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DOE OSTI2024

Dark Photon Dark Matter and Low-frequency Gravitational-wave Detection with Gaia-like Astrometry

Astrometric surveys offer us a method for searching for elusive cosmic signatures, such as ultralight dark photon dark matter and gravitational waves (GWs), by observing the temporal change of stars’ apparent locations. The detection capabilities of such surveys rapidly decrease at low frequencies, because the signals become hardly distinguishable from the background motion of stars. In this work, we find that the background motion can be well described by a linear model over time, based on which we propose a linear background subtraction scheme. Compared to the conventional quadratic subtraction, the advantage of linear subtraction emerges within the frequency range below 6 × 10 −9 Hz. Taking dark photons with purely gravitational interactions, dark photons with additional U(1) B or U(1) B−L gauge interactions, and low-frequency GWs as examples, we illustrate that the linear subtraction scheme can result in an enhancement of more than 1 order of magnitude in the exclusion limits of Gaia-like experiments in the low-frequency range.

Astrometry↗

DOE OSTI2019

Computational optical physical unclonable function

A system or method for encryption of data includes a light source, a random optical element and a light detection element. The light source is arranged to transmit an input data signal to the random optical element. The light source is incident on the random optical element such that the input data signal is randomly scattered by the random optical element to generate an image at on the detector disposed at an output of the random optical element. The image received by the detector is applied to a compressive sensing algorithm to generate a transfer function. The transfer function defines a relationship between the input data signal and the image to enable estimation and reconstruction of the input data signal.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

System and method for occupancy-driven register airflow control

An HVAC register airflow control system and an air flow control method are provided. A housing of the system is in fluid communication with an air source for supplying an airflow. The housing includes an air outlet to the enclosed space. A detection sensor detects occupancy information of the enclosed space. A controller generates a control signal based on the occupancy information and controls the shape change of a damper made of a shape memory material. The damper is controlled to switch between a first operative configuration for preventing the airflow from entering the enclosed space and a second operative configured for permitting the airflow to enter the enclosed space.

Wang, Ya↗

DOE OSTI2021

Computational optical physical unclonable function

Birch, Gabriel Carlisle↗

DOE OSTI2022

System and method for neutron and gamma radiation detection using non-homogeneous material scintillator

A scintillator system is disclosed for detecting incoming radiation. The system makes use of a scintillator structure having first and second dissimilar materials. The first dissimilar material emits a first color of light and the second dissimilar material emits a second color of light different from the first color of light. Either one, or both, of the first or second colors of light are emitted in response to receipt of the incoming radiation. A plurality of light detectors is disposed in proximity to the scintillator structure for detecting the first and second different colors of light and generating output signals in response thereto. A detector electronics subsystem is responsive to the output signals and provides an indication of colors emitted by the scintillator structure to infer at least one property of the incoming radiation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Delay-locked-loop timing error mitigation

Systems, methods, and circuits provide delay-locked loop (DLL) timing error mitigation. A DLL false-lock detection system can include DLL circuitry configured to receive a reference clock signal having a time period. The system can include shift register circuitry and latched comparison circuitry which can determine a time period of a locked condition of the DLL delay line with respect to the reference clock signal time period. The system can determine whether the system is correctly locked to the base time period or incorrectly locked to a multiple of the base time period. A further system can operate to cause a phase detector circuitry in a DLL to ignore the first edge of a reference clock signal presented to the phase detector circuitry and thereby avoid stuck-lock conditions.

Filippini, Leo↗

DOE OSTI2024

System and method for in situ inspection of defects in additively manufactured parts using high speed melt pool pyrometry

A system and method is disclosed for detecting anomalies in an additively manufactured part. An energy source generates a signal forming an optical beam for creating a melt pool in a layer of feedstock material being selectively fused to make a part in an additive manufacturing operation. A sensor is configured to receive a signal reflected from the melt pool. The reflected signal forms a thermal signal indicative of a temperature of the feedstock material at a known location on a layer of the feedstock material while the feedstock material is being fused at the known location. A controller receives and analyzes data relating to the received signal to determine if an anomaly exists at the known location.

Forien, Jean-Baptiste↗

DOE OSTI2024

Acoustics-based noninvasive wafer defect detection

Techniques are provided for detecting wafer defects. Example techniques include exciting a wafer using an acoustic signal to cause the wafer to exhibit vibrations, measuring one or more of linear frequency response metrics or nonlinear frequency responses metrics associated with the vibrations, and identifying any defects in the wafer based at least in part on one or more of the linear frequency response metrics or nonlinear frequency responses metrics. In embodiments, the wafer includes bismuth telluride (Bi 2 Te 3 ).

Pantea, Cristian↗

DOE OSTI2025

Low-energy nuclear recoil calibration of the LUX-ZEPLIN experiment with a photoneutron source

The LZ experiment is a liquid xenon time-projection chamber (TPC) searching for evidence of particle dark matter interactions. In the simplest assumption of elastic scattering, many dark matter models predict an energy spectrum which rises quasi-exponentially with decreasing energy transfer to a target atom. LZ expects to detect coherent neutrino-nucleus scattering of $^{8}$B solar neutrinos, the signal from which is very similar to a dark matter particle with mass of about 5.5 GeV/$c^{2}$, which result in typical nuclear recoil energies of $<$5 keV$_{\text{nr}}$. Therefore, it is of crucial importance to calibrate the response of recoiling xenon nuclei to keV-energy recoils. This analysis details the first in situ photoneutron calibration of the LZ detector and probes its response in this energy regime.

Aalbers, J. [SLAC; Stanford U., Phys. Dept.; KIPAC↗

DOE OSTI2025

Semi-supervised permutation invariant particle-level anomaly detection

The development of analysis methods to distinguish potential beyond the Standard Model phenomena in a model-agnostic way can significantly enhance the discovery reach in collider experiments. However, the typical machine learning (ML) algorithms employed for this task require fixed length and ordered inputs that break the natural permutation invariance in collision events. To address this, a semi-supervised anomaly detection tool is presented that takes a variable number of particle-level inputs and leverages a signal model to encode this information into a permutation invariant, event-level representation via supervised training with a Particle Flow Network (PFN). Data events are then encoded into this representation and given as input to an autoencoder for unsupervised ANomaly deTEction on particLe flOw latent sPacE (ANTELOPE), classifying anomalous events based on a low-level and permutation invariant input modeling. Performance of the ANTELOPE architecture is evaluated on simulated samples of hadronic processes in a high energy collider experiment, showing good capability to distinguish disparate models of new physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Status of the SABRE South experiment at the Stawell underground physics laboratory

The SABRE (Sodium iodide with Active Background REjection) experiments aim to detect an annual rate modulation from dark matter interactions in ultra-high purity NaI(Tl) crystals. The SABRE South experiment is located at the Stawell Underground Physics Laboratory (SUPL), Australia, and is the first deep underground laboratory in the Southern Hemisphere. SABRE South is designed to disentangle seasonal or site-related effects from the dark matter-like modulated signal first observed by DAMA/LIBRA in the Northern Hemisphere using an active veto and muon detection system. It is a partner to the SABRE North effort at the Laboratori Nazionali del Gran Sasso (LNGS). SABRE South is instrumented with ultra-high purity NaI(Tl) crystals immersed in a linear alkyl benzene (LAB) based liquid scintillator veto, further surrounded by passive steel and polyethylene shielding and a plastic scintillator muon veto. Significant work has been undertaken to understand the experimental backgrounds and performance of the crystals. The SABRE South experiment is under construction, and will be commissioned from late 2022 to early 2023. We will present the final design of SABRE South, the status of its construction, its expected background, and its sensitivity to a DAMA/LIBRA like modulation. We will also present recent quenching factor measurements of sodium nuclear recoils in NaI(Tl) crystals measured at the Heavy Ion Accelerator Facility, and a report on the status of SUPL.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Bayesian Estimation of Oscillator Parameters: Toward Anomaly Detection and Cyber-Physical System Security

Cyber-physical system security presents unique challenges to conventional measurement science and technology. Anomaly detection in software-assisted physical systems, such as those employed in additive manufacturing or in DNA synthesis, is often hampered by the limited available parameter space of the underlying mechanism that is transducing the anomaly. As a result, the formulation of anomaly detection for such systems often leads to inverse or ill-posed problems, requiring statistical treatments. Here, we present Bayesian inference of unknown parameters associated with a generic actuator considered as a representative vital element of a cyber-physical system. Via a series of experimental input-output measurements, a transfer function for the actuator is obtained numerically, which serves as our model for the proposed method. Linear, nonlinear, and delayed dynamics may be assumed for the actuator response. By devising a code-based malicious signal, we study the efficacy of Bayesian inference for its potential to produce a detection, including uncertainty quantification, with a remarkably small number of input data points. Our approach should be adaptable to a variety of real-time cyber-physical anomaly detection scenarios.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Wind Turbine Gearbox Failure Detection Through Cumulative Sum of Multivariate Time Series Data

The wind energy industry is continuously improving their operational and maintenance practice for reducing the levelized costs of energy. Anticipating failures in wind turbines enables early warnings and timely intervention, so that the costly corrective maintenance can be prevented to the largest extent possible. It also avoids production loss owing to prolonged unavailability. One critical element allowing early warning is the ability to accumulate small-magnitude symptoms resulting from the gradual degradation of wind turbine systems. Inspired by the cumulative sum control chart method, this study reports the development of a wind turbine failure detection method with such early warning capability. Specifically, the following key questions are addressed: what fault signals to accumulate, how long to accumulate, what offset to use, and how to set the alarm-triggering control limit. We apply the proposed approach to 2 years’ worth of Supervisory Control and Data Acquisition data recorded from five wind turbines. We focus our analysis on gearbox failure detection, in which the proposed approach demonstrates its ability to anticipate failure events with a good lead time.

17 WIND ENERGY↗

DOE OSTI2021

Speckle Imaging Characterization of Radial Velocity Exoplanet Systems

We conducted speckle imaging observations of 53 stellar systems that were members of long-term radial velocity (RV) monitoring campaigns and exhibited substantial accelerations indicative of planetary or stellar companions in wide orbits. Our observations were made with blue and red filters using the Differential Speckle Survey Instrument at Gemini-South and the NN-Explore Exoplanet Stellar Speckle Imager at the WIYN telescope. The speckle imaging identifies eight luminous companions within 2″ of the primary stars. In three of these systems—HD 1388, HD 87359, and HD 104304—the properties of the imaged companion are consistent with the RV measurements, suggesting that these companions may be associated with the primary and the cause of the RV variation. For all 53 stellar systems, we derive differential magnitude limits (i.e., contrast curves) from the imaging. We extend this analysis to include upper limits on companion mass in systems without imaging detections. In 25 systems, we rule out companions with masses greater than 0.2 M {sub ⊙}, suggesting that the observed RV signals are caused by late-M dwarfs or substellar (potentially planetary) objects. On the other hand, the joint RV and imaging analysis almost entirely rules out planetary explanations of the RV signal for HD 19522 and suggests that the companion must have an angular separation below a few tenths of an arcsecond. This work highlights the importance of combined RV and imaging observations for characterizing the outer regions of nearby planetary systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Early Fault Detection in Particle Accelerator Power Electronics Using Ensemble Learning

Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its power electronics (high voltage converter modulators). Following an advanced experimental facility setup that mimics SNS operating conditions, the authors successfully conducted 21 early fault detection experiments, where fault precursors are introduced in the system to a degree enough to cause degradation in the waveform signals, but not enough to reach a real fault. Nine different machine learning techniques based on ensemble trees, convolutional neural networks, support vector machines, and hierarchical voting ensembles are proposed to detect the fault precursors. Although all 9 models have shown a perfect and identical performance during the training and testing phase, the performance of most models has decreased in the next test phase once they got exposed to realworld data from the 21 experiments. The hierarchical voting ensemble, which features multiple layers of diverse models, maintains a distinguished performance in early detection of the fault precursors with 95% success rate (20/21 tests), followed by adaboost and extremely randomized trees with 52% and 48% success rates, respectively. The support vector machine models were the worst with only 24% success rate (5/21 tests). The study concluded that a successful implementation of machine learning in the SNS or particle accelerator power systems would require a major upgrade in the controller and the data acquisition system to facilitate streaming and handling big data for the machine learning models. In addition, this study shows that the best performing models were diverse and based on the ensemble concept to reduce the bias and hyperparameter sensitivity of individual models.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

A custom high-stability pulse generator for the test of a novel in-situ magnetic sensor developed to detect radiation damage in undulators

High-performance analog-to-digital converters (ADC) have been widely applied in many areas of science. For instance, magnetic field measurements based on the Faraday’s induction law require high-precision voltmeters to measure induced voltages. In this study in the context of free-electron lasers technology, the Magnetic Measurements Team at SLAC National Accelerator Laboratory proposed a novel in-situ radiation damage detection system (RDDS) for detecting small field variations in undulators. The system measures the flux change in a flexible printed-circuit coil attached to the magnet array during the undulator gap movement. The gap movement changes the magnetic field, which induces a voltage signal in the coil that is measured and integrated with an ADC. Although the system is capable of detecting relative flux changes better than 100 ppm, drift in the ADC’s gain or offset can cause apparent changes in the relative flux. This paper describes the first attempt to developed a high-precision verification circuit to perform ADC testing in the framework of the novel RDDS. The circuit generates a reference voltage pulse with a voltage–time integral relative precision better than 50 ppm for a few hundreds of mVs — the typical order of magnitude measured with the RDDS. The circuit’s design combines a fast and precise switch with a low-noise voltage reference. Long-term measurements allowed statistical analysis and showed that averaging the voltage–time integral of ten pulses gives the required 50 ppm stability. Moreover, reproducibility tests confirmed that the circuit’s output is invariable under small power supply instabilities and equipment shutdown. Instruments and applications designed to quantify the magnetic field by integrating voltage signals may use the pulse generator proposed in this paper for verification purposes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Ultrasonic Characterization of Lithium Ion Thermal Runaway Conditions for Real-time Ultrasonic Enabled BMS Integration

The demand for energy storage is growing, and lithium-ion batteries are a promising technology to meet this need due to their high power/energy density, high round-trip efficiency, rapid response time, and portability. However, recent catastrophic events caused by thermal runaway have slowed their adoption, highlighting the need for an early warning system for battery failure. In this work, ultrasound is used to detect physical changes in 950 mAh batteries by identifying material property changes independent of voltage and current. [Ultrasound signal features (e.g., time of flight, maximum frequency component) were extracted as the batteries were cycled and subjected to both constant current and constant voltage overcharge and were used to develop two metrics identifying failure: a warning to detect the start of overcharge and an emergency stop (E-stop) to immediately take the battery out of service. The identification method involved locating magnitude differences of several ultrasound features compared to baseline operation considering different currents and temperatures, and the warning/stopping metrics were consistent across all experiments. For an average overcharge time of 140 minutes, the average warning was issued 124 minutes before the failure and the average E-stop was triggered 94 minutes before failure. As a test of using ultrasound for early warning detection, a battery was forced into overcharge and returned to normal cycling conditions based on the previously determined warning metric. Both the voltage profile and the ultrasound measurements returned to their baseline behavior, indicating that ultrasonic detection can not only identify battery failure before a catastrophic event, but can also provide early enough warnings such that overcharges can be detected and corrected quickly enough so the battery does not need to be decommissioned.]

25 ENERGY STORAGE↗

DOE OSTI2019

Streamflow in the Columbia River Basin: Quantifying Changes Over the Period 1951-2008 and Determining the Drivers of Those Changes

Trend, detection, and attribution analyses were performed using naturalized streamflow observations and routed land surface model runoff for 10 subbasins in the Columbia River Basin during water years 1951–2008. The Energy Exascale Earth System land-surface model (ELM) version 1.0 and the Routing Application for Parallel computatIon of Discharge (RAPID) routing model were used to conduct semi-factorial simulations driven by multiple sets of bias-corrected forcing data sets. Four main potential drivers, including climate change (CLMT), CO 2 concentration (CO 2 ), nitrogen deposition (NDEP), and land use and land cover change (LULCC), were analyzed during the assessment. All subbasins showed significant (α = 0.10) declines in the observed amount of annual total streamflow, except for the Middle and Upper Snake and Upper Columbia Subbasins. These declines were led by significant decreases in June–October streamflow, which also directly led to significant decreases in peak and summer streamflow. Except for the Snake River Subbasins, LULCC had the same pattern of declines in monthly streamflow, but the period was shifted to May–September. NDEP also had significant trends in June–October; however, rather than decreases, the trends showed significant increases in streamflow. While there were significant trends in CO 2 , NDEP, and LULCC, their signals of change were weak in comparison to the signal in CLMT and the natural internal variability found in streamflow. Overall, the detection and attribution analysis showed that the historical changes found in annual total, center of timing of, and summer mean streamflow could be attributed to changing climate and variability.

54 ENVIRONMENTAL SCIENCES↗