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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Scanless laser waveform measurement in the near-infrared

Field-resolved measurements of few-cycle laser waveforms allow access to ultrafast electron dynamics in light–matter interactions and are key to future lightwave electronics. Recently, sub-cycle gating based on nonlinear excitation in active pixel sensors has allowed the first single-shot measurements of mid-infrared optical fields. Extending the techniques to shorter wavelengths, however, is not feasible using silicon-based detectors with bandgaps in the near-infrared. Here, we demonstrate an all-optical sampling technique for near-infrared laser fields, wherein an intense fundamental field generates a sub-cycle gate through nonlinear excitation of a wide-bandgap crystal, in this case, ZnO, which can sample the electric field of a weak perturbing pulse. By using a crossed-beam geometry, the temporal evolution of the perturbing field is mapped onto a transverse spatial axis of the nonlinear medium, and the waveform is captured in a single measurement of the spatially resolved fluorescence emission from the crystal. The technique is demonstrated through field-resolved measurements of the field reshaping during nonlinear propagation in the ZnO detection crystal.

Truong, Tran-Chau (ORCID:0000000348940635)↗
DOE OSTI2021

Waveform analysis of a large-area superconducting nanowire single photon detector

Superconducting nanowire single photon detectors (SNSPDs) are typically used as single-mode-fiber-coupled single-pixel detectors, but large area detectors are increasingly critical for applications ranging from microscopy to free-space quantum communications. However, the long meander-line length of such large-area SNSPDs results in a proportionately large kinetic inductance that affects the waveform generated by the device. Here, we explore changes in the rising edge of the readout pulse for a single-pixel large-area SNSPD as a function of the optical spot size on the detector and compare to the rising edge of the dark-count waveform. We observe a bimodal distribution of bright-count rise times and show that the probability of a slow rise time increases in the limit of large spot sizes, indicative of a position sensitive response. Additionally, in the limit of low bias currents, the dark-count readout pulse is most similar to the large spot-size bright-count readout pulse, which suggests that dark counts arise from locations spanning the device. Overall, these results are consistent with a simple model of traveling microwave modes excited by single photons incident at varying positions along the length of the nanowire.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

SCEMA: a high channel density electronics module for fast waveform capture

The development of fast, highly pixelated photodetectors with single-photon sensitivity has the potential to enable a variety of new radiation detection concepts. Systems that desire to employ these detectors without loss of information demand waveform digitization with high sampling rates. Switched capacitor arrays provide a low-cost, low-power, compact solution to fast readout with high channel density. The Sandia Laboratories Compact Electronics for Modular Acquisition (SCEMA) was developed to meet these demands. A single module employs two domino ring sampling switched capacitor arrays (DRS4) [1] to provide 16 channels of up to 5 GS/s waveform digitization. This paper presents an overview of the board design and function. Calibration procedures for the module are discussed. Lastly, temporal resolution tests are presented demonstrating the module's viability as readout for high fidelity temporal measurements of single photons in suitable photodetectors.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber

The liquid argon time projection chamber (LArTPC) detector technology has an excellent capability to measure properties of low-energy neutrinos produced by the sun and supernovae and to look for exotic physics at very low energies. In order to achieve those physics goals, it is crucial to identify and reconstruct signals in the waveforms recorded on each TPC wire. In this work, we report on a novel algorithm based on a one-dimensional convolutional neural network (CNN) to look for the region-of-interest (ROI) in raw waveforms. We test this algorithm using data from the ArgoNeuT experiment in conjunction with an improved noise mitigation procedure and a more realistic data-driven noise model for simulated events. This deep-learning ROI finder shows promising performance in extracting small signals and gives an efficiency approximately twice that of the traditional algorithm in the low energy region of ~0.03–0.1 MeV. This method offers great potential to explore low-energy physics using LArTPCs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Error quantification and comparison of binary neutron star gravitational waveforms from numerical relativity codes

Future gravitational wave detections of merging binary neutron star systems have the possibility to tightly constrain the equation of state of dense nuclear matter. In order to extract such constraints, gravitational waveform models need to be calibrated to accurate numerical relativity simulations of the late inspiral and merger. In this work, we take an essential step toward classifying the error and potential systematics in current generation numerical relativity simulations of merging binary neutron stars. To this end, we perform a direct comparison of two codes (fil, spec), which differ in many aspects, including the numerical methods and discretizations used and equations solved. We find that despite these different approaches, the codes are—within current numerical resolution bounds—fully consistent, and broadly comparable in cost for a given accuracy level. Our results indicate that the error in the waveforms is primarily dominated by the hydrodynamic evolution, consistent with earlier findings in the literature. Finally, we also discuss current limitations and cost estimates for numerical relativity simulations to reach the accuracies required in the era of next-generation gravitational detectors.

gravitational waves↗
DOE OSTI2025

Stochastic Waveform Estimation at the Fundamental Quantum Limit

Although measuring the deterministic waveform of a weak classical force is a well-studied problem, estimating a random waveform, such as the spectral density of a stochastic signal field, is much less well understood despite it being a widespread task at the frontier of experimental physics. State-of-the-art precision sensors of random forces must account for the underlying quantum nature of the measurement but the optimal quantum protocol for interrogating such linear sensors is not known. We derive the fundamental precision limit: the extended-channel quantum Cramér-Rao bound. In the experimentally relevant regime in which losses dominate, we prove that non-Gaussian-state preparation and measurement are required to achieve this fundamental limit and we determine numerically the optimal non-Gaussian protocol. We discuss how this scheme could accelerate searches for signatures of quantum gravity, stochastic gravitational waves, and axionic dark matter.

Axions↗
DOE OSTI2024

Grid Edge Waveform Analytics Framework for Event Detection and Classification

This paper provides a grid edge waveform analytics framework for power system event detection and classification in the local as well as in the wide area. This framework overviews data excellence for event detection and classification. The data excellence describes the data acquisition process and requirements, data processing, data quality, and data integrity. Power system event detection in the local area based on different features such as energy-based, cyclostationary approach, template matching, and wavelet transform are also discussed. Furthermore, local area event detection and classification using approaches such as statistical, signal processing, artificial intelligence, and hybrid are also discussed. Moreover, an overview of wide-area event detection and classification along with several other aspects such as wide-area events, wide-area event detection approaches, event location and system performance, event pattern recognition, inter-area oscillation, and wide-area frequency response under variable deployment of inverter-based resources are also provided. The proposed framework is the first step toward the goal of developing appropriate tools and methodologies to detect and classify local as well as wide-area events using waveform analytics. The appropriate event detection and classification framework development is especially important now as more and more grid edge devices with communication capabilities are being deployed in the modern power grid than ever before.

Bhusal, Narayan↗
DOE OSTI2021

Event Cause Analysis in Distribution Networks using Synchro Waveform Measurements

This paper presents a machine learning method for event cause analysis to enhance situational awareness in distribution networks. The data streams are captured using time-synchronized high sampling rates synchro waveform measurement units (SWMU). The proposed method is formulated based on a machine learning method, the convolutional neural network (CNN). This method is capable of capturing the spatiotemporal feature of the measurements effectively and perform the event cause analysis. Several events are considered in this paper to encompass a range of possible events in real distribution networks, including capacitor bank switching, transformer energization, fault, and high impedance fault (HIF). The dataset for our study is generated using the real time digital simulator (RTDS) to simulate real-world events. The event cause analysis is performed using only one cycle of the voltage waveforms after the event is detected. The simulation results show the effectiveness of the proposed machine learning-based method compared to the state-of-the-art classifiers.

Niazazari, Iman↗
DOE OSTI2014

PAPR-Constrained Pareto-Optimal Waveform Design for OFDM-STAP Radar

We propose a peak-to-average power ratio (PAPR) constrained Pareto-optimal waveform design approach for an orthogonal frequency division multiplexing (OFDM) radar signal to detect a target using the space-time adaptive processing (STAP) technique. The use of an OFDM signal does not only increase the frequency diversity of our system, but also enables us to adaptively design the OFDM coefficients in order to further improve the system performance. First, we develop a parametric OFDM-STAP measurement model by considering the effects of signaldependent clutter and colored noise. Then, we observe that the resulting STAP-performance can be improved by maximizing the output signal-to-interference-plus-noise ratio (SINR) with respect to the signal parameters. However, in practical scenarios, the computation of output SINR depends on the estimated values of the spatial and temporal frequencies and target scattering responses. Therefore, we formulate a PAPR-constrained multi-objective optimization (MOO) problem to design the OFDM spectral parameters by simultaneously optimizing four objective functions: maximizing the output SINR, minimizing two separate Cramer-Rao bounds (CRBs) on the normalized spatial and temporal frequencies, and minimizing the trace of CRB matrix on the target scattering coefficients estimations. We present several numerical examples to demonstrate the achieved performance improvement due to the adaptive waveform design.

Sen, Satyabrata↗
DOE OSTI2021

InversionNet3D: Efficient and Scalable Learning for 3-D Full-Waveform Inversion

Seismic full-waveform inversion (FWI) techniques aim to find a high-resolution subsurface geophysical model provided with waveform data. Some recent effort in data-driven FWI has shown some encouraging results in obtaining 2-D velocity maps. However, due to high computational complexity and large memory consumption, the reconstruction of 3-D high-resolution velocity maps via deep networks is still a great challenge. Here, in this article, we present InversionNet3D (InvNet3D), an efficient and scalable encoder–decoder network for 3-D FWI. The proposed method employs group convolution in the encoder to establish an effective hierarchy for learning information from multiple sources while cutting down unnecessary parameters and operations at the same time. The introduction of invertible layers further reduces the memory consumption of intermediate features during training and, thus, enables the development of deeper networks with more layers and higher capacity as required by different application scenarios. Experiments on the 3-D Kimberlina dataset demonstrate that InvNet3D achieves state-of-the-art reconstruction performance with lower computational cost and lower memory footprint compared to the baseline.

58 GEOSCIENCES↗
DOE OSTI2017

A Comparison of Different NDE Signal Processing Techniques Based on Waveform Entropies Applied to Long Fiber Graphite/Epoxy Plates

This study compares different approaches for imaging the internal architecture of graphite/epoxy composites using backscattered ultrasound. Two cases are studied. In the first, near-surface defects in a thin graphite/epoxy plates are imaged. The same backscattered waveforms were used to produce peak-to-peak, logarithm of signal energy, as well as entropy images of different types. All of the entropy images exhibit better border delineation and defect contrast than the either peak-to-peak or logarithm of signal energy. The best results are obtained using the joint entropy of the backscattered waveforms with a reference function. Two different references are examined. The first is a reflection of the insonifying pulse from a stainless steel reflector. The second is an approximate optimum obtained from an iterative parametric search. The joint entropy images produced using this reference exhibit three times the contrast obtained in previous studies. These plates were later destructively analyzed to determine size and location of near-surface defects and the results found to agree with the defect location and shape as indicated by the entropy images. In the second study, images of long carbon graphite fibers (50% by weight) in polypropylene thermoplastic are obtained as a first step toward ultrasonic determination of the distributions of fiber position and orientation.

Entropy Imaging↗
DOE OSTI2015

Reference respiratory waveforms by minimum jerk model analysis

Purpose: CyberKnife{sup ®} robotic surgery system has the ability to deliver radiation to a tumor subject to respiratory movements using Synchrony{sup ®} mode with less than 2 mm tracking accuracy. However, rapid and rough motion tracking causes mechanical tracking errors and puts mechanical stress on the robotic joint, leading to unexpected radiation delivery errors. During clinical treatment, patient respiratory motions are much more complicated, suggesting the need for patient-specific modeling of respiratory motion. The purpose of this study was to propose a novel method that provides a reference respiratory wave to enable smooth tracking for each patient. Methods: The minimum jerk model, which mathematically derives smoothness by means of jerk, or the third derivative of position and the derivative of acceleration with respect to time that is proportional to the time rate of force changed was introduced to model a patient-specific respiratory motion wave to provide smooth motion tracking using CyberKnife{sup ®}. To verify that patient-specific minimum jerk respiratory waves were being tracked smoothly by Synchrony{sup ®} mode, a tracking laser projection from CyberKnife{sup ®} was optically analyzed every 0.1 s using a webcam and a calibrated grid on a motion phantom whose motion was in accordance with three pattern waves (cosine, typical free-breathing, and minimum jerk theoretical wave models) for the clinically relevant superior–inferior directions from six volunteers assessed on the same node of the same isocentric plan. Results: Tracking discrepancy from the center of the grid to the beam projection was evaluated. The minimum jerk theoretical wave reduced the maximum-peak amplitude of radial tracking discrepancy compared with that of the waveforms modeled by cosine and typical free-breathing model by 22% and 35%, respectively, and provided smooth tracking for radial direction. Motion tracking constancy as indicated by radial tracking discrepancy affected by respiratory phase was improved in the minimum jerk theoretical model by 7.0% and 13% compared with that of the waveforms modeled by cosine and free-breathing model, respectively. Conclusions: The minimum jerk theoretical respiratory wave can achieve smooth tracking by CyberKnife{sup ®} and may provide patient-specific respiratory modeling, which may be useful for respiratory training and coaching, as well as quality assurance of the mechanical CyberKnife{sup ®} robotic trajectory.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Full‐Waveform Simulation of Infrasound Propagation in the Atmosphere: A Case Study of the 2023 April 20 SpaceX Starship Explosion

Infrasound, low‐frequency sound below 20 Hz, has been a key technology to monitor explosion events in the atmosphere. The International Monitoring System (IMS) of the Comprehensive Nuclear‐Test‐Ban Treaty Organization provides the means for continuous monitoring of infrasonic events worldwide. Infrasonic techniques for event location and size estimation can also complement other observational techniques for the detection and characterization of the entry of asteroids or large meteoroids. In this study, we describe the detection capability of IMS infrasound stations for an explosive event in the middle of the atmosphere. Full‐waveform simulations are performed with the specification of atmospheric conditions and incorporated into the event location and explosion yield estimation. We applied it to the 2023 April 20 SpaceX Starship explosion at 29 km altitude. Starship is a super heavy‐lift space vehicle constructed by SpaceX and known as the largest and most powerful rocket ever built. The Starship explosion created huge pressure disturbances in the atmosphere, and its infrasound was detected by the IMS arrays in North America between 2000 and 4000 km. Independent observational data and available ground‐truth information provide a rare opportunity to evaluate the monitoring capability of the IMS network for elevated sources in the atmosphere. We also demonstrate the capability of full‐waveform simulation for infrasound wavefield characterization and prediction to improve event location and yield estimation.

Geosciences↗
DOE OSTI2023

Data-assimilated time-lapse visco-acoustic full-waveform inversion: Theory and application for injected CO 2 plume monitoring

Continuous seismic monitoring for quantifying CO 2 plume migration and detection of any potential leakages in the subsurface is essential for the security of long-term anthropogenic carbon dioxide geologic storage. Traditional time-lapse full-waveform inversion (TLFWI) methods aim to map the CO 2 distribution by estimating seismic velocity changes, but recent studies find that CO 2 -induced attenuation is an important complement to seismic velocity for tracking the CO 2 plumes and even quantifying the CO 2 saturation. We have developed a novel data-assimilated TLFWI method to construct high-resolution time-lapse velocity and attenuation changes from dense time-lapse monitoring data. This method consists of two theoretical developments: visco-acoustic full-waveform inversion (QFWI) and multiparameter hierarchical matrix-powered extended Kalman filter (mHiEKF). The method is capable of (1) posing temporal constraints to retrieve time-lapse information from dense monitoring data by using mHiEKF, (2) accurately recovering high-spatial-resolution velocity and attenuation perturbations using first-order equation system-based QFWI, and (3) providing the model uncertainty by estimating their model standard deviation. With numerical examples, we first find the effectiveness of the new QFWI on estimating accurate velocity and attenuation models simultaneously. Then, a CO 2 leakage case and a realistic Frio-II CO 2 monitoring case are presented to find the advantages and applicability of our data-assimilated QFWI method for estimating time-lapse changes using dense time-lapse monitoring surveys. Here, by assimilating time-lapse seismic monitoring data over time, our data-assimilated QFWI method can improve the resolution of velocity and attenuation changes and decrease their model uncertainties.

58 GEOSCIENCES↗
DOE OSTI2024

Three phase spectral interferometry for recording high dynamic range optical waveforms with <1 ps resolution over >2 ns records applied to closed-loop pulse shaping

Herein we demonstrate, to our knowledge, a novel spectral interferometry technique that simultaneously captures three spectral interferograms of a signal waveform with a reference pulse. The measured performance is robust to nonidealities and ambient drifts by implementing a precisely calibrated 3 × 3 polarization maintaining (PM) splitter that provides three phase shift differences nominally spaced 120° apart. The system can achieve long record length by implementing three, high resolution, virtually imaged phase array (VIPA) spectrometers. Here, we experimentally implement this technique and demonstrate the measurement of waveforms with >2ns of record while maintaining <1ps resolution.

42 ENGINEERING↗
DOE OSTI2016

Detection of the Wenchuan aftershock sequence using waveform correlation with a composite regional network

Using template waveforms from aftershocks of the Wenchuan earthquake (12 May 2008, M s 8.0) listed in a global bulletin and continuous data from eight regional stations, we detected more than 6000 additional events in the mainshock source region from 1 May to 12 August 2008. These new detections obey Omori’s law, extend the magnitude of completeness downward by 1.1 magnitude units, and lead to a more than fivefold increase in number of known aftershocks compared with the global bulletins published by the International Data Centre and the Inter national Seismological Centre. Moreover, we detected more M > 2 events than were listed by the Sichuan Seismograph Network. Several clusters of these detections were then relocated using the double-difference method, yielding locations that reduced travel-time residuals by a factor of 32 compared with the initial bulletin locations. Finally, our results suggest that using waveform correlation on a few regional stations can find aftershock events very effectively and locate them with precision.

58 GEOSCIENCES↗
DOE OSTI2016

Pickless event detection and location: The waveform correlation event detection system (WCEDS) revisited

The standard paradigm for seismic event monitoring breaks the event detection problem down into a series of processing stages that can be categorized at the highest level into station-level processing and network-level processing algorithms (e.g., Le Bras and Wuster (2002)). At the station-level, waveforms are typically processed to detect signals and identify phases, which may subsequently be updated based on network processing. At the network-level, phase picks are associated to form events, which are subsequently located. Furthermore, waveforms are typically directly exploited only at the station-level, while network-level operations rely on earth models to associate and locate the events that generated the phase picks.

58 GEOSCIENCES↗
DOE OSTI2018

Implementation of the Waveform Correlation Event Detection System (WCEDS) Method for Regional Seismic Event Detection in Utah

Backprojection techniques are a class of methods for detecting and locating events that have been successfully implemented at local scales for dense networks. This paper develops the framework for applying a backprojection method to detect and locate a range of event sizes across a heteorogeneous regional network. This article extends previous work on the development of a backprojection method for local and regional seismic event detection, the Waveform Correlation Event Detection System (WCEDS). The improvements outlined here make the technique much more flexible for regional earthquake or explosion monitoring. We first explore how the backprojection operator can be formulated using either a travel-time model or a stack of full waveforms, showing that the former approach is much more flexible and can lead to the detection of smaller events, and to significant improvements in the resolution of event parameters. Second, we discuss the factors that influence the grid of event hypotheses used for backprojection, and develop an algorithm for generating suitable grids for networks with variable density. Third, we explore the effect of including different phases in the backprojection operator, showing that the best results for the study region can be obtained using only the Pg phase, and by including terms for penalizing early arrivals when evaluating the fit for a given event hypothesis. Fourth, we incorporate two parallel backprojection computations with different distance thresholds to enable the robust detection of both network-wide and small (sub-network-only) events. Finally, the set of improvements are outlined by applying WCEDS to four example events on the University of Utah Seismograph Stations (UUSS) network.

58 GEOSCIENCES↗