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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

wa-hls4ml: A GNN Surrogate Model for hls4ml

Recent advancements in use of machine learning techniques on field-programmable gate arrays (FPGAs) have allowed for implementation of embedded neural networks with extremely low latency. This is invaluable for particle detectors at the Large Hadron Collider, where latency and used area must be strictly bounded. The hls4ml framework is a procedure for converting from trained machine learning model software, to a synthesis result that can be used on an FPGA. However, running the pipeline is a time-consuming procedure, and there is a strong risk of failure. In particular, it is possible that the model is unable to be converted into a synthesis result, or that the resource consumption of the model will exceed the resources of the target FPGA. To aid with this development, we introduce wa-hls4ml, a surrogate model which uses a graph neural network to emulate the structure of the source models. The goal is to estimate the chance of success and resource consumption of an arbitrary model when passed through the hls4ml procedure, without the time consumption of actually running the pipeline.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Beam Design for Muon Catalyzed Fusion

Fusion holds great promise as a clean and abundant energy source. However, traditional thermonuclear fusion encounters significant challenges due to the extreme temperatures required to overcome the coulomb barrier for two nuclei to fuse. In contrast, muon-catalyzed fusion presents an alternative approach that can surmount this barrier at significantly lower temperatures. Muons, with properties resembling those of electrons but 200 times heavier, can effectively reduce the atomic orbital radius, enabling central nuclei to overcome the coulomb force through the strong force. By introducing muons into a mixture of deuterium and tritium (two hydrogen isotopes), fusion is facilitated, releasing a 3.5MeV alpha particle and a 14.1MeV neutron. In the majority of cases, the muon is liberated and can initiate further fusions. However, approximately 0.8% of the time, it adheres to the alpha particle and remains bound until it either decays or undergoes reactivation through collisio nal ionization. To maximize the number of fusions per muon, it is crucial to enhance the cycling rate and reactivation fraction. Theoretical predictions and experimental data both suggest that the sticking rate decreases with increasing density. However, there exists a discrepancy between experimental observations and theoretical estimations regarding the extent of this decrease. To address these disparities, this experiment aims to investigate the cycling rate and sticking fraction under higher temperatures and pressures than previously explored.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Measurement of the Triple Differential Muon-Antineutrino Charged-Current Inclusive Cross Section in the NOvA Near Detector

NOvA is a long-baseline accelerator-based internationally collaborated neutrino experiment based in the USA. NOvA uses an intense neutrino beam produced at Fermilab’s accelerator complex to make physics measurements of neutrino oscillations, neutrino cross sections, and other high quality neutrino analyses. For its physics goals, NOvA uses two functionally-identical detectors. The Near Detector (ND) is situated at Fermilab, 1 km from the neutrino target and the Far Detector (FD) is located at Ash River, MN, a distance of 810 km from the neutrino source. The ND sees high intensity of the neutrino beam due to its close proximity to the neutrino target. This gives us a unique opportunity for high-precision neutrino cross-section measurements. In this talk, we present our latest results of the muon antineutrino charge current inclusive cross section measurement in the NOvA ND. The new measurement is a triple differential cross section in antimuon kinematic phase-space and in the total energy of all observable final state hadrons, also known as the available energy. We compare different GENIE configuration to our data. We also compare our data results to various neutrino generator predictions, for example, comparisons to GENIE, NuWro, NEUT, and GiBUU neutrino generators are presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

ADRIANO2 Calorimeter Performance from 2022 Test Beams

A novel high-granularity dual-readout calorimetric technique was developed as part of the T1604 collaboration. The ADRIANO2 Calorimeter Prototype consists of a pair of optically isolated, small sized tiles made of scintillating plastic and lead glass. Čerenkov light from the lead glass are exploited to for high resolution timing measurements, while high granularity from scintillating plastic can be used to probe the spatial component of the particle shower. This setup works for excellent energy resolution and particle detection for REDTOP as it is crucial for a calorimeter to detect the decay products of eta/eta-prime mesons. Measurements were collected on ADRIANO2 between February to December 2022 to evaluate the detector performance at Fermilab’s Test Beam Facility. The key metrics extracted from my analysis are the detector’s efficiency for various tile configuration and light-yield which will then be used as parameters for an upgraded REDTOP monte-carlo simulation campaign. An in-depth analysis of ADRIANO2 performance are detailed in this presentation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Operational Experience of a Cryomodule Test Stand for LCLS-II Cryomodules

CMTS1 (cryomodule test stand 1) at Fermilab was built to test cryomodules built for the LCLS-II beamline at SLAC and is currently testing cryomodules for LCLS-II-HE, the high energy upgrade to LCLS-II. The first cryomodule test was in 2016 and to date over 30 cryomodules have been tested here. This talk will highlight operational experience of the vacuum systems including insulating, coupler, and a low particulate beamline vacuum system. It will focus on the problems that have come up over the years, their solutions, and mitigations put in place to prevent further issues.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Neutrino Beams & Fluxes

Overview of accelerator neutrino beams and neutrino fluxes. *Neutrinos & their sources • Accelerator Neutrino Beams • Beamline components • Neutrino flux • Why we care about flux uncertainties

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

SBND Cryogenics Ignition Screenshots

The slides are screenshots of a project created with the Ignition software platform by Inductive Automation for the Short-Baseline Near Detector (SBND). They depict the human-machine interface (HMI) for the experiment’s cryogenic system. As the HMI itself is not of a format that may be converted to PDF or similar document, these slides present a comprehensive set of screenshots of all windows within the HMI that may be publicly presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Computational Exploration of High Entropy Alloys as Promising Materials for Future Beam Windows

With the ever-increasing demand for high beam power, the currently used beam-intercepting devices (BIDs) such as targets, and beam windows may not be able to handle the high power required for future accelerator complexes or the lifetime may be reduced drastically. As beam power increases, the damage incurred by BIDs, including thermal shock, fatigue, and irradiation damage, also rises. Therefore, it is imperative to design materials that can withstand high beam power for longer lifetimes. High entropy alloys (HEAs) have emerged as potential alternative materials for designing next-generation BIDs. In this study, we primarily focus on materials for developing beam windows for next-generation accelerator complexes. We propose an integrated approach that combines various computational techniques to study and design new materials. Specifically, we use CALPHAD, density functional theory (DFT), and molecular dynamics (MD) to comprehensively investigate the defect properties of suitable HEAs, offering potential alternatives for future beam windows. We begin by scanning the extensive phase space provided by Cr-Mn-V-Ti-Al-Co HEAs, selecting 8 compositions after evaluating approximately 120,000 unique compositions using CALPHAD. We, then employ DFT-informed machine learning techniques to develop force-field parameters. Finally, MD simulations using these developed force-field parameters will be used to study the effects of radiation damage on the defect and mechanical properties of the selected alloys. This research explains the use of the CALPHAD approach and shows how critical modeling (DFT and MD) is in developing novel material such as HEAs. It also highlights the promising role of machine learning in this field. The results from this study will greatly improve the novel materials development to be used in next-generation accelerator components, leading to higher beam power and longer operational times of BIDs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Three-Flavor Neutrino Oscillations at NOvA

NOvA, is a two-detector, long-baseline neutrino oscillation experiment located at Fermilab, Batavia, IL, USA. It is designed primarily to constrain neutrino oscillation parameters such as the atmospheric mass squared splitting, $\Delta m^2_{32}$, the mixing angle, $\theta_{23}$, neutrino mass hierachy, and the CP-violating phase, $\delta_{CP}$, using $\nu_\mu \ (\bar{\nu}_\mu)$ disappearance and $\nu_e \ (\bar{\nu}_e)$ appearance data. NOvA receives a high purity 900 KW instense beam of neutrinos and anti-neutrinos from Fermilab's Neutrinos at Main Injector (NuMI) beamline. NOvA used functionally identical finely granulated liquid scintillation detectors, both situated 14.6 mrad off-axis to the beam direction. The NOvA near detector observes un-oscillated $\nu_\mu \ (\bar{\nu}_\mu)$ and beam $\nu_e \ (\bar{\nu}_e)$ events, while the far detector, which is situated 809 km away from the near detector, records un-oscillated $\nu_\mu \ (\bar{\nu}_\mu)$ and oscillated $\nu_e \ (\bar{\nu}_e)$ events. We will discuss the neutrino oscillation analysis strategy at NOvA and the latest three-flavor oscillation results from 10 years of NOvA data in this talk.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Real-Time Anomaly Detection for Charge-Based Triggering in LArTPCs

Modern particle detectors, including liquid argon time projection chambers (LArTPCs), collect a vast amount of data, making it impractical to save everything for offline analysis. As a result, these experiments need to employ different down-selection techniques during data acquisition, referred to as triggering. In this talk, I will present a framework that would enable real-time, data-driven triggering for LArTPCs, using anomaly detection algorithms implemented on Field-Programmable Gate Arrays (FPGAs). Drawing on a study that makes use of collected charge data from the MicroBooNE LArTPC Public Dataset, I will discuss the overall performance of such algorithms and potential applications for future neutrino experiments.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Predicting Missing Regions in Charged Particle Tracks Using a Sparse 3D Convolutional Neural Network

The 2x2 Demonstrator is a prototype detector for the Deep Underground Neutrino Experiment (DUNE)'s Near Detector. Both the 2x2 Demonstrator and the Near Detector itself will have inactive regions wherein there is no sensitivity to charge deposition and light signals that arise from charged particle interactions with liquid argon. In the 2x2, these inactive regions are positioned in-between the active detector modules, which introduces the challenge of inferring what charge signals ought to look like in these regions. This study explores the use of a Sparse 3D Convolutional Neural Network (ConvNet) to infer missing regions in charged particle tracks. Hits corresponding to energy depositions are voxelized into a three-dimensional (3D) grid for each track. Inactive regions within the tracks are replaced with a dense, rectangular 3D grid of voxels, ensuring consistent step sizes in X, Y, and Z directions. Voxels in these dense regions are initialized with an energy value of -1, indicating nonphysical energy or charge. The model is trained to predict which voxels should activate as part of the track and which should not, with the goal of eventually inferring the missing charge or energy values in these voxels. Results indicate that the model accurately predicts track voxels within ±1 unit in X, Y, or Z directions and effectively identifies non-track voxels, despite some overprediction. The approach shows promise in prediction of missing track regions with some accuracy.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

The Icarus Experiment at Fermilab

In these slides I will present the ICARUS experiment at FNAL within the SBN program and I will briefly describe some of the most recent results.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

SBND Analysis using ML Reconstruction Chain

As part of the Short Baseline Neutrino (SBN) Program at Fermilab, the Short Baseline Near Detector (SBND) is positioned in the Booster Neutrino Beam (BNB) and explores neutrino-argon interactions with unprecedented statistics. SBND is a Liquid Argon Time Projection Chamber (LArTPC). Electrons produced through ionization drift toward three wire planes, providing signals that form 2D images of particle trajectories. I introduce the Scalable Particle Imaging using Neural Embeddings (SPINE) framework, which employs a Machine Learning (ML)-based 3D reconstruction using a series of neural networks. Here, we present SPINE’s reconstruction chain, analysis approaches, and results from our latest simulation samples.

43 PARTICLE ACCELERATORS↗