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DOE OSTI2022

High-dimensional methods for quantum homodyne tomography

We provide optimized recursion relations for homodyne tomography. We improve previous methods by mitigating the divergences intrinsic in the calculation of the pattern functions used previously, and detail how to implement the data analysis through Monte Carlo simulations. Our refinements are necessary for the reconstruction of excited quantum states which populate a high-dimensional subspace of the electromagnetic field Hilbert space. •Stabilization of numerics for quantum homodyne tomography reconstructions.•High dimensional quantum homodyne tomography reconstruction.•Monte Carlo simulations of quantum homodyne tomography.•Quantum Homodyne tomography simulations.

Mosco, Nicola↗

DOE OSTI2021

Stabilization of the 81-channel coherent beam combination using machine learning

We develop a rapidly converging algorithm for stabilizing a large channel-count diffractive optical coherent beam combination. An 81-beam combiner is controlled by a novel, machine-learning based, iterative method to correct the optical phases, operating on an experimentally calibrated numerical model. A neural-network is trained to detect phase errors based on interference pattern recognition of uncombined beams adjacent to the combined one. Due to the non-uniqueness of solutions in the full space of possible phases, the network is trained within a limited phase perturbation/error range. This also reduces the number of samples needed for training. Simulations have proven that the network can converge in one step for small phase perturbations. When the trained neural-network is applied to a realistic case of 360 degree full range, an iterative scheme exploits random walking at the beginning, with the accuracy of prediction on phase feedback direction, to allow the neural-network to step into the training range for fast convergence. This neural-network-based iterative method of phase detection works tens of times faster than the commonly used stochastic parallel gradient descent approach (SPGD) using a single-detector and random dither when both are tested with random phase perturbations.

Wang, Dan↗

DOE OSTI2015

An "artificial retina" processor for track reconstruction at the full LHC crossing rate

Here, we present the latest results of an R&D; study for a specialized processor capable of reconstructing, in a silicon pixel detector, high-quality tracks from high-energy collision events at 40 MHz. The processor applies a highly parallel pattern-recognition algorithm inspired to quick detection of edges in mammals visual cortex. After a detailed study of a real-detector application, demonstrating that online reconstruction of offline-quality tracks is feasible at 40 MHz with sub-microsecond latency, we are implementing a prototype using common high-bandwidth FPGA devices.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1994

DE 1 VLF observations during Activny wave injection experiments

We report on coordinated high-altitude satellite observations in support of one of the first space-based very low frequency (VLF) wave injection experiments, namely the USSR Aktivny mission. The Aktivny satellite (A) was designed to carry a VLF transmitter (nominal frequency approximately 10 kHz, transmitter power approximately 10 kW) coupled to a 20-m-diameter loop antenna in a nearly polar orbit (83 deg inclination, apogee approximately 2500 km, perigee approximately 500 km). We focus our attention on conjunction experiments between the Aktivny and DE 1 satellites. Because of problems in the deployment of the loop antenna, the radiated power capability of the antenna was significantly reduced. Although this substantially reduced the expectation of receiving detectable signal levels on the satellite, the DE 1/Aktivny conjunction experiments were nevertheless carried out as a means of possibly placing an upper limit on the radiated power. During the period November 1989 through April 1990, a total of 10 DE 1/Aktivny wave injection sessions were conducted. During each session the Aktivny transmitter operated at 10.537 kHz with 1 s On - 1 s Off format, for a period of 6 min centered around the conjunction time. During three conjunction periods (December 12, 26, and 27, 1989) both DE 1 and Aktivny were in the southern hemisphere, and DE 1 was at relatively low altitudes (ranging from 6211 to 14,810 km), thus providing the best conjunction possibilities according to the ray tracing criteria developed above. On most days, Omega transmitter signals as well as commonly occuring natural wave phenomena such as whistlers (0(+)) and hiss were clearly seen well above the background level, but there was no evidence of the Aktivny 1 s On/ 1 s Off pattern. Though no Aktivny signals were detected by the LWR on the DE 1 satellite, the experimental constraints allow us to place an upper limit on the total power radiated by the Aktivny transmitter in the whistler-mode. Using experimental parameters, and the minimum detectable signal level of 0.05 muV/m for LWR, we find the upper limit on the total power radiated by the Aktivny satellite in the whistler-mode to be approximately 10 mW. Several recommendations for future space-based wave injection experiments are presented.

Sonwalkar, V. S.↗

DOE OSTI2019

Future SDN-HPON Control Plane Architecture and Protocol for On-Demand Terabit End-to-End Extreme-Scale Science Applications

The UC Davis team conducted architecture, algorithm and experimental studies for next generation ultra-high-bandwidth optical networks in support of extreme-scale science applications. In particular, over the course of this 3-year project, the UC Davis team achieved the following main accomplishments: Designed a network architecture for automated multi-domain service provisioning: Designed information exchange schemes between autonomous systems (domains) Designed application programming interfaces to orchestrate resource reservation in data centers, high performance computation facilities, and communication networks belonging to different autonomous systems Experimentally assessed the multi-domain network architecture in a distributed field trial set-up connecting premises in three continents Designed a broker-based orchestrated multi-domain optical networking framework supporting observe-analyze-act cycle based cognitive inter-domain service provisioning: Designed an inter-domain operation mechanism, e.g., topology abstraction, for the broker plane and domain managers to collaboratively accomplish inter-domain service provisioning while respecting the autonomy of each administrative domain Designed an alien wavelength monitoring scheme and an artificial neural network based optical signal-to-noise ratio estimator for quality-of-transmission aware inter-domain service provisioning Developed a deep neural network based multi-domain traffic predictor and a cognitive routing and spectrum assignment algorithm to realize knowledge-based autonomous traffic engineering in multi-domain networks Implemented an optical signal-to-noise ratio estimator and traffic predictor with Google’s Tensor flow and assessed their performance using experimental and synthetic data Demonstrated the benefits of the proposed cognitive schemes with experiments ona two-domain elastic optical network testbed and evaluations on a six-domain simulation framework Built a two-domain software-defined networking control plane testbed using the ONOS platform and demonstrated the workflow of broker and domain manager cooperation, including hierarchical performance monitoring, topology abstraction and cognitive resource allocation. Designed self-learning cognitive service provisioning schemes using advanced machine learning technologies: Designed a self-taught anomaly detection framework for optical networks, including an unsupervised data clustering module for pattern recognition, a supervised data regression and classification module for online anomaly detection, and the system operation principle Experimentally validated the effectiveness of the self-taught anomaly detection framework with various failure/anomaly scenarios. Designed a self-learning autonomic routing, modulation and spectrum assignment (RMSA) provisioning framework for elastic optical networks with deep reinforcement learning. Designed effective modeling and training mechanisms for the self-learning RMSA framework Conducted extensive numerical simulations with various traffic patterns and topologies to verify the advantage of the self-learning RMSA framework.

42 ENGINEERING↗

DOE OSTI2020

Data for Training and Testing Radiation Detection Algorithms in an Urban Environment

The US government routinely performs radiological response deployments to search for the presence of illicit nuclear materials (e.g., highly enriched uranium and weapons-grade plutonium) in a specified area. The deployments can be intelligence driven, in support of law enforcement, and for planned events such as WrestleMania, presidential inaugurations, or political conventions. In a typical deployment, radiation detection systems carried by human operators or mounted on vehicles move in a clearing pattern through the search area. Search teams rely on radiation detection algorithms running on these systems in real time to alert them to the presence of an illicit threat source. The detection and identification of sources is complicated by large variation of natural radiation background throughout a search area and the potential presence of localized non-threat sources such as patients undergoing treatment with medical isotopes. As a result, detection algorithms must be carefully balanced between missing real sources (false negatives) and reporting too many false alarms (false positives).The purpose of this data set is to spur innovations in detecting, identifying, and localizing nuclear materials inurban search missions.

07 ISOTOPE AND RADIATION SOURCES↗

NASA NTRS2009

Optoelectronic tweezers for microparticle and cell manipulation

An optical image-driven light induced dielectrophoresis (DEP) apparatus and method are described which provide for the manipulation of particles or cells with a diameter on the order of 100 .mu.m or less. The apparatus is referred to as optoelectric tweezers (OET) and provides a number of advantages over conventional optical tweezers, in particular the ability to perform operations in parallel and over a large area without damage to living cells. The OET device generally comprises a planar liquid-filled structure having one or more portions which are photoconductive to convert incoming light to a change in the electric field pattern. The light patterns are dynamically generated to provide a number of manipulation structures that can manipulate single particles and cells or groups of particles/cells. The OET preferably includes a microscopic imaging means to provide feedback for the optical manipulation, such as detecting position and characteristics wherein the light patterns are modulated accordingly.

Wu, Ming Chiang↗

NASA NTRS2014

Optoelectronic Tweezers for Microparticle and Cell Manipulation

An optical image-driven light induced dielectrophoresis (DEP) apparatus and method are described which provide for the manipulation of particles or cells with a diameter on the order of 100 micromillimeters or less. The apparatus is referred to as optoelectric tweezers (OET) and provides a number of advantages over conventional optical tweezers, in particular the ability to perform operations in parallel and over a large area without damage to living cells. The OET device generally comprises a planar liquid-filled structure having one or more portions which are photoconductive to convert incoming light to a change in the electric field pattern. The light patterns are dynamically generated to provide a number of manipulation structures that can manipulate single particles and cells or group of particles/cells. The OET preferably includes a microscopic imaging means to provide feedback for the optical manipulation, such as detecting position and characteristics wherein the light patterns are modulated accordingly.

Wu, Ming Chiang↗

DOE OSTI2016

Isolation, structural analysis, and expression characteristics of the maize nuclear factor Y gene families

NUCLEAR FACTOR-Y (NF-Y) has been shown to play an important role in growth, development, and response to environmental stress. A NF-Y complex, which consists of three subunits, NF-YA, NF-YB, and, NF-YC, binds to CCAAT sequences in a promoter to control the expression of target genes. Although NF-Y proteins have been reported in Arabidopsis and rice, a comprehensive and systematic analysis of ZmNF-Y genes has not yet been performed. To examine the functions of ZmNF-Y genes in this family, we isolated and characterized 50 ZmNF-Y (14 ZmNF-YA, 18 ZmNF-YB, and 18 ZmNF-YC) genes in an analysis of the maize genome. The 50 ZmNF-Y genes were distributed on all 10 maize chromosomes, and 12 paralogs were identified. Multiple alignments showed that maize ZmNF-Y family proteins had conserved regions and relatively variable N-terminal or C-terminal domains. The comparative syntenic map illustrated 40 paralogous NF-Y gene pairs among the 10 maize chromosomes. Microarray data showed that the ZmNF-Y genes had tissue-specific expression patterns in various maize developmental stages and in response to biotic and abiotic stresses. The results suggested that ZmNF-YB2, 4, 8, 10, 13, and 16 and ZmNF-YC6, 8, and 15 were induced, while ZmNF-YA1, 3, 4, 6, 7, 10, 12, and 13, ZmNF-YB15, and ZmNF-YC3 and 9 were suppressed by drought stress. ZmNF-YA3, ZmNF-YA8 and ZmNF-YA12 were upregulated after infection by the three pathogens, while ZmNF-YA1 and ZmNF-YB2 were suppressed. These results indicate that the ZmNF-Ys may have significant roles in the response to abiotic and biotic stresses. - Highlights: • We indicated a total of 50 members of ZmNF-Y gene family in maize genome. • We analyzed gene structure, protein architecture of ZmNF-Y genes. • Evolution pattern and phylogenic relationships were analyzed among 50 ZmNF-Y genes. • Expression pattern of ZmNF-Ys were detected in various maize tissues. • Transcript levels of ZmNF-Ys were measured under various abiotic and biotic stresses.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Identification of Mercury and Dissolved Organic Matter Complexes Using Ultrahigh Resolution Mass Spectrometry

The chemical speciation and bioavailability of mercury (Hg) is markedly influenced by its complexation with naturally dissolved organic matter (DOM) in aquatic environments. To date, however, analytical methodologies capable of identifying such complexes are scarce. Here in this paper, we utilize ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) coupled with electrospray ionization to identify individual Hg–DOM complexes. The measurements were performed by direct infusion of DOM in a 1:1 methanol:water solution at a Hg to dissolved organic carbon (DOC) molar ratio of 3 × 10 –4 . Heteroatomic molecules, especially those containing multiple S and N atoms, were found to be among the most important in forming strong complexes with Hg. Major Hg–DOM complexes of C 10 H 21 N 2 S 4 Hg + and C 8 H 17 N 2 S 4 Hg + were identified based on both the exact molecular mass and patterns of Hg stable isotope distributions detected by FTICR-MS. Density functional theory was used to predict the solution-phase structures of candidate molecules. Finally, these findings represent the first step to unambiguously identify specific DOM molecules in Hg binding, although future studies are warranted to further optimize and validate the methodology so as to explore detailed molecular compositions and structures of Hg–DOM complexes that affect biological uptake and transformation of Hg in the environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Unsupervised classification for region of interest in X-ray ptychography

X-ray ptychography offers high-resolution imaging of large areas at a high computational cost due to the large volume of data provided. To address the cost issue, we propose a physics-informed unsupervised classification algorithm that is performed prior to reconstruction and removes data outside the region of interest (RoI) based on the multimodal features present in the diffraction patterns. The preprocessing time for the proposed method is inconsequential in contrast to the resource-intensive reconstruction process, leading to an impressive reduction in the data workload to a mere 20% of the initial dataset. This capability consequently reduces computational time dramatically while preserving reconstruction quality. Through further segmentation of the diffraction patterns, our proposed approach can also detect features that are smaller than beam size and correctly classify them as within the RoI.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Influence of the Ag concentration on the medium-range order in a CuZrAlAg bulk metallic glass

Fluctuation electron microscopy of bulk metallic glasses of CuZrAl(Ag) demonstrates that medium-range order is sensitive to minor compositional changes. Furthermore, by analyzing nanodiffraction patterns medium-range order is detected with crystal-like motifs based on the B2 CuZr structure and its distorted structures resembling the martensitic ones. This result thus demonstrates some structural homology between the metallic glass and its high temperature crystalline phase. The amount of medium-range order seems slightly affected with increasing Ag concentration (0, 2, 5 at.%) but the structural motifs of the medium-range ordered clusters become more diverse at the highest Ag concentration. The decrease of dominant clusters is consistent with the destabilization of the B2 structure measured by calorimetry and accounts for the increased glass-forming ability.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Local structure of ion pair interaction in lapatinib amorphous dispersions characterized by synchrotron x-ray diffraction and pair distribution function analysis

For many years, the idea of analyzing atom-atom contacts in amorphous drug-polymer systems has been of major interest, because this method has always had the potential to differentiate between amorphous systems with domains and amorphous systems which are molecular mixtures. In this study, local structure of ionic and noninonic interactions were studied by High-Energy X-ray Diffraction and Pair Distribution Function (PDF) analysis in amorphous solid dispersions of lapatinib in hypromellose phthalate (HPMCP) and hypromellose (HPMC-E3). The strategy of extracting lapatinib intermolecular drug interactions from the total PDF x-ray pattern was successfully applied allowing the detection of distinct nearest neighbor contacts for the HPMC-E3 rich preparations showing that lapatinib molecules do not cluster in the same way as observed in HPMC-P, where ionic interactions are present. Orientational correlations up to nearest neighbor molecules at about 4.3 Å were observed for polymer rich samples; both observations showed strong correlation to the stability of the systems. Lasty, the superior physical stability of 1:3 LP:HPMCP was consistent with the absence of significant intermolecular interactions in (ΔD inter LP (r)) in the range of 3.0 to 6.0 Å, which are attributed to C-C, C-N and C-O nearest neighbor contacts present in drug-drug interactions.

36 MATERIALS SCIENCE↗

DOE OSTI2015

A MULTI-PERIOD OSCILLATION IN A STELLAR SUPERFLARE

Flares that are orders of magnitude larger than the most energetic solar flares are routinely observed on Sun-like stars, raising the question of whether the same physical processes are responsible for both solar and stellar flares. In this Letter, we present a white-light stellar superflare on the star KIC 9655129, observed by NASA’s Kepler mission, with a rare multi-period quasi-periodic pulsation (QPP) pattern. Two significant periodic processes were detected using the wavelet and autocorrelation techniques, with periods of 78 ± 12 minutes and 32 ± 2 minutes. By comparing the phases and decay times of the two periodicities, the QPP signal was found to most likely be linear, suggesting that the two periodicities are independent, possibly corresponding either to different magnetohydrodynamic (MHD) modes of the flaring region or different spatial harmonics of the same mode. The presence of multiple periodicities is a good indication that the QPPs were caused by MHD oscillations and suggests that the physical processes in operation during stellar flares could be the same as those in solar flares.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Witnessing the growth of the nearest galaxy cluster: thermodynamics of the Virgo Cluster outskirts

Here, we present results from Suzaku Key Project observations of the Virgo Cluster, the nearest galaxy cluster to us, mapping its X-ray properties along four long ‘arms’ extending beyond the virial radius. The entropy profiles along all four azimuths increase with radius, then level out beyond ~0.5r 200 , while the average pressure at large radii exceeds Planck Sunyaev–Zel'dovich measurements. These results can be explained by enhanced gas density fluctuations (clumping) in the cluster's outskirts. Using a standard Navarro, Frenk and White model, we estimate a virial mass, radius and concentration parameter of M 200 = 1.05 ± 0.02 × 10 14 M⊙, r 200 = 974.1 ± 5.7 kpc and c = 8.8 ± 0.2, respectively. The inferred cumulative baryon fraction exceeds the cosmic mean at r ~r 200 along the major axis, suggesting enhanced gas clumping possibly sourced by a candidate large-scale structure filament along the north–south direction. The Suzaku data reveal a large-scale sloshing pattern, with two new cold fronts detected at radii of 233 and 280 kpc along the western and southern arms, respectively. Two high-temperature regions are also identified 1 Mpc towards the south and 605 kpc towards the west of M87, likely representing shocks associated with the ongoing cluster growth. Although systematic uncertainties in measuring the metallicity for low-temperature plasma remain, the data at large radii appear consistent with a uniform metal distribution on scales of ~90 × 180 kpc and larger, providing additional support for the early chemical enrichment scenario driven by galactic winds at redshifts of 2–3.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

MetaPoL: Immersive VR based Indoor Patterns of Life (PoL) and Anomalies Data Generation for Insider Threat Modeling in Nuclear Security

Insider threats are perhaps the most serious challenges that nuclear and radiological security systems face. Insiders pose such a great threat due to their access, authority, and knowledge, granting them opportunities to bypass dedicated nuclear and radiological security elements. For example, in one of the latest major insider threat incidents to nuclear security, the Doel-4 nuclear powerplant in Belgium suffered a shutdown, the threat of nuclear materials diversion, and long-term loss of tens of millions of dollars. Seven years of investigation concluded that it was an inside job and attempted sabotage. In this regard, there is an immediate need for R&D and technology integration in the domain of modeling indoor Patterns-of-Life (PoL) and anomaly detection. This can be achieved by using datasets of facility users’ mobility and activity, which can support the design of algorithms for insider threat modeling and detection. However, due to classification, privacy, sensitivity, and safety protocols, such datasets from real physical nuclear reactor facilities are not only hard to share, but also not always feasible to deploy and collect. Aiming to find an alternate solution, our proposed demonstration work - MetaPoL, is the first-ever (for the application space) immersive VR (virtual reality) environment of a real-world secure facility and allows users to move-and-stay through the designed indoor physical layout and also encounter NPCs (non-player characters) that emulate other facility users. In the MetaPoL an interactive user performs realistic spatio-temporal movement, dwelling and activities using a Meta Quest Pro VR headset, and that generates high-frequency (in time) high-resolution (in space) indoor spatial-temporal datasets that are valuable for PoL modeling and anomaly detection research specifically for insider threat modeling and detection mission. Such generated realistic, rich in context, and mission specific datasets can boost AI/Machine Learning based research for modeling and detecting insider threats in nuclear security and nonproliferation.

Gunaratne, Chathika↗

DOE OSTI2020

Synchrophasor Measurements-based Events Detection Using Deep Learning

Deep learning algorithms have been developed for phasor measurement units (PMUs) analysis aiming at providing grid operators to observe and react to significant real-time changes in the grid associated with multiple factors (e.g., power generation and load variations, different type of faults, and equipment mailfunction), or for offline post-event system diagnostics. In this study, a Long Short-Term Memory (LSTM)-based deep neural network (DNN) is adopted and evaluated to identify the most appropriate model configurations for event detection and longer-term anomalous pattern extraction. The proposed DNN model shows the potential on long-term predictions with the ability to capture nonlinear and nonstationary mixture complex patterns in PMU datasets. Real-world PMU in the WECC system were used for model development and validation.

deep learning↗

DOE OSTI2022

Report on PTT Imaging of Defects in AM Metallic Materials-Part 2

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study.

36 MATERIALS SCIENCE↗