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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Sub-pixel high-resolution imaging of high-energy x-rays inspired by sub-wavelength optical imaging

We have developed and demonstrated an image super-resolution method—XR-UNLOC: X-Ray UNsupervised particle LOCalization—for hard x-rays measured with fast-frame-rate detectors that is an adaptation of the principle of photo-activated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM), which enabled biological fluorescence imaging at sub-optical-wavelength scales. We demonstrate the approach on experimental coherent Bragg diffraction data measured with 52 keV x-rays from a nanocrystalline sample. From this sample, we resolve the fine fringe detail of a high-energy x-ray Bragg coherent diffraction pattern to an upsampling factor of 16 of the native pixel pitch of 30 μ m of a charge-integrating fastCCD detector. This was accomplished by analysis of individual photon locations in a series of “nearly-dark” instances of the diffraction pattern that each contain only a handful of photons. Central to our approach was the adaptation of the UNLOC photon fitting routine for PALM/STORM to the hard x-ray regime to handle much smaller point spread functions, which required a different statistical test for photon detection and for sub-pixel localization. A comparison to a photon-localization strategy used in the x-ray community (“droplet analysis”) showed that XR-UNLOC provides significant improvement in super-resolution. We also developed a metric by which to estimate the limit of reliable upsampling with XR-UNLOC under a given set of experimental conditions in terms of the signal-to-noise ratio of a photon detection event and the size of the point spread function for guiding future x-ray experiments in many disciplines where detector pixelation limits must be overcome.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Cosmic filaments from cosmic strings

Cosmic strings are generically predicted in many extensions of the Standard Model of particle physics. In this work, we propose a new avenue for detecting cosmic strings through their effect on the filamentary structure in the cosmic web. Using cosmological simulations of the density wake from a cosmic string, we examine a variety of filament structure probes. We show that the largest effect of the cosmic string is an overdensity in the filament distribution around the string wake. The signal from the overdensity is stronger at higher redshift and more robust with a wider field. We analyze the spatial distribution of filaments from a publicly available catalog of filaments built from Sloan digital sky survey galaxies. With existing data, we find no evidence for the presence of a cosmic string wake with string tension parameter $\textit{Gμ}$ above 5 × 10 –6 . However, we project WFIRST will be able to detect a signal from such a wake at the 99% confidence level at redshift $\textit{z}$ = 2, with significantly higher confidence and the possibility of probing lower tensions ($\textit{Gμ}$ ~ 10 –6 ), at $\textit{z}$ = 10. The sensitivity of this method is not competitive with constraints derived from the cosmic microwave background. However, it provides an independent discovery channel at low redshift, which could be a smoking gun in scenarios in which the cosmic microwave background bound can be weakened.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Nondetection of Helium in the Upper Atmospheres of Three Sub-Neptune Exoplanets

We present a search for helium in the upper atmospheres of three sub-Neptune-sized planets to investigate the origins of these ubiquitous objects. The detection of helium for a low-density planet would be strong evidence for the presence of a primary atmosphere accreted from the protoplanetary nebula because large amounts of helium are not expected in the secondary atmospheres of rocky planets. We used Keck+NIRSPEC to obtain high-resolution transit spectroscopy of the planets GJ 1214b, GJ 9827d, and HD 97658b around the 10833 Å He triplet feature. We did not detect helium absorption for any of the planets despite achieving a high level of sensitivity. We used the nondetections to set limits on the planets’ thermosphere temperatures and atmospheric loss rates by comparing grids of 1D models to the data. We also performed coupled interior structure and atmospheric loss calculations, which suggest that the bulk atmospheres (winds) of the planets would be at most modestly enhanced (depleted) in helium relative to their primordial composition. Our lack of detections of the helium triplet for GJ 1214b and GJ 9827d is highly inconsistent with the predictions of models for the present-day mass loss on these planets. Higher signal-to-noise data would be needed to detect the helium feature predicted for HD 97658b. We identify uncertainties in the extreme-ultraviolet fluxes of the host stars and the lack of detailed mass-loss models specifically for cool and metal-enhanced atmospheres as the main limitations to the interpretation of our results. Ultimately, our results suggest that the upper atmospheres of sub-Neptune planets are fundamentally different from those of gas giant planets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Optical-pump–terahertz-probe spectroscopy in high magnetic fields with kHz single-shot detection

We demonstrate optical pump–THz probe (OPTP) spectroscopy with a variable external magnetic field (0–9 T), in which the time-dependent THz signal is measured by echelon-based single-shot detection at a repetition rate of 1 kHz. The method reduces data acquisition times by more than an order of magnitude compared to conventional electro-optic sampling using a scanning delay stage. The approach illustrates the wide applicability of the single-shot measurement approach to non-equilibrium systems that are studied through OPTP spectroscopy, especially in cases where parameters such as magnetic field strength (B) or other experimental parameters are varied. We demonstrate the capabilities of our measurement by performing cyclotron resonance experiments in bulk silicon, where we observe B-field-dependent carrier relaxation and distinct relaxation rates for different carrier types. We use a pair of economical linear array detectors to measure 500 time points on each shot, offering an equivalent performance to camera-based detection with possibilities for higher repetition rates.

Instruments & Instrumentation↗
DOE OSTI2024

Exploration of signal processing methods for superconducting magnet and quench data

Quenching is the phenomenon of a superconducting magnetic material carrying current transitioning into a regular conducting material. This may cause severe and irreparable damage to the superconductor due to Joule heating. The Magnet Department at Fermi National Accelerator Laboratory (FNAL) has acquired experimental data through quench antenna arrays that are recorded when the quench is detected. These data are in terms of voltage signals that are sampled at 100kHz for several minutes. There are multiple channels and each channel provides a data set of more than 20 million observations, while there is one channel, called the trigger channel which shows the time when quench is detected. Despite some advancements that were made including machine learning, data complexity still shadows the progress. In this work, we studied a multi-resolution analysis of the quench antenna data through the Haar wavelet transform. In particular, we applied the maximally overlapped discrete w avelet transform (MODWT) of a suitable level L to the given data and then projected it onto the wavelet basis. This decomposes a given signal (Original data) $x ϵ \mathbb{R}^N$ into $L + 1$ subspaces of $\mathbb{R}^N$. One of the subspaces called the approximation, captures the trend of the signal, and the others, called the details, capture the fluctuations at different frequency bands. This decomposition provides a clear trend of the data at a suitable level and also various activities (spikes) are seen in the details of the decomposition at every level. These spikes might reveal some information about the quench under investigation but in any case, give information about magnet behavior. Also, this decomposition is seen to be very useful in removing noise present in the data due to the source or mechanism of the experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Assessment of Envelope- and Machine Learning-Based Electrical Fault Type Detection Algorithms for Electrical Distribution Grids

This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Frequency Injection Based HVDC Attack-Defense Control Via Squeeze-Excitation Double CNN

Due to the independent controllability and fast power regulation capability, the High Voltage Direct Current (HVDC) system could be a prospective technology to provide multiple ancillary services to the system besides conventional bulk power transmission. However, with the increase of False Data Injection Attacks (FDIAs) on PMU data, the HVDC system could have the wrong response once the collected data that the HVDC system relied on is attacked, thus threatening the system operating security. How to ensure the security of the PMU-based HVDC ancillary service control become an urgent issue. To mitigate the risk, this paper proposed an HVDC attack-defense control based on the FDIAs detection method. Firstly, the Squeeze-Excitation based Double Convolutional Neural Networks (SE-DCNN) is proposed to realize fast identification of the attacking frequency type based on the time and frequency domain signals. Furthermore, the duration time of FDIAs is detected by the local outlier factor. Then, utilizing the results from SE-DCNN, HVDC ancillary service control framework is reorganized and an HVDC attack defense control is proposed for suppressing the potential influence of various types of FDIAs on the HVDC system ancillary service. Different experiments results demonstrate that the proposed method has the ability to significantly mitigate the frequency deviation and oscillation under the FDIA.

42 ENGINEERING↗
DOE OSTI2022

An improved synthetic signal injection routine for HAYSTAC

Microwave cavity haloscopes are among the most sensitive direct detection experiments searching for dark matter axions via their coupling to photons. When the power of the expected microwave signal due to axion-photon conversion is on the order of 10-24 W, having the ability to validate the detector response and analysis procedure by injecting realistic synthetic axion signals becomes helpful. Here we present a method based on frequency hopping spread spectrum for synthesizing axion signals in a microwave cavity haloscope experiment. It allows us to generate a narrow and asymmetric shape in frequency space that mimics an axion's spectral distribution, which is derived from a Maxwell-Boltzmann distribution. In addition, we show that the synthetic axion's power can be calibrated with reference to the system noise. Compared to the synthetic axion injection in HAYSTAC phase I, we demonstrated synthetic signal injection with a more realistic lineshape and calibrated power.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Explosion Detection Using Smartphones: Ensemble Learning with the Smartphone High-Explosive Audio Recordings Dataset and the ESC-50 Dataset

Explosion monitoring is performed by infrasound and seismoacoustic sensor networks that are distributed globally, regionally, and locally. However, these networks are unevenly and sparsely distributed, especially at the local scale, as maintaining and deploying networks is costly. With increasing interest in smaller-yield explosions, the need for more dense networks has increased. To address this issue, we propose using smartphone sensors for explosion detection as they are cost-effective and easy to deploy. Although there are studies using smartphone sensors for explosion detection, the field is still in its infancy and new technologies need to be developed. We applied a machine learning model for explosion detection using smartphone microphones. The data used were from the Smartphone High-explosive Audio Recordings Dataset (SHAReD), a collection of 326 waveforms from 70 high-explosive (HE) events recorded on smartphones, and the ESC-50 dataset, a benchmarking dataset commonly used for environmental sound classification. Two machine learning models were trained and combined into an ensemble model for explosion detection. The resulting ensemble model classified audio signals as either “explosion”, “ambient”, or “other” with true positive rates (recall) greater than 96% for all three categories.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Jamming Detection for Low-Resolution SC-FDE Systems: A Machine Learning Approach

Jammers interfere with communication between base stations (BSs) and legitimate users, leading to degradation of wireless system performance. Our study focuses on jamming detection for wideband single-carrier frequency domain equalization (SC-FDE) systems with low-resolution analog-to digital converters (ADCs). In such systems, jamming detection is challenging because traditional analytical approaches cannot be directly applied due to the delay dispersion in wideband channels and the non-linearity induced by low-resolution ADCs. We propose a machine learning (ML)-based jamming detection method that directly uses the quantized receive signals. Significantly, our ML-based detector can be integrated into existing standard frameworks, such as unique word (UW)-based SC-FDE systems, as it uses existing pilots without requiring additional pilots for jamming detection. Through numerical simulations, we show that two or more bits provide satisfactory performance compared to unquantized scenarios. Additionally, we demonstrate that using more and well-separated pilot symbols improves performance.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Quantifying Information without Entropy: Identifying Intermittent Disturbances in Dynamical Systems

A system’s response to disturbances in an internal or external driving signal can be characterized as performing an implicit computation, where the dynamics of the system are a manifestation of its new state holding some memory about those disturbances. Identifying small disturbances in the response signal requires detailed information about the dynamics of the inputs, which can be challenging. This paper presents a new method called the Information Impulse Function (IIF) for detecting and time-localizing small disturbances in system response data. The novelty of IIF is its ability to measure relative information content without using Boltzmann’s equation by modeling signal transmission as a series of dissipative steps. Since a detailed expression of the informational structure in the signal is achieved with IIF, it is ideal for detecting disturbances in the response signal, i.e., the system dynamics. Those findings are based on numerical studies of the topological structure of the dynamics of a nonlinear system due to perturbated driving signals. The IIF is compared to both the Permutation entropy and Shannon entropy to demonstrate its entropy-like relationship with system state and its degree of sensitivity to perturbations in a driving signal.

42 ENGINEERING↗
DOE OSTI2021

DeepSZ: identification of Sunyaev–Zel’dovich galaxy clusters using deep learning

Galaxy clusters identified via the Sunyaev–Zel’dovich (SZ) effect are a key ingredient in multiwavelength cluster cosmology. In this paper, we present and compare three methods of cluster identification: the standard matched filter (MF) method in SZ cluster finding, a convolutional neural networks (CNN), and a ‘combined’ identifier. We apply the methods to simulated millimeter maps for several observing frequencies for a survey similar to SPT-3G, the third-generation camera for the South Pole Telescope. The MF requires image pre-processing to remove point sources and a model for the noise, while the CNN requires very little pre-processing of images. Additionally, the CNN requires tuning of hyperparameters in the model and takes cut-out images of the sky as input, identifying the cut-out as cluster-containing or not. We compare differences in purity and completeness. The MF signal-to-noise ratio depends on both mass and redshift. Our CNN, trained for a given mass threshold, captures a different set of clusters than the MF, some with signal-to-noise-ratio below the MF detection threshold. However, the CNN tends to mis-classify cut-out whose clusters are located near the edge of the cut-out, which can be mitigated with staggered cut-out. We leverage the complementarity of the two methods, combining the scores from each method for identification. The purity and completeness are both 0.61 for MF, and 0.59 and 0.61 for CNN. The combined method yields 0.60 and 0.77, a significant increase for completeness with a modest decrease in purity. We advocate for combined methods that increase the confidence of many low signal-to-noise clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A critical review of cyber-physical security for building automation systems

Modern Building Automation Systems (BASs), as the brain that enable the smartness of a smart building, often require increased connectivity both among system components as well as with outside entities, such as the cloud, to enable low-cost remote management, optimized automation via outsourced cloud analytics, and increased building-grid integrations. As smart buildings move towards open communication technologies, providing access to BASs through the building's intranet, or even remotely through the Internet, has become a common practice. However, increased connectivity and accessibility come with increased cyber security threats. BASs were historically developed as closed environments with limited cyber-security considerations. As a result, BASs in many buildings are vulnerable to cyber-attacks that may cause adverse consequences, such as occupant discomfort, excessive energy usage, and unexpected equipment downtime. Therefore, there is a strong need to advance the state-of-the-art in cyber-physical security for BASs and provide practical solutions for attack mitigation in buildings. However, an inclusive and systematic review of BAS vulnerabilities, potential cyber-attacks with impact assessment, detection & defense approaches, and cyber resilient control strategies is currently lacking in the literature. This review paper fills the gap by providing a comprehensive up-to-date review of cyber-physical security for BASs at three levels in commercial buildings: management level, automation level, and field level. The general BASs vulnerabilities and protocol-specific vulnerabilities for the four dominant BAS protocols (i.e., BACnet, KNX, LonWorks, and Modbus) are reviewed, followed by a discussion on four attack targets and seven potential attack scenarios. Furthermore, the impact of cyber-attacks on BASs is summarized as signal corruption, signal delaying, and signal blocking. The typical cyber-attack detection and defense approaches are identified at the three levels. Cyber resilient control strategies for BASs under attack are categorized into passive and active resilient control schemes. Open challenges and future opportunities are finally discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Novel Liquid Argon Time-Projection Chamber Readouts

Liquid argon time-projection chambers (LArTPCs) have become a prominent tool for experiments in particle physics. Recent years have yielded significant advances in the techniques used to capture the signals generated by these cryogenic detectors. This article summarizes these novel developments for detection of ionization electrons and scintillation photons in LArTPCs. New methods to capture ionization signals address the challenges of scaling traditional techniques to the large scales necessary for future experiments. Pixelated readouts improve signal fidelity and expand the applicability of LArTPCs to higher-rate environments. Methods that leverage amplification in argon enable measurements in the keV regime and below. Techniques to enhance collection of argon scintillation photons improve calorimetry and expand the physics program for very large detectors. Future efforts aim to demonstrate systems for the combined detection of both electrons and photons.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Gravitational Waves from Primordial Black Hole Dark Matter Spikes

The origin of the binary black hole mergers observed by LIGO–Virgo–KAGRA remains an open question. We calculate the merger rate from primordial black holes (PBHs) within the density spike around supermassive black holes (SMBHs) at the centers of galaxies. We show that the merger rate within the spike is comparable to that within the wider dark matter halo. We also calculate the extreme mass ratio inspiral (EMRI) signal from PBHs hosted within the density spike spiralling into their host SMBHs due to gravitational-wave emission. We predict that LISA may detect ∼10 4 of these EMRIs with a signal-to-noise ratio threshold of 20 within a 4 yr observation run, if all dark matter is made up of ∼30 M ⊙ PBHs. Uncertainties in our rates come from the uncertain mass fraction of PBHs within the dark matter spike, relative to the host central SMBHs, which defines the parameter space LISA can constrain.

dark matter↗
DOE OSTI2021

Selective Enhancement of Spectroscopic Features by Quantum Optimal Control

Tailored light can be used to steer atomic motions into selected quantum pathways. In optimal control theory (OCT), the target is usually expressed in terms of the molecular wave function, a quantity that is not directly observable in experiment. In this study, we present simulations using OCT that optimize the spectroscopic signal itself. By shaping the optical pump, the x-ray stimulated Raman signal, which occurs solely during the passage through conical intersections, is temporally controlled and amplified by up to 2 orders of magnitude. This enhancement can be crucial in order to bring small coherence-based signatures above the detectable threshold. Our approach is applicable to any signal that depends on the expectation value of a positive definite operator.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Probing the Diffuse Ly α Emission on Cosmological Scales: Ly α Emission Intensity Mapping Using the Complete SDSS-IV eBOSS

Based on Sloan Digital Sky Survey Data Release 16, we have detected the large-scale structure of Lyα emission in the universe at redshifts z = 2–3.5 by cross-correlating quasar positions and Lyα emission imprinted in the residual spectra of luminous red galaxies. We apply an analytical model to fit the corresponding Lyα surface brightness profile and multipoles of the redshift-space quasar–Lyα emission cross-correlation function. The model suggests an average cosmic Lyα luminosity density of $6.6^{+3.3}_{-3.1}$ x 10 40 erg s -1 cMpc -3 , a ~2σ detection with a median value about 8–9 times those estimated from deep narrowband surveys of Lyα emitters at similar redshifts. Although the low signal-to-noise ratio prevents us from a significant detection of the Lyα forest–Lyα emission cross-correlation, the measurement is consistent with the prediction of our best-fit model from quasar–Lyα emission cross-correlation within current uncertainties. We rule out the scenario where the Lyα photons mainly originate from quasars. We find that Lyα emission from star-forming galaxies, including contributions from that concentrated around the galaxy centers and that in diffuse Lyα-emitting halos, is able to explain the bulk of the Lyα luminosity density inferred from our measurements. Ongoing and future surveys can further improve the measurements and advance our understanding of the cosmic Lyα emission field.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A quantum material spintronic resonator

In a spintronic resonator a radio-frequency signal excites spin dynamics that can be detected by the spin-diode effect. Such resonators are generally based on ferromagnetic metals and their responses to spin torques. New and richer functionalities can potentially be achieved with quantum materials, specifically with transition metal oxides that have phase transitions that can endow a spintronic resonator with hysteresis and memory. Here we present the spin torque ferromagnetic resonance characteristics of a hybrid metal-insulator-transition oxide/ ferromagnetic metal nanoconstriction. Our samples incorporate V 2 O 3 , with Ni, Permalloy (Ni 80 Fe 20 ) and Pt layers patterned into a nanoconstriction geometry. The first order phase transition in V 2 O 3 is shown to lead to systematic changes in the resonance response and hysteretic current control of the ferromagnetic resonance frequency. Further, the output signal can be systematically varied by locally changing the state of the V 2 O 3 with a dc current. These results demonstrate new spintronic resonator functionalities of interest for neuromorphic computing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗