Status and Future Prospects for United States Accelerators and Accelerator Physics
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This fact sheet summarizes the key accomplishments and results from DOE's Better Buildings Sustainable Wastewater Infrastructure of the Future (SWIFt) Accelerator. The Accelerator was a 3-year effort between DOE and state and local jurisdictions to promote energy data management practices in wastewater operations in an effort to increase energy efficiency in wastewater facilities around the country. Accelerator participants shared implementation plans, and DOE combined the results into a final toolkit.
Precise knowledge of neutrino interaction cross sections is required for current and future accelerator-based neutrino oscillation experiments. Precision neutrino oscillation measurements require inference of the neutrino energy and flavor from the visible particles in the neutrino interaction in the detector. This inference is different for the true neutrino flavors measured, electron and muon neutrinos, and can be studied by observation of neutrino interactions in an experiment’s near detector. However, anti-electron neutrinos make up only a few percent of an anti-muon neutrino beam and pose a challenge in making cross section measurements and predictions. The reported measurements were made using data from MINERvA, a neutrino-nucleus scattering experiment, with an anti-neutrino beam configuration of mean energy ~ 6 GeV. This thesis provides two double-differential cross sections of anti-electron neutrino inclusive charged-current reactions using the kinematics of visible energy, three-momentum transfer, and transverse momentum. The analysis is carried out at low three-momentum transfer, making it sensitive to regions with multi-nucleon effects.
High energy physics research will need more and more powerful circular accelerators in the next decades. It is therefore desirable to have dipole magnets able to produce the largest possible magnetic field, in order to keep the machine diameter within a reasonable size. A 20 T dipole is considered a desired achievement since it would allow the construction of an 80 km machine, able to circulate 100 TeV proton beams. In order to reach 20 T, a hybrid Low-Temperature Superconductor (LTS) - High-Temperature Superconductor (HTS) magnet is needed, since LTS technology is presently limited to ~16 T for accelerator magnet applications. In this paper, we present the design of a 6 layers 20 T hybrid dipole magnet using Nb3Sn (LTS) and Bi2212 (HTS). We show that it is possible to achieve this magnetic field with accelerator field quality, with sufficient margin on a realistic conductor, keeping the stresses within safe limit, avoiding conductor degradation
Traveling-Wave (TW) technology can push the accelera-tor field gradient of niobium SRF cavity to 70MV/m or higher beyond the fundamental limit of 50~60MV/m in Standing-Wave regime. The success of TW resonance excitation in a proof-of-principle 3-cell SRF cavity in 2K liquid helium encouraged to advance TW technologies necessary more for future accelerator-scale one. Fermilab has proposed a preliminary RF design of a half-meter scale TW cavity by considering the physical dimensions of existing SRF facilities and the lessons learned from the 3-cell. It consists of a 7-cell structure and a power feed-back waveguide (WG) loop with new TW excitation and control schemes such as, double directional coupler and two WG tuners. Mock-up waveguide loop development was launched under Fermilab LDRD program to demon-strate those new RF schemes at a room temperature. Fab-rication drawings of a mock-up loop were completed. Here details and plans of the development are reported.
Lifelong learning-an agent's ability to learn throughout its lifetime-is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI). The development of lifelong learning algorithms could lead to a range of novel AI applications, but this will also require the development of appropriate hardware accelerators, particularly if the models are to be deployed on edge platforms, which have strict size, weight and power constraints. Here we explore the design of lifelong learning AI accelerators that are intended for deployment in untethered environments. We identify key desirable capabilities for lifelong learning accelerators and highlight metrics to evaluate such accelerators. We then discuss current edge AI accelerators and explore the future design of lifelong learning accelerators, considering the role that different emerging technologies could play. The Perspective explores the future design of lifelong learning artificial intelligence (AI) accelerators that are intended for deployment in untethered environments, identifying key desirable capabilities for such edge AI accelerators and guidance on metrics to evaluate them.
Future scientific discoveries will rely on flexible ecosystems that incorporate modern scientific instruments, high performance computing resources, parallel distributed data storage, and performant networks across multiple, independent facilities. In addition to connecting physical resources, such an ecosystem presents many challenges in logistics and accessibility, especially in orchestrating computations and experiments that span across leadership computing systems and experimental instruments. Past efforts have typically been application-specific or limited to interfaces for computing resources. This paper proposes a general framework for integrating computation resources and instrument operations, addressing challenges in code development/execution, data staging and collection, software stack, control mechanisms, resource authorization and governance, and hardware integration. We also describe a demonstration use case wherein a Bayesian optimization algorithm running on an edge computing resource guides a scanning probe microscope to autonomously and intelligently characterize a material sample. This science edge ecosystem framework will provide a blueprint for federating multi-institutional, disparate resources and orchestrating scientific workflows across them to enable next-generation discoveries.
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In this talk I provide an update on the RF research for future colliders in the U.S., through the prism of Snowmass and P5 report. During Snowmass, we considered various applications of RF technology to the proposed future colliders and other accelerator- and non-accelerator-based experiments. P5 narrowed down the choices of future machines. The colliders include circular and linear $e^+ e^-$ Higgs factories, and longer-term options such as muon and hadron high energy colliders. I will start with Snowmass and P5 recommendations. As it is impossible to cover all possible RF R&D topics, I will discuss only three critical topics relevant to future colliders: efficiency of RF power sources, cold normal conducting RF, and cavities for ionization channel of muon collider. Progress on SRF accelerating cavities for future colliders will be covered in a separate talk. The choice of topics reflects my preference and in some cases ignorance, which I think is inevitable when one tries to cover such a broad subject.
The development of effective vaccines is crucial for combating current and emerging pathogens. Despite significant advances in the field of vaccine development there remain numerous challenges including the lack of standardized data reporting and curation practices, making it difficult to determine correlates of protection from experimental and clinical studies. Significant gaps in data and knowledge integration can hinder vaccine development which relies on a comprehensive understanding of the interplay between pathogens and the host immune system. In this review, we explore the current landscape of vaccine development, highlighting the computational challenges, limitations, and opportunities associated with integrating diverse data types for leveraging artificial intelligence (AI) and machine learning (ML) techniques in vaccine design. We discuss the role of natural language processing, semantic integration, and causal inference in extracting valuable insights from published literature and unstructured data sources, as well as the computational modeling of immune responses. Furthermore, we highlight specific challenges associated with uncertainty quantification in vaccine development and emphasize the importance of establishing standardized data formats and ontologies to facilitate the integration and analysis of heterogeneous data. Through data harmonization and integration, the development of safe and effective vaccines can be accelerated to improve public health outcomes. Looking to the future, we highlight the need for collaborative efforts among researchers, data scientists, and public health experts to realize the full potential of AI-assisted vaccine design and streamline the vaccine development process.
The future discoveries in the field of High Energy Physics are led by the development of new types of accelerators. Around the world, there are a lot of researchers that are studying some possible designs for the future accelerators and some of them are considering the use of alternating current inside the superconducting magnets in order to have better efficiency. The work described in this report tries to compare some superconducting magnets made of different materials and to understand which one of them can be considered for its better performance working in a variable magnetic field. For this goal, becomes really important to control the temperature of the coil, and, as visible in the next chapter, to relate this parameter to the frequency of the magnetic field, in order to find, as will be named, the critical frequency .
The history of particle physics can be considered nothing less than a huge triumph for science. Over the course of a little more than a century of effort, our understanding of the world of atomic and subatomic physics went from a vague understanding of atoms, to one that is much more detailed. Early in this hundred-year-long period, we learned about electrons (1897), then how they circle a dense nucleus (1911), followed by the discovery of the protons (1917) and neutrons (1932) that form the nucleus. From the 1930s onward, researchers used both cosmic rays and particle accelerators to discover antimatter (1932), and particles that don’t exist in atoms (e.g., the muon [1936] and neutrino [1956], as well as a huge number of others).
Idaho National Laboratory (INL) announced at COP27 it would reach net zero greenhouse gas (GHG) emissions by 2031. As a Nuclear, Energy and Environment, and National Homeland Security laboratory, the predominant solution to closing the clean energy gap will include nuclear as a safe, clean, reliable and affordable electricity source with the additional benefit of producing heat and hydrogen to fuel INL’s large transportation fleet. INL’s collaboration first vs. competition is essential to the program’s success. The focused actions in INL’s Nuclear Roadmap include: Infrastructure, Licensing/Regulatory, Financial, Time to Market, Fuel Cycle and Public Confidence/Communications. The roadmap also includes nuclear technology innovations and creative partnerships with utility providers, regulators, businesses, community members, and Indigenous Peoples to accelerate deployment of advanced reactors. Through development of the Net-Zero Nuclear Roadmap, INL will offer a model to provide safe and secure energy for the nation and the world by: (1) establishing the necessary infrastructure on its 890-square mile site to support demonstration, (2) showing proven pathways through the licensing and regulation process, (3) partnering with utilities to ensure commercial application, and (4) collaborating with industry to site new technologies.
In order to define the acceleration requirements for future Shuttle and Space Station Freedom payloads, methods and hardware characterizing accelerations on microgravity experiment carriers are discussed. The different aspects of the acceleration environment and the acceptable disturbance levels are identified. The space acceleration measurement system features an adjustable bandwidth, wide dynamic range, data storage, and ability to be easily reconfigured and is expected to fly on the Spacelab Life Sciences-1. The acceleration characterization and analysis project describes the Shuttle acceleration environment and disturbance mechanisms, and facilitates the implementation of the microgravity research program.
The width-tapered cantilever beam method is used to quantify the debond energy (adhesion) of encapsulant and backsheet structures of 32 modules collected from the field. The collected population of modules contains both those that have remained intact and those with instances of either or both encapsulant and backsheet delamination. From this survey, initial threshold values (an adhesion value above which a module should remain intact throughout its lifetime) for encapsulant and backsheet interfaces are proposed. For encapsulants this value is ~ 160J/m 2 and for backsheets ~ 10J/m 2 . Here, it is expected that these values will continue to be refined and evolve as the width-tapered cantilever beam method gets adopted by the PV industry, and that they may aid in the future improvement of accelerated lifetime tests and the development of new, low-cost materials.
The Materials Science Laboratory-2 (MSL-2) mission flew the Marshall Space Flight Center-developed Linear Triaxial Accelerometer (LTA) on the Space Transportation System (STS) 61-C Shuttle mission launched January 21, 1986. Flight data were analyzed to verify the quietness of the MSL carrier and to characterize the acceleration environment for future MSL users. The MSL was found to introduce no significant experiment acceleration; and the effects of crew treadmill exercise, Orbiter vernier engine firings, and other routine flight occurrences were established. The LTA was found to be well suited for measuring nominal to very quiet STS acceleration levels at frequencies below 50 Hz. Special processing was used to examine the low-frequency spectrum and to establish the effective rms amplitude associated with dominant frequencies.
Current and future high-power accelerators put severe requirements on materials used for target and beam windows and target facilities have been recognized as a critical challenge in development of future particle accelerators. In accelerators, window and target materials are exposed to extreme conditions, which include bombardment with very high energy protons (1- 100 GeV) and thermomechanical shock waves. Radiation can cause direct damage in the material, and it leads to production of transmutation products (especially helium), both phenomena having a potential adverse effect on the stability and durability of the target/window material. At high enough temperatures, He can aggregate to form gas bubbles, which in turn cause significant dimensional changes (swelling), enable easy crack propagation, and eventually cause failure by fracture. On the other hand, if the temperature is too low, radiation damage accumulates in the form of internal defects (e.g., dislocations), leading to hardening and a decreased ductility of the material. In this project, we will focus on beryllium since it is considered to be one of the candidate materials for beam windows and targets in the next-generation proton accelerators, e.g., the Long Baseline Neutrino Facility (LBNF). Radiation effects in Be have been studied in the context of nuclear fusion reactor applications. However, key differences exist between reactor and accelerator conditions, including neutron vs. proton irradiation, continuous vs. pulsed beam flux, much higher energies of bombarding particles in accelerators, and higher operating temperatures for typical reactors. For example, the impact of beam pulsing on the radiation damage and the He bubble kinetics is largely unknown. While results obtained on Be from fusion research might not be directly transferrable to understanding target materials, there is an opportunity to bring state-of-the-art tools from materials research in nuclear reactors to aid design of target and beam window materials in high-power accelerators. To this end, the overarching goal of this project are to develop an experimentally-validated computational framework capable of predicting radiation damage evolution in beryllium relevant to beam window and target conditions, focusing on He bubble formation and growth as a function of irradiation temperature. Our model will be based on the cluster dynamics formalism, where size distribution of defects and He bubbles is simulated as a function of time, temperature, and radiation dose. Parameters for the model will be taken from published experiments and from high-fidelity atomistic simulations proposed in this project. In addition, we will carry out a series of targeted ex-situ and in-situ dual-beam experiments using low-energy protons to provide critical data for validation of the model on the effects of radiation on He clustering, He bubble distribution, and dislocation loop density/size in proton irradiated Be.