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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Electroexcitation of nucleon resonances of the [ 70 , 1 - ] multiplet in a light-front relativistic quark model

We utilize the light-front relativistic quark model to predict the 3q core contribution to the electroexcitation of nucleon resonances of the [70,1 – ] multiplet on the proton and neutron at Q 2 < 5 GeV 2 . The investigation is motivated by new experimental data from continuous electron beam accelerator facility large acceptance spectrometer on meson electroproduction for a wide range of the hadronic invariant mass including the full third nucleon resonance region up to √s = 1.8 GeV. For the states N(1520)3/2 – , N(1535)1/2 – , and N(1675)5/2 – , experimental results on the electroexcitation amplitudes on the proton are available for a wide range of Q 2 . Lastly, this allowed us also to quantify the expected meson-baryon contributions to these amplitudes as a function of Q 2 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Diffusion model approach to simulating electron-proton scattering events

Generative artificial intelligence is a fast-growing area of research offering various avenues for exploration in high-energy nuclear physics. In this work, we explore the use of generative models for simulating electron-proton collisions relevant to experiments like the Continuous Electron Beam Accelerator Facility and the future Electron-Ion Collider (EIC). These experiments play a critical role in advancing our understanding of nucleons and nuclei in terms of quark and gluon degrees of freedom. The use of generative models for simulating collider events faces several challenges such as the sparsity of the data, the presence of global or eventwide constraints, and steeply falling particle distributions. In this work, we focus on the implementation of diffusion models for the simulation of electron-proton scattering events at EIC energies. Our results demonstrate that diffusion models can reproduce relevant observables such as momentum distributions and correlations of particles, momentum sum rules, and the leading electron kinematics, all of which are of particular interest in electron-proton collisions. Although the sampling process is relatively slow compared to other machine-learning architectures, we find diffusion models can generate high-quality samples. We foresee various applications of our work including inference for nuclear structure, interpretable generative machine learning, and searches of physics beyond the Standard Model. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Using inertial fusion implosions to measure the T + He 3 fusion cross section at nucleosynthesis-relevant energies

Light nuclei were created during big-bang nucleosynthesis (BBN). Standard BBN theory, using rates inferred from accelerator-beam data, cannot explain high levels of 6 Li in low-metallicity stars. Using high energy-density plasmas we measure the T( 3 He,γ) 6 Li reaction rate, a candidate for anomalously high 6 Li production; we find that the rate is too low to explain the observations, and different than values used in common BBN models. In conclusion, this is the first data directly relevant to BBN, and also the first use of laboratory plasmas, at comparable conditions to astrophysical systems, to address a problem in nuclear astrophysics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Continuous Data Acquisition for Liquid Argon Time Projection Chamber Neutrino Detectors Using FPGA-Based Real-Time Compression Algorithms

Liquid argon time projection chambers (LArTPCs) have been proposed as neutrino detectors that combine both large sizes to maximize the number of neutrino interactions and detailed recording of the interaction. The readout of thousands of channels at MHz sampling rates produce images of the neutrino-nucleus interaction with millimeter-scale resolution, enabling the identification of the resulting particles and offering multiple handles to measure their energies at the expense of large data rates. Continuous acquisition of the LArTPC data is required to enable the use of high-latency triggers for capturing non-accelerator-beam events. We describe the case of the continuous readout of the MicroBooNE LArTPC, that grants the possibility of acquiring the neutrino burst from a supernova using the SNEWS (Supernova Early Warning System) alert as delayed trigger. The continuous data acquisition is accomplished by using real-time compression algorithms (zero suppression and Huffman compression) implemented in an FPGA in the readout electronics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Study on Conduction Cooling of Superconducting Magnets for the ILC Main Linac

In the main linac of the International Linear Collider (ILC), superconducting magnets for beam focusing and steering will be located periodically in superconducting RF (SRF) cavity string for beam acceleration in common cryomodules. A concept of conduction cooling of the combined-functioned, splittable superconducting magnets has been proposed and investigated to adapt much different features and to meet different requirements for the superconducting magnet and SRF cavity in fabrication, assembly, and operation. It is required to integrate the superconducting magnet after the SRF cavity string assembly which completed under an ultra-clean environment. The magnet must be conductively cooled down through thermal links to a liquid helium supply pipe. According to this concept, a model magnet development was carried out in cooperation with Fermilab and KEK, and has been demonstrated in KEK superconducting RF test facility (STF). In addition, an important issue has been recently identified. High gradient SRF cavities naturally emit field emission electron flux from the inner surface, so-called dark current. It may pass through the subsequent SRF cavity string and penetrate into the superconducting magnets placed downstream. It may heat up the superconducting coils, and may cause a quench. Therefore, further study on reliable conduction cooling and to secure the superconducting magnet operation with a keeping sufficient safety margin is quite essential. In this paper, we report the installation, the improvement achieved in STF, and the R&D progress in the study on the conduction cooling of the superconducting magnet for the ILC main linac.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

Calibration of cathode strip gains in multiwire drift chambers of the GlueX experiment

A technique for calibrating cathode strip gains in multiwire drift chambers of the GlueX experiment is described. The accuracy of the technique is estimated based on Monte Carlo generated data with known gain coefficients in the strip signal channels. One of the four detector sections has been calibrated using cosmic rays. Results of drift chamber calibration on the accelerator beam upon inclusion in the GlueX experimental setup are presented.

Berdnikov, V. V.↗
DOE OSTI2018

Momentum Dependence of Near-Threshold J/ψ Photoproduction off Nuclei

The photoproduction of J/ψ mesons off nuclei at the energies of 8 and 11 GeV, which are close to the threshold for their production on a free nucleon, is considered on the basis of the first-collisionmodel relying on the nuclear spectral function and including incoherent processes of charmonium production in photon–nucleon collisions. The model takes into account the absorption of final J/ψ mesons, the binding of target nucleons, and their Fermi motion, as well as the effect of the scalar J/ψ–nucleus potential (or the inmedium shift of the J/ψ-meson mass) on these processes. Under various assumptions on the magnitude of this shift, the momentum dependences of various observables, such as the relative transparency of carbon and gold nuclei and the J/ψ-meson yields, are calculated for meson production on these nuclei. The observables in question include the relative transparencies of these nuclei to J/ψ mesons and the absolute and relativemeson yields from them. It is shown that, at the energy of 11 GeV, which is above the threshold, the first observable is virtually independent of the in-medium change in the J/ψ-meson mass. At the same time, it is moderately sensitive to this change at the subthreshold energy of 8 GeV. On the other hand, it is shown that, at these photon energies, the absolute and relative values of the differential cross sections, especially for the the heavy nucleus of {sup 197} Au, in the momentum range of 4–6 GeV/с are highly sensitive to the in-medium change in the J/ψ-meson mass. Therefore, the present calculations can be used to determine the unknown shift of the J/ψ-meson mass in nuclei by means of comparing their results with data expected from experiments in Hall C at the Continuous Electron Beam Accelerator Facility (CEBAF, USA) upgraded to the energy of 12 GeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2011

The dubna gas-filled recoil separator: Software and algorithms

Experiments carried out on the gas-filled separator of recoil nuclei at the Flerov Laboratory of Nuclear Reactions, Joint Institute for Nuclear Research, in the past several years have proved the hypothesis of the existence of a stability island of superheavy nuclei. The accelerator, beam diagnostics, and detector technologies underlay the success. It is the highly sensitive detecting system of the separator combined with the method of “active correlations” that allowed rare events of the decay of superheavy nuclei to be reliably detected under almost background-free conditions. In turn, to a large extent, this has been made possible owing to special algorithms and software developed for long-term experiments in the beam of {sup 48}Ca ions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Strong interaction physics at the luminosity frontier with 22 GeV electrons at Jefferson Lab

Here, the purpose of this document is to outline the developing scientific case for pursuing an energy upgrade to 22 GeV of the Continuous Electron Beam Accelerator Facility (CEBAF) at the Thomas Jefferson National Accelerator Facility (TJNAF, or JLab). This document was developed with input from a series of workshops held in the period between March 2022 and April 2023 that were organized by the JLab user community and staff with guidance from JLab management (see Sec. 10). The scientific case for the 22 GeV energy upgrade leverages existing or already planned Hall equipment and world-wide uniqueness of CEBAF high-luminosity operations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

High-$Q_0$ treatment of CEBAF 1.5 GHz SRF cavities

The Continuous Electron Beam Accelerator Facility (CEBAF) was the first large-scale accelerator to employ superconducting radiofrequency (SRF) cavities for continuous-wave operation. Ongoing research and development efforts continue to focus on increasing the intrinsic quality factor (Q 0 ) of these cavities in order to reduce cryogenic losses while maintaining operational gradients. Here, in this work, we report on the application of high-Q 0 surface treatments to single-cell and multicell C100 and C75 style 1.5 GHz niobium cavities used in the CEBAF accelerator. Nitrogen infusion and oxygen alloying via medium-temperature baking were applied under heat-treatment constraints relevant to existing cavity hardware. Both processes yielded substantial improvements in Q 0 at moderate accelerating gradients, achieving values of approximately 2 × 10 10 at 2.07 K and 20 MV/m. The effectiveness of nitrogen infusion at reduced annealing temperatures and the successful extension of oxygen alloying to multicell cavities are demonstrated. These results establish viable pathways for implementing high-Q 0 treatments in CEBAF-compatible cavities and support future integration into cryomodules for reduced operational cryogenic load.

Dhakal, Pashupati [Thomas Jefferson National Accel↗
DOE OSTI2020

Initial Implementation Of Machine Learning System for SRF Cavity Fault Classification at CEBAF

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is high power CW electron accelerator. It uses a mixture of two types of SRF modules, older, lower energy C20/C50 modules and newer higher energy C100 modules, arrayed in two anti-parallel linear accelerators. Accurately classifying the type of cavity faults is essential to maintaining and improving accelerator performance. Each C100 cryomodule contains eight 7-cell cavities. When a cavity fault occurs within a cryomodule, all eight cavities generate 17 waveforms each containing 8192 points. This data is exported from EPICS and saved for review. Analysis of these waveforms is time intensive and requires subject matter expertise (SME). SMEs examine the data from each event and label it according to one of several known cavity fault types. Multiple machine learning models have been developed on this labeled dataset with sufficient performance to warrant the creation of a limited machine learning software system for use by accelerator operations staff. This paper discusses the transition from model development to implementation of a prototype system.

Carpenter, A.↗
DOE OSTI2022

INITIAL STUDIES OF CAVITY FAULT PREDICTION AT JEFFERSON LABORATORY

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linac that utilizes over 400 superconducting radio-frequency (SRF) cavities to accelerate electrons up to 12 GeV through 5-passes. Recent work has shown that, given RF signals from a cavity during a fault as input, machine learning approaches can accurately classify the fault type. In this paper we report on initial results of predicting a fault onset using only data prior to the failure event. A data set was constructed using time-series data immediately before a fault (’unstable’) and 1.5 seconds prior to a fault (’stable’) gathered from over 5,000 saved fault events. The data was used to train a binary classifier. The results gave key insights into the behavior of several fault types and provided motivation to investigate whether data prior to a failure event could also predict the type of fault. We discuss our method using a sliding window approach and report on initial results. Recent modifications to the low-level RF control system will provide access to streaming signals and we outline a path forward for leveraging deep learning on streaming data

Vidyaratne, Lasitha↗
DOE OSTI2016

Software Tools for Emittance Measurement and Matching for 12 GeV CEBAF

This paper discusses model-driven setup of the Continuous Electron Beam Accelerator Facility (CEBAF) for the 12GeV era, focusing on qsUtility. qsUtility is a set of software tools created to perform emittance measurements, analyze those measurements, and compute optics corrections based upon the measurements.qsUtility was developed as a toolset to facilitate reducing machine configuration time and reproducibility by way of an accurate accelerator model, and to provide Operations staff with tools to measure and correct machine optics with little or no assistance from optics experts.

Turner, Dennis L.↗
DOE OSTI2018

New Methods for Dispersion Measurement and Correction for 12GeV CEBAF

This paper discusses methods for dispersion measurement and correction for the Continuous Electron Beam Accelerator Facility (CEBAF) for the 12GeV era. New methods will be compared with methods used during the 6GeV era. New software tools which implement the new methods will be discussed, along with a method for automating dispersion measurement and correction. New dispersion measurement and correction methods and tools are being implemented to provide more deterministic results and to reduce machine setup time.

Turner, Dennis L.↗
DOE OSTI2019

PERLE: A High Power Energy Recovery Facility

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab is the first large high power CW recirculating electron accelerator to make use of SRF accelerating structures. The structures are configured in two anti-parallel linacs connected by arcs. Each linac consists of twenty C20/C50 cryomodules each containing eight 5-cell cavities and five C100 upgrade cryomodules each containing eight 7-cell cavities. Accurately classifying the source of cavity faults is critical for improving accelerator performance. A cavity fault triggers a waveform acquisition process where 17 waveform records sampled at 5 kHz are recorded for each of the 8 cavities in the affected cryomodule. The waveform record length is sufficiently long for transient microphonic effects to be observable. This data combined with archived signals sampled at 10 Hz are used to classify faults. Significant time is required for a subject matter expert to analyze and identify the intra-cavity signatures of imminent faults. This paper describes a path forward that utilizes machine learning for automatic fault classification. Post-training identification of the physical origins of faults are discussed, as are potential machine-trained model-free implementations of trip avoidance procedures. These methods should provide new insights into cavity fault mechanisms and facilitate intelligent optimization of cryomodule performance

Kaabi, W.↗
DOE OSTI2019

SRF Cavity Fault Classification Using Machine Learning At CEBAF

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab is the first large high power CW recirculating electron accelerator to make use of SRF accelerating structures. The structures are configured in two anti-parallel linacs connected by arcs. Each linac consists of twenty C20/C50 cryomodules each containing eight 5-cell cavities and five C100 upgrade cryomodules each containing eight 7-cell cavities. Accurately classifying the source of cavity faults is critical for improving accelerator performance. A cavity fault triggers a waveform acquisition process where 17 waveform records sampled at 5 kHz are recorded for each of the 8 cavities in the affected cryomodule. The waveform record length is sufficiently long for transient microphonic effects to be observable. This data combined with archived signals sampled at 10 Hz are used to classify faults. Significant time is required for a subject matter expert to analyze and identify the intra-cavity signatures of imminent faults. This paper describes a path forward that utilizes machine learning for automatic fault classification. Post-training identification of the physical origins of faults are discussed, as are potential machine-trained model-free implementations of trip avoidance procedures. These methods should provide new insights into cavity fault mechanisms and facilitate intelligent optimization of cryomodule performance

Solopova, A.↗
DOE OSTI2021

Superconducting Radio-frequency Cavity Fault Classification Using Machine Learning at Jefferson Laboratory

We report on the development of machine learning models for classifying C100 superconducting radiofrequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. Of the 418 SRF cavities in CEBAF, 96 are designed with a digital low-level RF system configured such that a cavity fault triggers recordings of RF signals for each of eight cavities in the cryomodule. Subject matter experts analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time - rather than postmortem - identification of the offending cavity and classification of the fault type has been implemented. We discuss the performance of the machine learning models during a recent physics run. We also discuss efforts for further insights into fault types through unsupervised learning techniques and present preliminary work on cavity and fault prediction using data collected prior to a failure event.

Tennant, C. D.↗