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At least 271 records · Page 15

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DOE OSTI2024

Mu2e: Modeling Drift of Ionized Particles with ML

The Mu2e experiment searches for charged lepton flavor violation through muon-to-electron conversion in the field of a nucleus. The signal is a monoenergetic electron with an energy of 104.97 MeV. Its momentum is reconstructed using information from drifting ionized particles in a straw tracker detector. This project analyzes the drift of ionized particles with a deep neural network to help improve the momentum reconstruction process. The model yields a 20% improvement in resolution from a reference linear model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

A Search for Correlated Low-energy Electron Antineutrinos in KamLAND with Gamma-Ray Bursts

We present the results of a time-coincident event search for low-energy electron antineutrinos in the KamLAND detector with gamma-ray bursts (GRBs) from the Gamma-ray Coordinates Network and Fermi Gamma-ray Burst Monitor. Using a variable coincidence time window of ±500 s plus the duration of each GRB, no statistically significant excess above the background is observed. We place the world’s most stringent 90% confidence level upper limit on the electron antineutrino fluence below 17.5 MeV. Assuming a Fermi–Dirac neutrino energy spectrum from the GRB source, we use the available redshift data to constrain the electron antineutrino luminosity and effective temperature.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1992

An international program to protect the earth from impact catastrophe - Initial steps

Risks posed by impacting objects of various sizes are analyzed using the Spaceguard Survey, the 1992 NASA report of International Near-Earth-Object (NEO) Detection workshop. The state-of-the-art technology makes it possible to discover and track nearly all earth-crossing asteroids and short-period comets large enough to threaten global catastrophe. To deal with this hazard a long-term telescopic search is required that reaches stellar magnitude 22 in order to achieve a nearly complete census of objects 1 km or larger. A program can be performed on the basis of an international network of six telescopes of 2-3 m aperture equipped with modern CCD detectors and automatic signal processing capability.

Morrison, David↗

NASA NTRS2010

Terrestrial Gamma-Ray Flashes (TGFs) Observed with the Fermi-Gamma-Ray Burst Monitor: The First Hundred TGFs

The Gamma-ray Burst Monitor (GBM) on the Fermi Gamma-ray Space Telescope Observatory (Fermi) is now detecting ~2.1 TGFs per week. At this rate, nearly a hundred TGFs will have been detected by the time of this Meeting. This rate has increased by a factor of ~8 since new flight software was uploaded to the spacecraft in November 2009 in order to increase the sensitivity of GBM to TGFs. The high time resolution (2 microseconds) allows temporal features to be resolved so that some insight may be gained on the origin and transport of the gamma-ray photons through the atmosphere. The absolute time of the TGFs, known to several microseconds, also allows accurate correlations of TGFs with lightning networks and other lightning-related phenomena. The thick bismuth germanate (BGO) scintillation detectors of the GBM system have observed photon energies from TGFs at energies above 40 MeV. New results on the some temporal aspects of TGFs will be presented.

Fishman, G J.↗

NASA NTRS2010

Terrestrial Gamma-ray Flashes (TGFs) Observed with the Fermi-Gamma-ray Burst Monitor: Temporal and Spectral Properties

The Gamma-ray Burst Monitor (GBM) on the Fermi Gamma-ray Space Telescope Observatory (Fermi) was detecting ~2.1 TGFs per week. This rate has increased by a factor of ~8 since new flight software was uploaded to the spacecraft in November 2009 in order to increase the sensitivity of GBM to TGFs. Further upgrades to Fermi-GBM to allow observations of weaker TGFs are in progress. The high time resolution (2 s) allows temporal features to be resolved so that some insight may be gained on the origin and transport of the gamma-ray photons through the atmosphere. The absolute time of the TGFs, known to several microseconds, also allows accurate correlations of TGFs with lightning networks and other lightning-related phenomena. The thick bismuth germanate (BGO) scintillation detectors of the GBM system have observed photon energies from TGFs at energies above 40 MeV. New results on the some temporal aspects of TGFs will be presented along with spectral characteristics and properties of several electron-positron TGF events that have been identified.

Fishman, G. J.↗

NASA NTRS2012

ACCESS: Design and Sub-System Performance

Establishing improved spectrophotometric standards is important for a broad range of missions and is relevant to many astrophysical problems. ACCESS, "Absolute Color Calibration Experiment for Standard Stars", is a series of rocket-borne sub-orbital missions and ground-based experiments designed to enable improvements in the precision of the astrophysical flux scale through the transfer of absolute laboratory detector standards from the National Institute of Standards and Technology (NIST) to a network of stellar standards with a calibration accuracy of 1% and a spectral resolving power of 500 across the 0.35 -1.7 micrometer bandpass.

Kaiser, Mary Elizabeth↗

NASA NTRS2015

Overview of the Design, Fabrication and Performance Requirements of Micro-Spec, an Integrated Submillimeter Spectrometer

Micro-Spec is a compact submillimeter (350-700 GHz) spectrometer which uses low loss superconducting niobium microstrip transmission lines and a single-crystal silicon dielectric to integrate all of the components of a grating-analog spectrometer onto a single chip. Here we present details of the fabrication and design of a prototype Micro-Spec spectrometer with resolution, R64, where we use a high-yield single-flip wafer bonding process to realize instrument components on a 0.45 m single-crystal silicon dielectric. We discuss some of the electromagnetic design concerns (such as loss, stray-light, cross-talk, and fabrication tolerances) for each of the spectrometer components and their integration into the instrument as a whole. These components include a slot antenna with a silicon lens for optical coupling, a phase delay transmission line network, parallel plate waveguide interference region, and aluminum microstrip transmission line kinetic inductance detectors with extremely low cross-talk and immunity to stray light. We have demonstrated this prototype spectrometer with design resolution of R64. Given the optical performance of this prototype, we will also discuss the extension of this design to higher resolutions suitable for balloon-flight.

spectrometer↗

DOE OSTI2024

Mu2e: Modeling Drift of Ionized Particles with ML

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Testing CP properties of the Higgs boson coupling to τ leptons with heterogeneous graphs

In this paper we explore the possibility of utilizing Deep Learning in measuring the CP properties of the coupling of Higgs boson to τ leptons at the High Luminosity Large Hadron Collider. We employ three Deep Learning (DL) networks, Multi-Layer Perceptron (MLP), Graph Convolution Network (GCN), and Graph Transformer Network (GTN) to enhance signal-to-background separation. The angle between τ lepton decay planes at the detector level is CP-sensitive observables, and we develop Heterogeneous Graphs that integrate diverse node and edge structures to incorporate the CP-sensitive observable efficiently. Using simplified detector simulations we estimate the reconstruction accuracy of the angle between τ lepton planes at the detector level, considering hadronic τ decay modes and standard model backgrounds. With $\sqrt{s}$ = 14 TeV and $\mathcal{L}$ = 100 fb -1 , MLP excludes CP mixing angles above 20° at 68% confidence level (CL), while GCN and GTN achieve exclusions at 90% CL and 95% CL, respectively. The networks also achieve a 3σ significance in excluding a pure CP-odd state.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Jet tagging in the Lund plane with graph networks

The identification of boosted heavy particles such as top quarks or vector bosons is one of the key problems arising in experimental studies at the Large Hadron Collider. In this article, we introduce LundNet, a novel jet tagging method which relies on graph neural networks and an efficient description of the radiation patterns within a jet to optimally disentangle signatures of boosted objects from background events. We apply this framework to a number of different benchmarks, showing significantly improved performance for top tagging compared to existing state-of-the-art algorithms. We study the robustness of the LundNet taggers to non-perturbative and detector effects, and show how kinematic cuts in the Lund plane can mitigate overfitting of the neural network to model-dependent contributions. Finally, we consider the computational complexity of this method and its scaling as a function of kinematic Lund plane cuts, showing an order of magnitude improvement in speed over previous graph-based taggers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Universal Monte Carlo Event Generator

With the Jefferson Lab 12 GeV physics program underway and plans for the future Electron-Ion Collider (EIC), the nuclear physics community is entering a new era of exploration of QCD phenomena involving extensive data taking and event-level processing. This brings with it the potential for unprecedented access to multidimensional particle momentum distributions (PMDs) that can be connected to various theoretical frameworks by unfolding the emergent quantum mechanical properties of QCD using the PMDs. In practice, the PMDs are rendered as discretized histograms (typically one- or two-dimensional projections), and detector effects must be taken into account to unfold the pure detector effect-free PMDs that can be connected with theory. One of the challenges in this new era is obtaining faithful reconstructions of the multidimensional PMDs that preserve all of the inherent particle correlations. In this LDRD project we developed a novel approach using machine learning (ML) that solves this challenge, by avoiding entirely the need to use histograms as the main numerical technique to obtain the detector effect-free PMDs. The new approach is, moreover, scalable to higher dimensional PMDs. The central idea involves training neural networks (NNs) to generate synthetic event-level data (momenta 4-vectors of final state particles) to preserve all correlations among the particles. This is achieved by converting the trial synthetic vertex-level events to detector-level events using detector simulators. The NNs are then tuned using a specialized distance metric between the synthetic detector events and the real detector events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Angular Dependence of Drell-Yan in the SeaQuest Experiment using Deep Neural Network-Based Reconstruction

The SeaQuest and SpinQuest experiments at Fermilab were designed to probe the internal dynamics of protons and neutrons via the angular dependence of muons created by colliding 120 GeV protons into stationary targets. To do this, it is necessary to translate the detector data into coherent information about the particles detected in the experiment. This dissertation describes the development and implementation of QTracker, a neural network-based algorithm designed to reconstruct muons. The application of QTracker to experimental data yields improved reconstruction of muon tracks, allowing for a more precise investigation of the transverse momentum distributions and angular modulations in the Drell-Yan process. This work highlights the potential of neural network-based methods to advance particle tracking and enhance our understanding of nucleon structure.

Conover, Arthur↗

NASA NTRS2003

Reliable Transport over SpaceWire for James Webb Space Telescope (JWST) Focal Plane Electronics (FPE) Network

NASA's James Webb Space Telescope (JWST) faces difficult technical and budgetary challenges to overcome before it is scheduled launch in 2010. The Integrated Science Instrument Module (ISIM), shares these challenges. The major challenge addressed in this paper is the data network used to collect, process, compresses and store Infrared data. A total of 114 Mbps of raw information must be collected from 19 sources and delivered to the two redundant data processing units across a twenty meter deployed thermally restricted interface. Further data must be transferred to the solid-state recorder and the spacecraft. The JWST detectors are kept at cryogenic temperatures to obtain the sensitivity necessary to measure faint energy sources. The Focal Plane Electronics (FPE) that sample the detector, generate packets from the samples, and transmit these packets to the processing electronics must dissipate little power in order to help keep the detectors at these cold temperatures. Separating the low powered front-end electronics from the higher-powered processing electronics, and using a simple high-speed protocol to transmit the detector data minimize the power dissipation near the detectors. Low Voltage Differential Signaling (LVDS) drivers were considered an obvious choice for physical layer because of their high speed and low power. The mechanical restriction on the number cables across the thermal interface force the Image packets to be concentrated upon two high-speed links. These links connect the many image packet sources, Focal Plane Electronics (FPE), located near the cryogenic detectors to the processing electronics on the spacecraft structure. From 12 to 10,000 seconds of raw data are processed to make up an image, various algorithms integrate the pixel data Loss of commands to configure the detectors as well as the loss of science data itself may cause inefficiency in the use of the telescope that are unacceptable given the high cost of the observatory. This combination of requirements necessitates a redundant, fault tolerant, high- speed, low mass, low power network with a low Bit error Rate(1E-9- 1E-12). The ISIM systems team performed many studies of the various network architectures that meeting these requirements. The architecture selected uses the Spacewire protocol, with the addition of a new transport and network layer added to implement end-to-end reliable transport. The network and reliable transport mechanism must be implemented in hardware because of the high average information rate and the restriction on the ability of the detectors to buffer data due to power and size restrictions. This network and transport mechanism was designed to be compatible with existing Spacewire links and routers so that existing equipment and designs may be leveraged upon. The transport layer specification is being coordinated with European Space Agency (ESA), Spacewire Working Group and the Consultative Committee for Space Data System (CCSDS) PlK Standard Onboard Interface (SOIF) panel, with the intent of developing a standard for reliable transport for Spacewire. Changes to the protocol presented are likely since negotiations are ongoing with these groups. A block of RTL VHDL that implements a multi-port Spacewire router with an external user interface will be developed and integrated with an existing Spacewire Link design. The external user interface will be the local interface that sources and sinks packets onto and off of the network (Figure 3). The external user interface implements the network and transport layer and handles acknowledgements and re-tries of packets for reliable transport over the network. Because the design is written in RTL, it may be ported to any technology but will initially be targeted to the new Actel Accelerator series (AX) part. Each link will run at 160 Mbps and the power will be about 0.165 Watt per link worst case in the Actel AX.

Rakow, Glenn↗

DOE OSTI2026

Designing resilient IoT and Edge Computing with federated tinyML

The rapid growth of the Internet of Things (IoT) and Edge Computing (EC) has brought significant conveniences to modern society but has also greatly expanded the cyber attack surfaces, particularly as these technologies are being increasingly integrated into critical systems such as power grids, healthcare, and smart homes. Here, to improve IoT/EC’s cybersecurity posture, we leveraged Artificial Intelligence (AI) and Machine Learning (ML) by employing tinyML to monitor voluminous IoT data for cyber threats while addressing devices’ resource constraints, and utilizing Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. Building on our three-layer architecture combining tinyML and FL to enhance autonomous cyber attack detection, this paper demonstrated that the architecture improves detection accuracy, reduces resource consumption, and enables lightweight, secure IoT device monitoring. These results were validated using the public N-BaIoT dataset as well as real IoT network traffic data collected under multiple attack scenarios from our testbeds. Additionally, we introduced an enhanced FL methodology with a novel preprocessing stage, including federated feature selection and global preprocessor construction, to address IoT/EC data heterogeneity. We developed a physical IoT testbed for attack simulations and data collection, implemented a tinyML-powered detector for realistic model validation, and also built a virtual testbed for scalable evaluations of FL models across diverse network environments.

Cognitive cyber↗

DOE OSTI2024

Exploring the synergy of kinematics and dynamics for collider physics

In collider experiments, an event is characterized by two distinct yet mutually complementary features: the “global features” and the “local features.” Kinematic information such as the event topology of a hard process, masses, and spins of particles comprises global features spanning the entire phase space. This global feature can be inferred from reconstructed objects. In contrast, representations of particles in gauge groups, such as quantum chromodynamics (QCD), offer localized features revealing the dynamics of an underlying theory. These local features, particularly observed in the patterns of radiation as raw data in various detector components, complement the global kinematic features. We propose a simple but effective neural network architecture that seamlessly integrates information from both kinematics and QCD to enhance the signal sensitivity at colliders. Published by the American Physical Society 2024

Ban, Kayoung (ORCID:000000019691877X)↗

DOE OSTI2025

A Probabilistic Reasoner Based on Bayes Risk for Damage Detection in Structural Systems

Structural health monitoring (SHM) systems are used to inform operation of structural systems subject to loads and environments that may affect their integrity. SHM systems rely on continuous monitoring of the structure to determine its health state. These systems are often coupled with a model of the deployed structure to determine the consequences of changes in the system by forecasting the response to future states. These models, which may be thought of as digital twins, need to be updated to reflect the latest state of the structural system. This work makes use of an uncertainty-aware machine learning model that enforces distance preservation of the original input space to determine deviations from the training data input space distributions. This workflow enables domain shift detection to determine whether damage is present in the structure. The uncertainty metrics generated by this network are then used in a Bayes risk framework to design an optimal damage detector given cost and risk considerations. The approach is demonstrated on a computational example with simulated damage.

Najera-Flores, David [ATA Engineering, Inc.]↗

NASA NTRS1980

ESTL RF communication simulators for orbiter payloads and GPS satellites

The simulator contains a transponder compatible with the NASA Spaceflight Tracking and Data Network, a transponder compatible with the DOD Space Ground Link System, and associated command detector units and telemetry multiplexer/modulators. It is pointed out that during selected ESTL tests, the actual payload transponder will be configured with the Payload Communications Simulator (PCS) telemetry multiplexer and modulators to form a complete payload communication system. A block diagram of the PCS is included.

Schmidt, O. L.↗

NASA NTRS2007

35-45 Giga Hertz Transceiver System for Phase and Magnitude Detection

Nondestructive evaluation (NDE) is the science and practice of examining an object in a way that the object's usefulness is not adversely affected. Different types of NDE methods exist but this thesis is based on microwave and millimeter wave NDE using imaging techniques. Microwave NDE is based on illuminating the object under test with a microwave signal and studying the various properties of the reflected signal from the object. This reflected signal contains some information about the inner structure of the object under test. This information may be contained in several parameters including the phase and magnitude of the reflected signal. The goal of this project is to design and build a Q-band coherent transceiver that is capable of measuring the reflected signal's phase and magnitude so that an image of the object under test may be reconstructed. From the several techniques that can be used to construct an image of the object under test, techniques of interest to this work include synthetic aperture focusing technique (SAFT) and microwave holography. The transceiver system should have the ability to sweep a large portion of Q-band frequency range in small frequency steps as quick as possible while the detected phase and magnitude of the reflected signal is very accurate. Several different designs were studied and the final schematic diagram of the transceiver system was determined. One of the most important modules that was designed, implemented and tested in the laboratory was an accurate phase/magnitude detector circuit. The compared results of the scans using the transceiver system and vector network analyzer (VNA) showed that this transceiver system has a great potential to replace a VNA for the purpose of microwave and millimeter wave imaging.

Beni, Aman Aflaki↗