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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.

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DOE OSTI2021

Monte Carlo Driven MDI Optimization at a Muon Collider

A Muon Collider represents a very interesting possibility for a future machine to explore the energy frontier in particle physics. However, to reach the needed luminosity, beam intensities of the order of 10⁹–10¹² muons per bunch are needed. In this context, the Beam-Induced Background must be taken into account for its effects on magnets and detector. Several mitigation strategies can however be conceived. In this view, it is of crucial importance to develop a flexible tool that allows to easily reconstruct the machine geometry in a Monte Carlo code, allowing to simulate in detail the interaction of muon decay products in the machine, while being able to change the machine optics itself to find the best configuration. In this contribution, a possible approach to such a purpose is presented, based on FLUKA for the Monte Carlo simulation and on LineBuilder for the geometry reconstruction. Results based on the 1.5 TeV machine optics developed by the MAP collaboration are discussed, as well as a first approach to possible mitigation strategies.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider

A 10 TeV Muon Collider is a compelling candidate for a future energy-frontier facility, offering unprecedented opportunities to explore the fundamental laws of particle physics. Muon decays in the collider ring produce intense beam-induced background (BIB) that can overwhelm detector occupancy and exceed readout bandwidth constraints. We investigate the potential of on-detector Machine Learning for BIB rejection in the vertex detector, exploiting pixel cluster shapes to distinguish background from collision products. We study three classes of lightweight neural-network architectures, and evaluate their implementation feasibility using high-level synthesis. Selected architectures achieve 88 to 90% data reduction at 99% signal efficiency, while requiring hardware resources compatible with potential ASIC implementation. These results demonstrate the potential of performing substantial BIB rejection directly in the pixel readout, providing a strategy for meeting the tracker readout requirements at a future Muon Collider.

Abadjiev, Daniel [Chicago U.]↗
DOE OSTI2021

Self-consistent PIC simulations of ultimate space charge compensation with electron lenses

Further progress of fundamental particle physics requires high intensity and high brightness of accelerated proton and ion beams. This goal is essential for the FAIR hadron beams at GSI, for the neutrino production at the facilities such as Fermilab and JPARC, and for the Large Hadron Collider luminosity at CERN. One of the most formidable obstacles toward that goal is the beam's own space charge, whose forces cause beam emittance growth, losses and lifetime degradation. Typically, such effects become intolerable when the space charge tune-shift parameter Δ QSC exceeds ~ - (0.25–0.5). To reduce these detrimental effects, it was suggested to use electron lenses to compensate the space charge forces. Furthermore, this paper reports on detailed particle-in-cell space charge and electron lens compensation simulations for extremely intense proton bunches whose space charge tune-shift parameter exceeds -1.0. Different scenarios were evaluated based on reduction in the emittance growth and particle loss at a 4σ aperture. Further, we investigate phenomena and issues related to the focusing lattice errors, importance of the transverse and longitudinal matching of the electron beam profiles to the proton ones, and vary the strength and the number of the electron lenses distributed around a circular machine to optimize the reduction of harmful space charge effects.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

The BDX experiment at Jefferson Laboratory

The Beam Dump Experiment (BDX) at Jefferson Laboratory (JLab) is an electron-beam thick-target experiment to search for Light Dark Matter (LDM) particles in the MeV-GeV mass range. BDX will exploit the high-intensity 10.6 GeV e^-e ? beam from CEBAF accelerator impinging on the beam dump of experimental Hall-A, collecting up to 10^{22}10 22 electrons-on-target (EOT) in a few years time. Any LDM particle produced by the interaction of the primary e^-e ? beam with the beam dump will be detected by measuring their scattering inside the BDX detector, an electromagnetic calorimeter surrounded by an hermetic veto system, which is to be installed in a dedicated underground facility, located 20 m downstream. Thanks to the large detection efficiency and background rejection capabilities, BDX will be able to explore a so-far unknown region in the LDM parameter space, improving current exclusion limits by one order of magnitude in case of a null observation. In preparation to the full experiment, a small-scale version called BDX-MINI, has been built and operated at JLab with a lower energy beam. Despite the small interaction volume, the large accumulated charge of 2.2\times10^{21}2.2×10 21 EOT allowed for the BDX-MINI measurement to set competitive exclusion limits on the LDM parameters space, comparable to those reported by larger-scale efforts.

Celentano, A.↗
DOE OSTI2020

Improving the Event Data Server of the Fermilab Test Beam Data Acquisition System (OTSDAQ)

The Fermilab Test Beam Facility (FTBF) is a high-energy test beam used for precision tests of high-energy physics detectors. The FTBF has two beams lines, MTest and MCenter, which provides a variety particle types such as proton beam and secondary beams with muons, electrons, and kaons. The beam is variable in energy and intensity, and a target can be inserted into the beam to convert the protons to other particle types for users.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Simulations of future particle accelerators: issues and mitigations

The ever increasing demands placed upon machine performance have resulted in the need for more comprehensive particle accelerator modeling. Computer simulations are key to the success of particle accelerators. Many aspects of particle accelerators rely on computer modeling at some point, sometimes requiring complex simulation tools and massively parallel supercomputing. Examples include the modeling of beams at extreme intensities and densities (toward the quantum degeneracy limit), and with ultra-fine control (down to the level of individual particles). In the future, adaptively tuned models might also be relied upon to provide beam measurements beyond the resolution of existing diagnostics. Much time and effort has been put into creating accelerator software tools, some of which are highly successful. However, there are also shortcomings such as the general inability of existing software to be easily modified to meet changing simulation needs. In this paper possible mitigating strategies are discussed for issues faced by the accelerator community as it endeavors to produce better and more comprehensive modeling tools. This includes lack of coordination between code developers, lack of standards to make codes portable and/or reusable, lack of documentation, among others.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Accelerator Real-time Edge AI for Distributed Systems (READS) (Proposal)

Over the last decade, Machine Learning (ML) technologies have slowly made their way into the accelerator community. Rapid advances in recent years in deep learning, particularly reinforcement learning for control system applications and the accessibility of deep learning in embedded hardware, have generated renewed interest and spawned a number of applications. The Fermilab Accelerator Complex, shown in Fig. 1, has provided High Energy Physics (HEP) experiments with proton beams for nearly fifty years. The current focus of the laboratory is its world-class experimental program at the intensity frontier. While increasing beam intensity certainly presents its own challenges, preserving beam size while minimizing beam losses – particles lost through interactions with the beam vacuum pipe – turns out to be, in many ways, the main challenge. The accelerator is controlled via a complex system of hundreds of thousands of devices. Enabling fine tuning and real-time optimization of their parameters using ML methods and stepping beyond experience-based reasoning of human operators are key to the success of future intensity upgrades. Our objective will be to integrate ML into accelerator operations and furthermore, provide an accessible framework, which can also be used by a broad range of other accelerator systems with dynamic tuning needs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Dynamic evolution of ultracold electron beams from nanostructured cathodes

Dynamic Coulomb expansion of dense, nonuniform particle beamlets can lead to causticlike formations in the charge density profile of the resultant beam. Such features only occur for cold beams where thermal diffusion of the particles is minimized. These phenomena have been observed experimentally for select cases with ion beams but not yet for electron beams as the electron dynamics occur over far faster timescales. We simulate the interaction of electron bunches while varying the initial transverse temperature and density profiles to determine the thresholds that characterize this pattern formation. Furthermore, we extend the simulations to geometric arrangements that are more pertinent to the nanotip cathode application and consider the effects of asymmetries and the impact of a low-density halo on the overall process. The simulations are conducted using a previously documented high-fidelity algorithm for collisional particle dynamics. These studies identify the temperature and density thresholds required for caustic formation and provide guidance for optimizing the design of nanoscale structured cathodes. We also demonstrate that the correlation between the final density patterns and the underlying beam parameters enables a virtual diagnostic tool for measuring ultralow emittance beams solely based on a transverse charge density profile.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Linear beam stability in periodic focusing systems: Krein signature and band structure

The general question of how a beam becomes unstable has been one of the fundamental research topics among beam and accelerator physicists for several decades. In this study, we revisited the general problem of linear beam stability in periodic focusing systems by applying the concepts of Krein signature and band structure. We numerically calculated the eigenvalues and other associated characteristics of one-period maps, and discussed the stability properties of single-particle motions with skew quadrupoles and envelope perturbations in high-intensity beams on an equal footing. In particular, an application of the Krein theory to envelope instability analysis was newly attempted in this study. Finally, the appearance of instabilities is interpreted as the result of the collision between eigenmodes of opposite Krein signatures and the formation of a band gap.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Thermal Analysis of Thermal Shield for PIP-II SSR1 Cryomodule

Fermilab's PIP-II project plans to enhance the onsite accelerator into the world's most intense neutrino beam, bringing Fermilab into the next generation of particle accelerators. The five different types of cryomodules are vital parts of the PIP-II project and each requires numerous engineering analyses to prove operation. For example, the thermal shield of the Single Spoke Resonator-1 (SSR1) cryomodule required a thermal analysis to ensure the design was operating as intended before fabrication commenced. The thermal shield is cooled by the supercritical helium inside of the extrusion and must remain between 45 and 80 Kelvin. The main purpose of the thermal shield is to intercept different high heat loads and thermal radiation and prevent them from reaching the more sensitive internal components. Finite Element Analysis (FEM) was used to determine temperature estimates of the thermal shield based on the location of a variety of heat sources. Results determined temperatures exceeding the design limit around the crucial component of the current leads. Due to superconducting properties, the areas around the current leads cannot surpass 68 Kelvin. Different solutions to the critical high-temperature areas will be proposed. These solutions range from changing the properties of the helium flow to designing an attachment for the thermal shield. Each proposition works as a viable solution, however, each proposition also has its own set of advantages and disadvantages that are described for further design consideration.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Subsurface Spectroscopy in Heterogeneous Materials Using Self-Healing Laser Beams

Self-healing optical beams are a class of propagation modes that can recover their beam shapes after distortion or partial blockage. This self-healing property makes them attractive for use in applications involving turbid media as they can—in theory—penetrate further into these materials than standard Gaussian beams. In this paper, we characterize the propagation of two different self-healing beams (Bessel and Airy) through a solid scattering material with different scatterer concentrations and find that both beams do recover after scattering for samples below a threshold scatterer concentration. Additionally, we test the applicability of both beam shapes for improved sub-surface spectroscopy in heterogeneous materials using fluorescent particles and find that there is an average fluorescence intensity enhancement of 1.3× using self-healing beams versus a standard Gaussian beam.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

De-noising drift chambers in CLAS12 using convolutional auto encoders

Modern Nuclear Physics experimental setups run experiments with higher beam intensity resulting in increased noise in detector components used for particle track reconstruction. Increased uncorrelated signals (noise) result in decreased particle reconstruction efficiency. In this paper, we investigate the usage of Machine Learning, specifically Convolutional Neural Network Auto-Encoders (CAE), for de-noising raw hits from drift chambers in the CLAS12 detector. To the best of our knowledge, this is the first time CAE is employed to perform such an operation in this field. During the de-noising phase, it is important to remove as much noise as possible while retaining the valid hits to avoid losing crucial information about the experiment. Here, we show that using CAE, it is possible to remove noise hits while retaining up to 94% of valid tracks for a beam current of 110nA while for lower beam currents (45-55nA), we get up to 98% efficiency. Studies on experimental conditions with increasing noise show that CAE performs better than conventional tracking algorithms in isolating hits belonging to tracks. Specifically, the de-noising algorithm results in tracking efficiency improvements greater than 15%, in real data production procedures with nominal conditions, and up to two times better efficiency in synthetically generated data with high luminosity conditions (90-110nA), indicating that machine learning can lead to significantly shorter times for conducting physics experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Coupled Bunch Instabilities Growth in the Fermilab Booster during Acceleration Cycle

Currently, Fermilab Booster accelerates ~4.5E12 protons per pulse (ppp) in 81 bunches from 400 MeV to 8 GeV at 15 Hz to provide beam to multiple HEP experiments and is being upgraded to handle higher beam intensity >6.7E12 ppp at a repetition rate of 20 Hz. In the current mode of operation, we control longitudinal coupled bunch instabilities (LCBI) using combination of passive and active dampers. The issues with LCBI are expected to worsen at higher beam intensities. Here, we would like to investigate at what time in the beam cycle a particular mode is going to originate and how much it contributes at a different time of the acceleration cycle using the collected wall current monitor data from injection to extraction. Also, measure growth rates as a function of beam intensity for some dominant modes. Finally, this paper presents results from the observed LCBI analysis for the highest available beam intensities, unexpected findings, and future plans to mitigate the LCBI at higher beam intensities

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Track Matching in the DUNE Near Detectors

The Deep Underground Neutrino Experiment (DUNE) is an international particle physics experiment looking answer some of the largest unanswered questions in neutrino physics. DUNE uses a high power neutrino beam produced at Fermi National Accelerator Laboratory (Fermilab), and consists of a near detector (ND) also located at Fermilab and a far detector (FD) 1300 km away at the Sanford Underground Research Facility (SURF) in South Dakota. In the first phase of the experiment, the ND complex will contain a modular liquid argon TPC (ND-LAr) and a solid scintillator-based muon spectrometer (TMS), in addition to a beam monitoring detector (SAND) and systems for moving ND-LAr and TMS away from the neutrino beam axis (PRISM). A prototype of ND-LAr, the 2x2 demonstrator, alongside a solid scintillator muon tagger provided by repurposed MINERvA planes, has been built and taken data at Fermilab. For analyses with the ND, connecting particle tracks (such as muons) that exit the liquid argon active volume into the solid scintillator muon detector can improve particle identification and energy reconstruction, and alleviate pileup due to the intense beam. To match tracks between detectors during reconstruction, we have explored using Graph Neural Networks (GNNs) to connect tracks segments between the liquid argon detector region and the solid scintillator detector planes. We have trained a GNN on reconstructed simulated data from the 2×2 demonstrator and repurposed MINERvA planes. We will evaluate its performance and then train a similar network on reconstructed ND-LAr and TMS simulations.

Xing, Daniel [U. Colorado, Boulder]↗
DOE OSTI2024

Harmonic Slip-Stacking

Slip-stacking is a particle acceleration technique used at Fermilab to increase Main Injector intensity by combining two beams at the cost of longitudinal beam quality.In a conventional beam line, a single beam is typically accelerated and delivered to the experimental area without combining it with another beam

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Accelerator Neutrinos

Neutrino beams generated by particle accelerators are essential for probing fundamental physics. This presentation will examine the creation of high-intensity, well-collimated neutrino beams, crucial for long-baseline experiments like DUNE and NOvA. These experiments are pushing proton beam power to multi-MW levels and utilizing large-scale detectors to overcome the challenge of limited event statistics. At LBNF, the DUNE experiment will rigorously test the three-neutrino flavor model and explore CP violation by analyzing oscillation signatures in high-intensity νμ(νμˉ)νμ​(νμ​ˉ​) to νe(νeˉ)νe​(νe​ˉ​) beams. We'll explore the technical complexities of beamline components, the drive towards higher beam powers, and the strategies for measuring and managing neutrino flux. The presentation will also cover advancements in neutrino beam instrumentation and efforts to enhance beam precision, which are key to achieving the next generation of multi-megawatt accelerator facilities. By reviewing past achievements and future prospects, this talk aims to provide a clear overview of the current state and future potential of neutrino beam technology.

Ganguly, Sudeshna↗