Results for “synthetic diagnostics” — Page 9

NASA NTRS2019

Warm Absorber Diagnostics of AGN Dynamics

Warm absorbers and related phenomena are some of the observable manifestations of outflows or winds from active galactic nuclei (AGNs). Warm absorbers are common in low-luminosity AGNs. They have been extensively studied observationally and are well described by simple phenomenological models. However, major open questions remain. What is the driving mechanism? What is the density and geometrical distribution? How much associated fully ionized gas is there? What is the relation to the quasi-relativistic "ultrafast outflows"? In this paper we present synthetic spectra for the observable properties of warm absorber flows and associated quantities. We use ab initio dynamical models, i.e., solutions of the equations of motion for gas in finite difference form. The models employ various plausible assumptions for the origin of the warm absorber gas and the physical mechanisms affecting its motion. The synthetic spectra are presented as an observational test of these models. In this way we explore various scenarios for warm absorber dynamics. We show that observed spectra place certain requirements on the geometrical distribution of the warm absorber gas, and that not all dynamical scenarios are equally successful at producing spectra similar to what is observed.

Kallman, Timothy R.↗

DOE OSTI2022

Applications of machine learning to a compact magnetic spectrometer for high repetition rate, laser-driven particle acceleration

Accurately and rapidly diagnosing laser–plasma interactions is often difficult due to the time-intensive nature of the analysis and will only become more so with the rise of high repetition rate lasers and the desire to implement feedback on a commensurate timescale. Diagnostic analysis employing machine learning techniques can help address this problem while maintaining a high degree of accuracy. We report on the application of machine learning to the analysis of a scintillator-based electron spectrometer for experiments on high intensity, laser–plasma interactions at the Colorado State University Advanced Lasers and Extreme Photonics facility. Our approach utilizes a neural network trained on synthetic data and tested on experiments to extract the accelerated electron temperature. By leveraging transfer learning, we demonstrate an improvement in the neural network accuracy, decreasing the network error by 50%.

Swanson, K. K. (ORCID:0000000286880568)↗

DOE OSTI2024

Deep learning based x-ray spectrometer for high repetition rate characterization of betatron radiation

Betatron radiation produced from a laser-wakefield accelerator is a broadband, hard x-ray (>1 keV) source that has been used in a variety of applications in medicine, engineering, and fundamental science. Further development and optimization of stable, high repetition rate (HRR) (>1 Hz) betatron sources will provide a means to extend their application base to include single-shot dynamical measurements of ultrafast processes or dense materials. Recent advances in laser technology used in such experiments have enabled increases in shot-rate and system stability, providing improved statistical analysis and detailed parameter scans. However, unique challenges exist at high repetition rate, where data throughput and source optimization are now limited by diagnostic acquisition rates and analysis. Here, we present the development of a machine-learning algorithm for the real-time analysis of betatron radiation. We report on the fielding of this deep learning algorithm for online source characterization at the Institut National de la Recherche Scientifique's Advanced Laser Light Source. By fine-tuning an algorithm originally trained on a fully synthetic dataset using a subset of experimental data, the algorithm can predict the betatron critical energy with a percent error of 7.2 % with a reconstruction time of 1.5 ms, providing a valuable tool for real-time, multi-objective optimization at HRR.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

First fluctuation measurements using an Imaging Neutral Particle Analyzer on DIII-D

A recent upgrade to the Imaging Neutral Particle Analyzer (INPA) on DIII-D has allowed for the first fluctuation measurements using an INPA to be taken during a neoclassical tearing mode (NTM). The INPA signal tracked the mode over 150 ms as the mode frequency dropped to zero, capturing both the NTM with poloidal and toroidal mode numbers $m/n=2/1$ and a $3/2$ mode at double the frequency. Analysis shows that the signals originate from charge exchange events near the edge of the plasma, and relative fluctuation amplitudes are greater than 25% for the duration of the NTM. Filtered signals show frequency beating patterns that are phase-space dependent. Simulated signal is dominated by prompt transport from the neutral beams to the INPA sightline, while the contribution from the slowing down distribution is significantly lower. Simulated measurements in the range of pitches that the diagnostic is sensitive to (0.5≤|v∥/v|≤ 0.75) show the signal is dominated by trapped orbits that pass through magnetic islands near the edge of the plasma. Calculations of expected fluctuation levels show that only direct interaction with the NTM can provide the strong relative fluctuation levels seen experimentally. The prompt nature of the orbits and thin radial layer found to contribute to synthetic signals suggest INPA passive data may be used to measure the perturbations of confined orbits on a single pass through a plasma instability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, and robustness. Inspired by the concept of Edge Artificial Intelligence (Edge AI), we propose a robust approach that enables fast FLI with no degradation of accuracy. This approach couples a recurrent neural network (RNN), which is trained to estimate the fluorescence lifetime directly from raw timestamps without building histograms, to SPAD TCSPC systems, thereby drastically reducing transfer data volumes and hardware resource utilization, and enabling real-time FLI acquisition. We train two variants of the RNN on a synthetic dataset and compare the results to those obtained using center-of-mass method (CMM) and least squares fitting (LS fitting). Results demonstrate that two RNN variants, gated recurrent unit (GRU) and long short-term memory (LSTM), are comparable to CMM and LS fitting in terms of accuracy, while outperforming them in the presence of background noise by a large margin. To explore the ultimate limits of the approach, we derive the Cramer-Rao lower bound of the measurement, showing that RNN yields lifetime estimations with near-optimal precision. To demonstrate real-time operation, we build a FLI microscope based on an existing SPAD TCSPC system comprising a 32 x 32 SPAD sensor named Piccolo. Four quantized GRU cores, capable of processing up to 4 million photons per second, are deployed on the Xilinx Kintex-7 FPGA that controls the Piccolo. Powered by the GRU, the FLI setup can retrieve real-time fluorescence lifetime images at up to 10 frames per second. The proposed FLI system is promising and ideally suited for biomedical applications, including biological imaging, biomedical diagnostics, and fluorescence-assisted surgery, etc.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Top-level physics requirements and simulated performance of the MRSt on the National Ignition Facility

The time-resolving Magnetic Recoil Spectrometer (MRSt) for the National Ignition Facility (NIF) has been identified by the US National Diagnostic Working Group as one of the transformational diagnostics that will reshape the way inertial confinement fusion (ICF) implosions are diagnosed. The MRSt will measure the time-resolved neutron spectrum of an implosion, from which the time-resolved ion temperature, areal density, and yield will be inferred. Top-level physics requirements for the MRSt were determined based on simulations of numerous ICF implosions with varying degrees of alpha heating, P2 asymmetry, and mix. Synthetic MRSt data were subsequently generated for different configurations using Monte–Carlo methods to determine its performance in relation to the requirements. The system was found to meet most requirements at current neutron yields at the NIF. This work was supported by the DOE and LLNL.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Top-level physics requirements and simulated performance of the MRSt on the National Ignition Facility

The time-resolving Magnetic Recoil Spectrometer (MRSt) for the National Ignition Facility (NIF) has been identified by the US National Diagnostic Working Group as one of the transformational diagnostics that will reshape the way inertial confinement fusion (ICF) implosions are diagnosed. The MRSt will measure the time-resolved neutron spectrum of an implosion, from which the time-resolved ion temperature, areal density, and yield will be inferred. Top-level physics requirements for the MRSt were determined based on simulations of numerous ICF implosions with varying degrees of alpha heating, P2 asymmetry, and mix. Synthetic MRSt data were subsequently generated for different configurations using Monte–Carlo methods to determine its performance in relation to the requirements. The system was found to meet most requirements at current neutron yields at the NIF. This work was supported by the DOE and LLNL.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Neutron backscatter edges as a diagnostic of burn propagation

High gain in hotspot-ignition inertial confinement fusion (ICF) implosions requires the propagation of thermonuclear burn from a central hotspot to the surrounding cold dense fuel. As ICF experiments enter the burning plasma regime, diagnostic signatures of burn propagation must be identified. In previous work [A. J. Crilly et al., Phys. Plasmas 27(1), 012701 (2020)], it has been shown that the spectral shape of the neutron backscatter edges is sensitive to the dense fuel hydrodynamic conditions. The backscatter edges are prominent features in the ICF neutron spectrum produced by the 180° scattering of primary deuterium–tritium fusion neutrons from ions. In this work, synthetic neutron spectra from radiation-hydrodynamics simulations of burning ICF implosions are used to assess the backscatter edge analysis in a propagating burn regime. Significant changes to the edge's spectral shape are observed as the degree of burn increases, and a simplified analysis is developed to infer scatter-averaged fluid velocity and temperature. The backscatter analysis offers direct measurement of the increased dense fuel temperatures that result from burn propagation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

New, improved analysis of correlation ECE data to accurately determine turbulent electron temperature spectra and magnitudes (invited)

Turbulent electron temperature fluctuation measurement using a correlation electron cyclotron emission (CECE) radiometer has become an important diagnostic for studying energy transport in fusion plasmas, and its use is widespread in tokamaks (DIII-D, ASDEX Upgrade, Alcator C-Mod, Tore Supra, EAST, TCV, HL-2A, etc.). The CECE diagnostic typically performs correlation analysis between two closely spaced (within the turbulent correlation length) ECE channels that are dominated by uncorrelated thermal noise emission. This allows electron temperature fluctuations embedded in the thermal noise to be revealed and fluctuation level and spectra determined. We have demonstrated a new, improved CECE coherency-based analysis for calculating the temperature fluctuation frequency spectrum and level, which has been verified both numerically through the simulation of synthetic ECE radiometer data and through analysis of experimental data from the CECE system on DIII-D. The new formulation places coherency-based analysis on a firm foundational footing and corrects some currently published methodologies. This new method accurately accounts for bias error in the coherence function and correctly calculates noise levels for a fixed data record length. It provides excellent accuracy in determining temperature fluctuation level (e.g., <10% error) even for a small realization number in the ensemble average. The method also has a smaller uncertainty (i.e., error bar) in the power spectrum when compared to the more standard cross-power method when evaluated at low coherency. Direct calculation of system noise level using correlation between randomized intermediate frequency signals is recommended.

Wang, G. (ORCID:0000000225739827)↗

NASA NTRS2013

Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex

Functional near infrared spectroscopy (fNIRS) is an emerging optical neuroimaging technology that indirectly measures neuronal activity in the cortex via neurovascular coupling. It quantifies hemoglobin concentration ([Hb]) and thus measures the same hemodynamic response as functional magnetic resonance imaging (fMRI), but is portable, non-confining, relatively inexpensive, and is appropriate for long-duration monitoring and use at the bedside. Like fMRI, it is noninvasive and safe for repeated measurements. Patterns of [Hb] changes are used to classify cognitive state. Thus, fNIRS technology offers much potential for application in operational contexts. For instance, the use of fNIRS to detect the mental state of commercial aircraft operators in near real time could allow intelligent flight decks of the future to optimally support human performance in the interest of safety by responding to hazardous mental states of the operator. However, many opportunities remain for improving robustness and reliability. It is desirable to reduce the impact of motion and poor optical coupling of probes to the skin. Such artifacts degrade signal quality and thus cognitive state classification accuracy. Field application calls for further development of algorithms and filters for the automation of bad channel detection and dynamic artifact removal. This work introduces a novel adaptive filter method for automated real-time fNIRS signal quality detection and improvement. The output signal (after filtering) will have had contributions from motion and poor coupling reduced or removed, thus leaving a signal more indicative of changes due to hemodynamic brain activations of interest. Cognitive state classifications based on these signals reflect brain activity more reliably. The filter has been tested successfully with both synthetic and real human subject data, and requires no auxiliary measurement. This method could be implemented as a real-time filtering option or bad channel rejection feature of software used with frequency domain fNIRS instruments for signal acquisition and processing. Use of this method could improve the reliability of any operational or real-world application of fNIRS in which motion is an inherent part of the functional task of interest. Other optical diagnostic techniques (e.g., for NIR medical diagnosis) also may benefit from the reduction of probe motion artifact during any use in which motion avoidance would be impractical or limit usability.

Harrivel, Angela↗

NASA NTRS2013

Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex

Functional near infrared spectroscopy (fNIRS) is an emerging optical neuroimaging technology that indirectly measures neuronal activity in the cortex via neurovascular coupling. It quantifies hemoglobin concentration ([Hb]) and thus measures the same hemodynamic response as functional magnetic resonance imaging (fMRI), but is portable, non-confining, relatively inexpensive, and is appropriate for long-duration monitoring and use at the bedside. Like fMRI, it is noninvasive and safe for repeated measurements. Patterns of [Hb] changes are used to classify cognitive state. Thus, fNIRS technology offers much potential for application in operational contexts. For instance, the use of fNIRS to detect the mental state of commercial aircraft operators in near real time could allow intelligent flight decks of the future to optimally support human performance in the interest of safety by responding to hazardous mental states of the operator. However, many opportunities remain for improving robustness and reliability. It is desirable to reduce the impact of motion and poor optical coupling of probes to the skin. Such artifacts degrade signal quality and thus cognitive state classification accuracy. Field application calls for further development of algorithms and filters for the automation of bad channel detection and dynamic artifact removal. This work introduces a novel adaptive filter method for automated real-time fNIRS signal quality detection and improvement. The output signal (after filtering) will have had contributions from motion and poor coupling reduced or removed, thus leaving a signal more indicative of changes due to hemodynamic brain activations of interest. Cognitive state classifications based on these signals reflect brain activity more reliably. The filter has been tested successfully with both synthetic and real human subject data, and requires no auxiliary measurement. This method could be implemented as a real-time filtering option or bad channel rejection feature of software used with frequency domain fNIRS instruments for signal acquisition and processing. Use of this method could improve the reliability of any operational or real-world application of fNIRS in which motion is an inherent part of the functional task of interest. Other optical diagnostic techniques (e.g., for NIR medical diagnosis) also may benefit from the reduction of probe motion artifact during any use in which motion avoidance would be impractical or limit usability.

Harrivel, Angela↗

NASA NTRSDate not supplied

Multi-Wavelength Modeling and Analysis of the Center-to-Limb Effects of Solar Spectroscopy and Helioseismology

An accurate interpretation of observed solar dynamics with different instruments requires modeling solar magnetoconvection indifferent regimes, as well as taking into account center-to-limb effects, magnetic fields, and turbulence. Realistic 3D radiative MHD modeling of the solar magnetoconvection and atmosphere allows us to generate synthetic observables that directly link the physical properties of the solar plasma to spectroscopic and helioseismic observables. In this work, we investigate the influence of the center-to-limb effects for a wide range of wavelengths, which correspond to the operational lines of HMI/SDO, Hinode, DKIST, and other instruments. In particular, we discuss the wavelength-dependency of the center-to-limb helioseismic observations of acoustic travel times that are used for diagnostics of the deep meridional circulation, as well as 'concave' Sun effect. The presented study will support interpretation of helioseismic inversion results by taking into account realistic coupling of subsurface and atmosphere, and geometry-related effects. In particular, it allows us to improve accuracy of solar subsurface measurements from the SoHO and SDO missions, and resolve the long-standing problem of the meridional circulation and evolution with the solar cycle.

SMD↗

NASA NTRS2003

Global Properties of Local Star Forming Galaxies (ADP 2000)

We performed an archival study of the Hopkins Ultraviolet Telescope (HUT) Astro-2 database. Nineteen spectra of star-forming regions and starburst galaxies were retrieved, reprocessed, and analyzed. The spectra cover the wavelength region 912- 1800 A, providing access to the domain of peak luminosity from a young stellar population. We created an atlas of galaxy spectra documenting the continuum and line properties with an emphasis on the relatively unexplored spectral region below 1200 A. The dust obscuration law was derived from a comparison of the HUT spectra with synthetic population models. The law is similar to the commonly adopted starburst reddening curve at longer wavelengths and approaches the Milky Way law near the Lyman break. A simple power-law parameterization is given, which allows users to express the reddening law in terms of the stellar or nebular color excess at ultraviolet or optical wavelengths. We studied the effect of time-dependent dust obscuration on synthetic ultraviolet line profiles of a young stellar population. If the youngest and most massive stars are more obscured than the older, less massive stars, the C IV 1550 and other stellar wind lines are significantly diluted with respect to a simple foreground screen model for the dust. We propose to use stellar wind lines as a probe of the dust-obscuration model instead of the previously employed nebular emission lines. Since purely stellar diagnostics are utilized, uncertain assumptions on the nebular properties are unnecessary. Photoionization models demonstrate that the C IV 1550 emission is typically dominated by stellar winds and nebular contamination is negligible. A first comparison with the galaxy sample observed with the Hopkins Ultraviolet Telescope favors a dust geometry affecting ionizing and nonionizing stars equally. We point out the need for higher quality data for a more rigorous comparison. The Hubble Space Telescope is capable of obtaining such data in the future.

Leitherer, Claus↗

DOE OSTI2021

Reveal-CoV Diaagnostic Platform (Final Report)

LLNL designed and built a rapid, RT-LAMP-based molecular diagnostics platform as a potential tool to quickly diagnose COVID-19 in under one hour. This point-of-care testing approach involves an initial high temperature swab sample inactivation step followed by amplification of viral RNA using up to 5 control and pathogen-specific assays. Results are determined based on a discreet reaction color change from red to yellow but can also be determined using fluorescence detection. Testing of this prototype platform was conducted with synthetic viral RNA and dried, stabilized reagents. Buffer systems, swab selection, and assay stabilization formulations were evaluated for performance. Limits of detection were determined using RNA; however, testing was not performed with viable SARS-CoV-2 virus or clinical samples.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2016

Traveling Weather Disturbances in Mars Southern Extratropics: Sway of the Great Impact Basins

As on Earth, between late autumn and early spring on Mars middle and high latitudes within its atmosphere support strong mean thermal contrasts between the equator and poles (i.e. "baroclinicity"). Data collected during the Viking era and observations from both the Mars Global Surveyor (MGS) and Mars Reconnaissance Orbiter (MRO) indicate that this strong baroclinicity supports vigorous, large-scale eastward traveling weather systems (i.e. transient synoptic-period waves). Within a rapidly rotating, differentially heated, shallow atmosphere such as on Earth and Mars, such large-scale, extratropical weather disturbances are critical components of the global circulation. These wave-like disturbances act as agents in the transport of heat and momentum, and moreover generalized tracer quantities (e.g., atmospheric dust, water vapor and water-ice clouds) between low and high latitudes of the planet. The character of large-scale, traveling extratropical synoptic-period disturbances in Mars' southern hemisphere during late winter through early spring is investigated using a high-resolution Mars global climate model (Mars GCM). This global circulation model imposes interactively lifted (and radiatively active) dust based on a threshold value of the instantaneous surface stress. Compared to observations, the model exhibits a reasonable "dust cycle" (i.e. globally averaged, a more dusty atmosphere during southern spring and summer occurs). In contrast to their northern-hemisphere counterparts, southern synoptic-period weather disturbances and accompanying frontal waves have smaller meridional and zonal scales, and are far less intense synoptically. Influences of the zonally asymmetric (i.e. east-west varying) topography on southern large-scale weather disturbances are examined. Simulations that adapt Mars' full topography compared to simulations that utilize synthetic topographies emulating essential large-scale features of the southern middle latitudes indicate that Mars' transient barotropic/baroclinic eddies are significantly influenced by the great impact basins of this hemisphere (e.g., Argyre and Hellas). In addition, the occurrence of a southern storm zone in late winter and early spring is keyed particularly to the western hemisphere via orographic influences arising from the Tharsis highlands, and the Argyre and Hellas impact basins. Geographically localized transient-wave activity diagnostics are constructed that illuminate fundamental differences amongst such simulations and these are described.

Weather disturbances↗

DOE OSTI2022

Modeling Air Handling Units to Create a Diverse Fault Dataset for FDD Innovation: Lessons Learned and Recommendations

As energy management and information systems (e.g., automated fault detection and diagnostics [AFDD] tools) become more prevalent in the commercial building stock, it is important to determine the effectiveness of these technologies by benchmarking their performance. The authors have been working to develop the largest publicly available dataset of HVAC fault datasets for performance benchmarking applications, covering the most common HVAC systems and designs including chiller plants, rooftop packaged units, dual duct air handling unit and single duct air handling units. This study covers the development, modeling, and validation of a synthetic fault dataset for the air handling unit (AHU), one of the most common HVAC configurations found in the commercial building stock. Despite this being a common system, real-world time series data are scarce and usually do not span a wide range of weather conditions. Due to this limitation, two detailed AHU models, which included the single duct AHU and dual duct AHU developed in the Modelica language and HVACSIM+ were employed to carry out annual simulations of numerous common sensor faults, mechanical faults, and control sequence faults. The fault inclusive data were then validated by comparing fault effects on system performance to expected symptoms. We summarize the nature of each fault and their impacts under different weather and operation conditions. We report some lessons learnt during the efforts of validating the high volumes of the FDD data sets. Finally, we highlight considerations for FDD developers that may want to use this dataset to assess their algorithms’ performance and their improvement over time.

Casillas, Armando↗

DOE OSTI2022

Development of a Annual Air Handling Unit Fault Dataset for FDD Tools: Lessons Learned and Considerations for FDD Developers

As energy management and information systems (e.g., automated fault detection and diagnostics [AFDD] tools) become more prevalent in the commercial building stock, it is important to determine the effectiveness of these technologies by benchmarking their performance. The authors have been working to develop the largest publicly available dataset of HVAC fault data for performance benchmarking applications, covering the most common HVAC systems and designs including chiller plants, rooftop packaged units, dual duct air handling units and single duct air handling units. This study covers the development, modeling, and validation of a synthetic fault dataset for a single duct air handling unit (AHU), one of the most common HVAC configurations found in the commercial building stock. Despite this being a common system, real-world time series data are scarce and usually do not span a wide range of weather conditions. Due to this limitation, a detailed AHU model was employed to carry out annual simulations of numerous common sensor and mechanical faults, which were then validated by comparing their effects on system performance to expected symptoms. We summarize the nature of each fault and their impacts under different weather and operation conditions. Finally, we highlight considerations for FDD developers that may want to use this dataset to assess their algorithms’ performance and their improvement over time.

Casillas, Armando↗

DOE OSTI2022

Statistical characterization of experimental magnetized liner inertial fusion stagnation images using deep-learning-based fuel–background segmentation

Significant variety is observed in spherical crystal x-ray imager (SCXI) data for the stagnated fuel–liner system created in Magnetized Liner Inertial Fusion (MagLIF) experiments conducted at the Sandia National Laboratories Z-facility. As a result, image analysis tasks involving, e.g., region-of-interest selection (i.e. segmentation), background subtraction and image registration have generally required tedious manual treatment leading to increased risk of irreproducibility, lack of uncertainty quantification and smaller-scale studies using only a fraction of available data. We present a convolutional neural network (CNN)-based pipeline to automate much of the image processing workflow. This tool enabled batch preprocessing of an ensemble of N scans = 139 SCXI images across N exp = 67 different experiments for subsequent study. The pipeline begins by segmenting images into the stagnated fuel and background using a CNN trained on synthetic images generated from a geometric model of a physical three-dimensional plasma. The resulting segmentation allows for a rules-based registration. Our approach flexibly handles rarely occurring artifacts through minimal user input and avoids the need for extensive hand labelling and augmentation of our experimental dataset that would be needed to train an end-to-end pipeline. Here we also fit background pixels using low-degree polynomials, and perform a statistical assessment of the background and noise properties over the entire image database. Our results provide a guide for choices made in statistical inference models using stagnation image data and can be applied in the generation of synthetic datasets with realistic choices of noise statistics and background models used for machine learning tasks in MagLIF data analysis. We anticipate that the method may be readily extended to automate other MagLIF stagnation imaging applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗