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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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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Beam Performance of the Positron Transport Line for CEBAF Positron Upgrade

The Low Energy Recirculator Facility (LERF) at Jefferson Lab, formerly operated for the Free-Electron Laser program, has been proposed as the injector complex for the planned 12 GeV CEBAF positron upgrade (Ce+BAF), with an additional pathway to support a potential 22 GeV CEBAF electron upgrade. In this configuration, LERF would generate and pre-accelerate positrons to 123 MeV, matching the present injection energy into the North Linac. Due to the relatively large emittance expected from the positron source, a comprehensive acceptance study has been performed from LERF through the CEBAF recirculating linacs and beam transport lines to the experimental halls. The objective is to establish the positron phase-space acceptance and provide design feedback to the positron production and capture systems. Furthermore, given CEBAF’s capability to deliver highly polarized beams, spin-tracking simulations have been carried out including magnet imperfections, alignment errors, and synchrotron-radiation–induced energy spread. Particular attention is given to the evolution of the spin tune and the corresponding depolarization mechanisms along the beam delivery path, especially for providing longitudinal polarization at the experimental halls. These results inform injector design choices and assess the overall feasibility of delivering high-polarization positron beams in CEBAF.

Ogur, S. [Thomas Jefferson National Accelerator Fa↗
DOE OSTI2025

Preliminary study of auto-differentiation algorithm in beam dynamics with stochastic process

Modern particle accelerator optimization requires sophisticated computational methods to address the inherently stochastic nature of beam dynamics. This research develops a framework applying AD to SDEs that specifically addresses beam dynamics challenges in particle accelerators, focusing on accurately modeling and optimizing beam behavior in regimes dominated by stochastic processes. By incorporating key physical phenomena such as synchrotron radiation, wakefield effects, and quantum excitation, the framework aims to provide auto differentiation on the figure of merit of the phase space evolution and beam dynamics. The methodology will enable effective optimization method in a dynamic system with stochastic process.

Accelerator Physics↗
DOE OSTI2026

AI-driven neutrino diagnostics and radiation-hard beam instrumentation for next-generation neutrino experiments

The Long Baseline Neutrino Facility (LBNF) at Fermilab will deliver a high-intensity, multi-megawatt neutrino beam to the Deep Underground Neutrino Experiment (DUNE), enabling precision tests of the three-neutrino paradigm, CP violation searches, neutrino mass ordering determination, and supernova neutrino studies. To accelerate DUNE’s physics reach and ensure robust beam operations, we propose an integrated AI-driven framework with real-time diagnostics and radiation-hardened instrumentation. At its core is a Real-Time Beam Integrity Monitor using a physics-informed Digital Twin. By reconstructing pion phase space from muon profiles and exploiting magnetic horn optic linearity, it enables spill-by-spill beam correction and flux stabilization. By using this approach, flux-related systematics could be reduced from 5% to 1%, potentially accelerating the discovery of CP violations by four to six years. Complementing this, a US–Japan R&D effort will deploy a LAPPD-based muon monitor in the NuMI beamline. ToF measurements can be acquired with picosecond precision using this radiation-hard system, enhancing sensitivity to horn chromatic effects. Simulations confirm strong response to these effects. ML models predict beam quality and horn current to sub-percent accuracy from muon data, enhancing anomaly detection and stability. This scalable, AI-enabled strategy improves beam fidelity, reduces systematics, and sets a new standard for high-power accelerator operations.

Ganguly, Sudeshna [Fermilab] (ORCID:00000003163482↗
DOE OSTI2025

Ferromagnets, a new anomaly, instantons, and (noninvertible) continuous translations

We discuss a large class of classical field theories with continuous translation symmetry. In the quantum theory, a new anomaly explicitly breaks this translation symmetry to a discrete symmetry. Furthermore, this discrete translation symmetry is extended by a d – 2-form global symmetry. All these theories can be described as U(1) gauge theories where Gauss law states that the system has nonzero charge density. Special cases of such systems can be phrased as theories with a compact phase space. Examples are ferromagnets and lattices in the lowest Landau level. In some cases, the broken continuous translation symmetry can be resurrected as a noninvertible symmetry. We clarify the relation between the discrete translation symmetry of the continuum theory and the discrete translation symmetry of an underlying lattice model. Our treatment unifies, clarifies, and extends earlier works on the same subject.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Sparks in the dark

This study presents a novel method for the definition of signal regions in searches for new physics at collider experiments. By leveraging multi-dimensional histograms with precise arithmetic and utilizing the SparkDensityTree library, it is possible to identify high-density regions within the available phase space, potentially improving sensitivity to very small signals. Inspired by a search for dark mesons at the ATLAS experiment, CMS open data is used for this proof-of-concept intentionally targeting an already excluded signal. Signal regions are defined based on density estimates of signal and background. These preliminary regions align well with the physical properties of the signal while effectively rejecting background events.

Gudnadottir, Olga Sunneborn [Uppsala Univ. (Sweden↗
DOE OSTI2025

Normalizing flows for high-dimensional detector simulations

Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance for fast calorimeter shower simulations with increasing phase space dimension. We use fast and expressive coupling spline transformations applied to the CaloChallenge datasets. In addition to the base flow architecture we also employ a VAE to compress the dimensionality and train a generative network in the latent space. We evaluate our networks on several metrics, including high-level features, classifiers, and generation timing. Our findings demonstrate that invertible neural networks have competitive performance when compared to autoregressive flows, while being substantially faster during generation.

Ernst, Florian↗
DOE OSTI2025

A microscopic realization of dS$_3$

We propose a precise duality between pure de Sitter quantum gravity in 2+1 2 + 1 dimensions and a double-scaled matrix integral. This duality unfolds in two distinct aspects. First, by carefully quantizing the gravitational phase space, we arrive at a novel proposal for the quantum state of the universe at future infinity. We compute cosmological correlators of massive particles in the universe specified by this wavefunction. Integrating these correlators over the metric at future infinity yields gauge-invariant observables, which are identified with the string amplitudes of the complex Liouville string [S. Collier et al., arXiv: 2409.17246]. This establishes a direct connection between integrated cosmological correlators and the resolvents of the matrix integral dual to the complex Liouville string, thereby demonstrating one aspect of the dS _3 3 /matrix integral duality. The second aspect concerns the cosmological horizon of the dS static patch and the Gibbons-Hawking entropy it is conjectured to encode. We show that this entropy can be reproduced exactly by counting the entries of the matrix.

Collier, Scott (ORCID:0000000286476653)↗
DOE OSTI2025

Generative unfolding with distribution mapping

Machine learning enables unbinned, highly-differential cross section measurements. A recent idea uses generative models to morph a starting simulation into the unfolded data. We show how to extend two morphing techniques, Schrödinger Bridges and Direct Diffusion, in order to ensure that the models learn the correct conditional probabilities. This brings distribution mapping (DM) to a similar level of accuracy as the state-of-the-art conditional generative unfolding methods. Numerical results are presented with a standard benchmark dataset of single jet substructure as well as for a new dataset describing a 22-dimensional phase space of Z+2 -jets.

Butter, Anja↗
DOE OSTI2026

Iterative HOMER with uncertainties

We present iHOMER, an iterative version of the HOMER method to extract Lund fragmentation functions from experimental data. Through iterations, we address the information gap between latent and observable phase spaces and systematically remove bias. To quantify uncertainties on the inferred weights, we use a combination of Bayesian neural networks and uncertainty-aware regression. We find that the combination of iterations and uncertainty quantification produces well-calibrated weights that accurately reproduce the data distribution. A parametric closure test shows that the iteratively learned fragmentation function is compatible with the true fragmentation function.

Butter, Anja [Heidelberg Univ. (Germany); Sorbonne↗
DOE OSTI2024

Final Report for Center for Tokamak Transient Simulations at USU

Providing plasma fluid codes like NIMROD with continuum drift kinetic (CDK) physics that is quantitatively valid and computationally feasible throughout the spatial domain is difficult. Work at Utah State University (USU), in collaboration with the Center for Tokamak Transient Simulations (CTTS), focused on applying CDK closures in disruption-related calculations. Three examples where kinetic physics is paramount are (1) the electron stress tensor closure in Ohms law for accurately describing neoclassical tearing mode (NTM) evolution, (2) runaway electron (RE) density (nRE) and current (jRE) moments in NIMROD’s extended MHD model for self-consistent evolution of RE populations during disruptions and, (3) energetic ion effects on a myriad of MHD instabilities. While NTM simulations and continuum and PIC approaches to energetic ions in NIMROD have been a major goals of USU’s closure work for several years, the development of self-consistent CDK RE capability in NIMROD was started and extended considerably during the CTTS effort. Some goals of CDK RE in NIMROD are to explore the effects of the 2D relativistic phase space in 4D simulations and compare with NIMROD’s fluid RE model. Four publications and two PhD theses came out of the USU CTTS effort.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Complete 6D Tracking of a Single Electron in the IOTA Ring

We present the results of the first experiments on 6-dimensional phase-space tracking of a single electron in a storage ring, using a linear multi-anode photomultiplier tube for simultaneously measuring transverse coordinates and arrival times of synchrotron-radiation pulses. This technology makes it possible to fully reconstruct turn-by-turn positions and momentums in all three planes for a single particle. Complete experimental particle tracking enables the first direct measurements of dynamical properties, including invariants, amplitude and energy dependence of tunes with exceptional precision, and chaotic behavior.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Machine-Assisted Discovery of Integrable Symplectic Mappings

Integrable systems possess a hidden symmetry associated with the existence of conserved quantities known as integrals of motion. These systems play an important role in understanding general dynamics in accelerators and have potential for future designs. This work will cover two automated methods for finding integrable symplectic maps of the plane. The first algorithm is based on the observation that the evolution of an integrable system in phase space is confined to a lower-dimensional submanifold of a specific type. The second algorithm relies on an analysis of dynamical variables. Both methods rediscover some of the famous McMillan-Suris integrable mappings and ultra-discrete Painlev\'e equations. Over 100 new integrable families are presented and analyzed, some of which are isolated in the space of parameters, while others are families with one parameter (or the ratio of parameters) being either continuous or discrete. In addition, the newly discovered maps are related to a general 2D symplectic map through the use of discrete perturbation theory. A method is proposed for constructing smooth near-integrable dynamical systems based on mappings with polygon invariants.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Experimental Verification of Integrability in a Danilov-Nagaitsev Lattice using Machine Learning

In non-linear optics, achieving integrability can enhance the dynamic aperture in storage rings. We analyze turn-by-turn phase-space data from our Danilov-Nagaitsev lattice implementation at Fermilab's Integrable Optics Test Accelerator using machine learning. \textit{AI Poincar\'e} estimates conserved quantities from experimental data without prior knowledge of the invariant structure, showing qualitative agreement with theoretical predictions. Additionally, one of the two learned invariants exhibits comparable or better conservation compared to known theoretical expressions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Linac_Gen: Integrating Machine Learning and Particle-in-Cell Methods for Enhanced Beam Dynamics at Fermilab

Here, we introduce Linac_Gen, a tool developed at Fermilab, which combines machine learning algorithms with Particle-in-Cell methods to advance beam dynamics in linacs. Linac_Gen employs techniques such as Random Forest, Genetic Algorithms, Support Vector Machines, and Neural Networks, achieving a tenfold increase in speed for phase-space matching in Linacs over traditional methods, through the use of genetic algorithms. Crucially, Linac_Gen's adept handling of 3D field maps elevates the precision and realism in simulating beam instabilities and resonances, marking a key advancement in the field. Benchmarked against established codes, Linac_Gen demonstrates not only improved efficiency and precision in beam dynamics studies but also in the design and optimization of Linac systems, as evidenced in its application to Fermilab's PIP-II Linac project. This work represents a notable advancement in accelerator physics, marrying ML with PIC methods to set new standards for efficiency and accuracy in accelerator design and research. Linac_Gen exemplifies a novel approach in accelerator technology, offering substantial improvements in both theoretical and practical aspects of beam dynamics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

High-throughput combinatorial approach expedites the synthesis of a lead-free relaxor ferroelectric system

Developing novel lead-free ferroelectric materials is crucial for next-generation microelectronic technologies that are energy efficient and environment friendly. However, materials discovery and property optimization are typically time-consuming due to the limited throughput of traditional synthesis methods. In this work, we use a high-throughput combinatorial synthesis approach to fabricate lead-free ferroelectric superlattices and solid solutions of (Ba 0.7 Ca 0.3 )TiO 3 (BCT) and Ba(Zr 0.2 Ti 0.8 )O 3 (BZT) phases with continuous variation of composition and layer thickness. High-resolution x-ray diffraction (XRD) and analytical scanning transmission electron microscopy (STEM) demonstrate high film quality and well-controlled compositional gradients. Ferroelectric and dielectric property measurements identify the “optimal property point” achieved at the composition of 48BZT–52BCT. Displacement vector maps reveal that ferroelectric domain sizes are tunable by varying {BCT–BZT} N superlattice geometry. This high-throughput synthesis approach can be applied to many other material systems to expedite new materials discovery and properties optimization, allowing for the exploration of a large area of phase space within a single growth.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Measuring the Multi-Neutron Antineutrino Cross Section at Low Charged Hadron Energy in MINERvA

Current and future accelerator neutrino oscillation experiments need neutrino interaction models with smaller systematic uncertainties to resolve much of delta CP phase space. Final state interactions (FSI) and scattering off of correlated nuclei (2p2h) are poorly understood processes that currently contribute large uncertainties to leading models. These processes have proven difficult to study because they often produce relatively low energy nucleons. Protons up to about 100 MeV are below the detection threshold of some accelerator neutrino detectors, and neutrons are usually discounted as undetectable. This poster presents a measurement of the multi-neutron antineutrino cross section at low available energy using the MINERvA detector at Fermilab. This interaction channel is particularly sensitive to FSI and 2p2h interactions. A sideband-driven background constraint that greatly reduces uncertainties on the result will be presented. The measured cross section is compared to GENIE v3 models with different FSI treatments and the SuSA model's 2p2h predictions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Capture Cavities for the CW Polarized Positron Source Ce+BAF

The initial design of the capture cavities for the continuous wave (CW) polarized positron beams at Jefferson Lab (Ce+BAF) is presented. A chain of standing wave multi-cell copper cavities inside a solenoid tunnel are selected to bunch/capture positrons. The cavity design strategy is presented to accommodate constrains from the large phase distribution of the incident beams, RF power, radiation and RF heating, beam loading, etc. to improve the capture efficiency. A matrix of design parameters' range are given for future system optimization when the capture cavities are considered together with other sub-systems and beam dynamics. The contents will also be useful for other CW cavity design for beams with large phase space distribution.

Wang, Shaoheng↗
DOE OSTI2024

The Light Dark Matter Experiment (LDMX)

The constituents of dark matter are still unknown, and the viable possibilities span a very large mass range. Specific scenarios for the origin of dark matter sharpen the focus to within about an MeV to 100 TeV. Most of the stable constituents of known matter have masses in this lower range, and a thermal origin for dark matter works in a simple and predictive manner in this mass range as well. If there is an interaction between light DM and ordinary matter, as there must be in the case of a thermal origin, then there necessarily is a production mechanism in accelerator-based experiments. The Light Dark Matter eXperiment (LDMX) is a planned electron-beam fixed-target missing-momentum experiment at SLAC that has unique sensitivity to light DM in the sub-GeV mass range. Although optimized for a missing momentum technique, LDMX is effectively a fully instrumented beam dump experiment, making it possible to search for visibly decaying signatures of dark sector particles. This would provide another outlet for LDMX to probe complementary regions of dark matter phase space for a variety of models. This contribution will give an overview of the theoretical motivation for LDMX, the main experimental challenges and how they are addressed, as well as projected sensitivities in comparison to other experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗