutrino tuning campaigns is expected to increase substantially, and new approaches will be needed to make progress. In this paper, we examine the application of simulation-based inference (SBI) to the neutrino interaction model tuning for the first time. Using a previous tuning study performed by the MicroBooNE experiment as a test case, we find that our SBI algorithm can correctly infer the tuned parameter values when confronted with a mock data set generated according to the MicroBooNE procedure. This initial proof-of-principle illustrates a promising new technique for next-generation simulation tuning campaigns for the neutrino experimental community.