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NASA NTRS2017

Blowing Snow Sublimation and Transport over Antarctica from 11 Years of CALIPSO Observations

Blowing snow processes commonly occur over the earth's ice sheets when the 10 mile wind speed exceeds a threshold value. These processes play a key role in the sublimation and redistribution of snow thereby influencing the surface mass balance. Prior field studies and modeling results have shown the importance of blowing snow sublimation and transport on the surface mass budget and hydrological cycle of high-latitude regions. For the first time, we present continent-wide estimates of blowing snow sublimation and transport over Antarctica for the period 2006-2016 based on direct observation of blowing snow events. We use an improved version of the blowing snow detection algorithm developed for previous work that uses atmospheric backscatter measurements obtained from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite. The blowing snow events identified by CALIPSO and meteorological fields from MERRA-2 are used to compute the blowing snow sublimation and transport rates. Our results show that maximum sublimation occurs along and slightly inland of the coastline. This is contrary to the observed maximum blowing snow frequency which occurs over the interior. The associated temperature and moisture reanalysis fields likely contribute to the spatial distribution of the maximum sublimation values. However, the spatial pattern of the sublimation rate over Antarctica is consistent with modeling studies and precipitation estimates. Overall, our results show that the 2006-2016 Antarctica average integrated blowing snow sublimation is about 393 +/- 196 Gt yr(exp -1), which is considerably larger than previous model-derived estimates. We find maximum blowing snow transport amount of 5 Mt km-1 yr(exp -1) over parts of East Antarctica and estimate that the average snow transport from continent to ocean is about 3.7 Gt yr(exp -1). These continent-wide estimates are the first of their kind and can be used to help model and constrain the surface mass budget over Antarctica.

Antarctica Sublimation Ice-sheet↗

DOE OSTI2025

Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory

Abstract An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification (UQ), or extracting physical insights from large datasets. However, atomistic ML often relies on unsupervised learning or model predictions to analyze information contents from simulation or training data. Here, we introduce a theoretical framework that provides a rigorous, model-free tool to quantify information contents in atomistic simulations. We demonstrate that the information entropy of a distribution of atom-centered environments explains known heuristics in ML potential developments, from training set sizes to dataset optimality. Using this tool, we propose a model-free UQ method that reliably predicts epistemic uncertainty and detects out-of-distribution samples, including rare events in systems such as nucleation. This method provides a general tool for data-driven atomistic modeling and combines efforts in ML, simulations, and physical explainability.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Characterization of volcanic tuff pores pre- and post-underground nuclear detonation using ultra-small and small angle neutron scattering

The ability to accurately model the subsurface transport of radionuclides is fundamental to the remote detection and characterization of underground nuclear explosion (UNE) events. Developing more sophisticated transport models presents a significant opportunity to enhance monitoring capabilities, particularly in the reliable prediction of signature migration. Experimentally determined characterization of geologic materials associated with transport properties is the pertinent base information for such robust model development and calibration. Here, we report results from an unprecedented study demonstrating changes to the pore and fracture network structures in geological materials in response to UNEs over nanometer to micrometer length scales. Volcanic tuffs of five different lithological formations from pre- and post-UNE environments were collected from the Nevada National Security Site. Combined ultra-small and small-angle neutron scattering techniques were used to characterize the tuff pore structure. The results demonstrate measurable differences in the specific surface area and porosity of samples pre- and post-shot from texturally similar lithological formations, indicating that pore properties can serve as a direct physical signature of a UNE. The results also provide experimentally determined transport parameters in support of advanced model development through the integration of gas migration, hydrodynamic simulations, and geologic framework models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

New Physics Agnostic Selections For New Physics Searches

We discuss a model-independent strategy for boosting new physics searches with the help of an unsupervised anomaly detection algorithm. Prior to a search, each input event is preprocessed by the algorithm - a variational autoencoder (VAE). Based on the loss assigned to each event, input data can be split into a background control sample and a signal enriched sample. Following this strategy, one can enhance the sensitivity to new physics with no assumption on the underlying new physics signature. Our results show that a typical BSM search on the signal enriched group is more sensitive than an equivalent search on the original dataset.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Millimeter-wave electromagnetic monitoring for liquid metal droplet-on-demand printing

As new printing approaches emerge, in situ diagnostics to monitor the print quality in real-time become essential for long-term monitoring and feedback control. In this article, we present a millimeter-wave electromagnetic monitoring approach for liquid metal droplet-on-demand printing to support the high-speed and real-time evaluation of droplet ejection. An open-ended rectangular waveguide is placed perpendicular to a jetted droplet stream and operated at a continuous-wave frequency of 40 GHz. Liquid metal droplets with diameters as low as 1.2 mm are characterized, and droplet jetting events on the order of 500 μm are detected at ejection rates up to 80 Hz. The measured results demonstrate that trends at the macro-level (large-scale print variation and anomalies at the nozzle tip) as well as micro-level (droplet size, position, and dynamics) can be detected using this technique.

42 ENGINEERING↗

DOE OSTI2020

An outflow powers the optical rise of the nearby, fast-evolving tidal disruption event AT2019qiz

ABSTRACT At 66 Mpc, AT2019qiz is the closest optical tidal disruption event (TDE) to date, with a luminosity intermediate between the bulk of the population and the faint-and-fast event iPTF16fnl. Its proximity allowed a very early detection and triggering of multiwavelength and spectroscopic follow-up well before maximum light. The velocity dispersion of the host galaxy and fits to the TDE light curve indicate a black hole mass ≈106 M⊙, disrupting a star of ≈1 M⊙. By analysing our comprehensive UV, optical, and X-ray data, we show that the early optical emission is dominated by an outflow, with a luminosity evolution L ∝ t2, consistent with a photosphere expanding at constant velocity (≳2000 km s−1), and a line-forming region producing initially blueshifted H and He ii profiles with v = 3000–10 000 km s−1. The fastest optical ejecta approach the velocity inferred from radio detections (modelled in a forthcoming companion paper from K. D. Alexander et al.), thus the same outflow may be responsible for both the fast optical rise and the radio emission – the first time this connection has been observed in a TDE. The light-curve rise begins 29 ± 2 d before maximum light, peaking when the photosphere reaches the radius where optical photons can escape. The photosphere then undergoes a sudden transition, first cooling at constant radius then contracting at constant temperature. At the same time, the blueshifts disappear from the spectrum and Bowen fluorescence lines (N iii) become prominent, implying a source of far-UV photons, while the X-ray light curve peaks at ≈1041 erg s−1. Assuming that these X-rays are from prompt accretion, the size and mass of the outflow are consistent with the reprocessing layer needed to explain the large optical to X-ray ratio in this and other optical TDEs, possibly favouring accretion-powered over collision-powered outflow models.

Nicholl, M.↗

DOE OSTI2020

Metastable nuclear isomers as dark matter accelerators

Inelastic dark matter and strongly interacting dark matter are poorly constrained by direct detection experiments since they both require the scattering event to deliver energy from the nucleus into the dark matter in order to have observable effects. We propose to test these scenarios by searching for the collisional deexcitation of metastable nuclear isomers by the dark matter particles. The longevity of these isomers is related to a strong suppression of γ- and β-transitions, typically inhibited by a large difference in the angular momentum for the nuclear transition. The collisional deexcitation by dark matter is possible since heavy dark matter particles can have a momentum exchange with the nucleus comparable to the inverse nuclear size, hence lifting tremendous angular momentum suppression of the nuclear transition. This deexcitation can be observed either by searching for the direct effects of the decaying isomer, or through the rescattering or decay of excited dark matter states in a nearby conventional dark matter detector setup. Existing nuclear isomer sources such as naturally occurring 180m Ta, 137m Ba produced in decaying Cesium in nuclear waste, 177m Lu from medical waste, and 178m Hf from the Department of Energy storage can be combined with current dark matter detector technology to search for this class of dark matter.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Understanding Impacts of Frequency Calculations on Underfrequency Load Shedding

Power system fundamental frequency-dependent protection decisions and control decisions are increasingly common in the distribution system space. Understanding the algorithms used in frequency measurements is critical to understanding the decisions made by the many intelligent electronic devices in the power system. These decisions are critically important for reliability analysis for interconnected power systems as they bear directly on resource planning to mitigate under-frequency conditions that would result in under-frequency load shedding (UFLS). This work aims to improve the understanding of frequency measurement algorithms, evaluate the intelligent electronic devices that use frequency measurements for decision making, and understand the algorithms' direct impact on frequency related decision making. First, this work presents background information on the use of frequency measurements in protection logic, specifically UFLS. Second, the paper presents a relay hardware evaluation test bed used to detect and protect systems from under-frequency events. Finally, the paper presents the dynamic events used to evaluate commercially available, off-the-shelf relay equipment and the results from the relay evaluation.

COMTRADE↗

DOE OSTI2025

A Weakly Supervised Machine Learning Procedure for Magnet Quench Diagnostics

Voltage taps remain the standard and reliable diagnostic tool for detecting quenches in superconducting magnets. However, they identify a quench only at the time of voltage rise and do not provide information on earlier physical precursors. In this work, we investigate whether acoustic emission data can reveal precursor activity that occurs before conventional voltage detection using machine learning techniques. We introduce an event selection method and a weakly supervised machine learning procedure to learn data-driven criteria for identifying potential acoustic precursors to quenches. Two Convolutional Neural Network (CNN) architectures are trained: one on acoustic sensor events from our selection procedure and one on the Fast Fourier Transforms (FFTs) of these events. Both networks are trained iteratively using confidence-weighted loss functions to associate certain subsets of training data with a precursor label. We evaluate the performance of these models by examining the time distribution of events classified as potential precursors relative to the quench onset. Results indicate that the proposed approach can possibly distinguish acoustic emission events occurring closer to the quench from earlier acoustic activity during ramping, suggesting the potential for flagging quench precursors in acoustic data.

Khan, Maira [Fermilab] (ORCID:0009000891602387)↗

DOE OSTI2021

Detection of Anomalies in Environmental Gamma Radiation Background with Hopfield Artificial Neural Network - Consortium on Nuclear Security Technologies (CONNECT) Q3 Report

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Development of Hopfield Artificial Neural Network for Anomaly Detection in Environmental Gamma Radiation Background: Consortium on Nuclear Security Technologies (CONNECT) (Q2 Report)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Detection of Anomalies in Gamma Background Radiation Data with K-Means and Self-Organizing Map Clustering Algorithms (Consortium on Nuclear Security Technologies (CONNECT) Q1 Report)

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. The challenge is that spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2022

In-Field Alpha Spectrometer Development FY2022 Mid-Year Report

In response to needs identified by the International Atomic Energy Agency (IAEA), Idaho National Laboratory (INL) has developed an In-Field Alpha Spectrometer (IFAS) to allow IAEA safeguards inspectors to collect samples of uranium hexafluoride (UF 6 ) at processing facilities, to perform field assessments to verify uranium enrichment. For sample collection, the IFAS method uses Single-Use Destructive Assay (SUDA) samplers, developed at Pacific Northwest National Laboratory (PNNL), which contain thin zeolite coatings that trap UF6 gas and convert it to the safer, more stable form uranyl fluoride (as a dihydrate, UO 2 F 2 ·2H 2 O). For alpha spectrometry, the IFAS instrument employs a large area silicon semiconductor transducer to detect and record alpha particle energy-deposition events. Over the course of this project INL, PNNL, Oak Ridge National Laboratory worked to optimize the SUDA sample design for alpha spectrometry, to optimize processes for manufacturing, loading, and shipping SUDA samples, and to allow the measurement of loaded SUDA samples. In fiscal year (FY) 2022 INL has bene investigating the reproducibility and measurement precision of IFAS measurements and the associated determination of uranium enrichment.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2025

Quantum Gravity and Laser Interferometry: Towards Observable Predictions

Understanding quantum gravity remains one of the deepest challenges in modern physics, as direct experimental access to Planck-scale effects is beyond current technological reach. However, recent theoretical advances indicate that quantum fluctuations of spacetime may produce measurable effects in precision experiments, particularly near causal horizons. This opens new avenues for testing quantum gravity phenomena through high-precision measurement techniques. This dissertation develops multiple theoretical models to characterize these effects and examines their potential observational signatures in future gravitational wave interferometers. We begin by investigating the role of quantum fluctuations in near-horizon geometries through the lens of the AdS/CFT correspondence, which provides a powerful framework for understanding the interplay between quantum field theory and general relativity via holographic principles. By modeling stochastic energy-momentum sources in Rindler-AdS spacetime, we demonstrate that vacuum fluctuations transform the Einstein equations into a Langevin-type stochastic differential equation, leading to potentially observable fluctuations in photon traversal times. Extending this approach to Minkowski spacetime, we establish a correspondence between gravitational shockwaves and fluid dynamics, showing that near-horizon perturbations satisfy an equation analogous to that governing incompressible fluids, thereby reinforcing the membrane paradigm and hydrodynamic analogies in the context of the fluid/gravity duality. Furthermore, we construct the covariant phase space of a spherically symmetric causal diamond in Minkowski spacetime, identifying two fundamental charges that govern its evolution. These results provide a foundation for quantizing causal horizons and understanding their microscopic degrees of freedom. Building upon these theoretical developments, we further examine a related stochastic phenomenon: the gravitational wave memory background arising from the cumulative memory steps produced by supermassive black hole mergers. After reviewing the standard stochastic gravitational wave background, gravitational memory effects, and BMS symmetries, we model the stochastic memory background using a Brownian motion framework. We show that while the cumulative memory background initially appears above the sensitivity curve of space-based interferometers like LISA, the realistic subtraction of individually resolvable merger events substantially suppresses the residual signal, making its detection more challenging. This highlights the critical importance of source subtraction when evaluating the detectability of gravitational memory effects. By bridging fundamental theory with experimental prospects, this dissertation contributes to the ongoing effort to uncover the quantum nature of spacetime through precision measurement techniques. Whether through detecting quantum spacetime fluctuations, gravitational memory backgrounds, or probing the symmetries of causal horizons, the pursuit of observable quantum gravity phenomena continues to expand the frontiers of both theory and experiment.

Zhang, Yiwen [Caltech] (ORCID:0000000323559416)↗

NASA NTRS1972

Carbon isotopic studies of organic matter in Precambrian rocks.

A survey has been undertaken of the carbon composition of the total organic fraction of a suite of Precambrian sediments to detect isotopic trends possibly correlative with early evolutionary events. Early Precambrian cherts of the Fig Tree and upper and middle Onverwacht groups of South Africa were examined for this purpose. Reduced carbon in these cherts was found to be isotopically similar to photosynthetically produced organic matter of younger geological age. Reduced carbon in lower Onverwacht cherts was found to be anomalously heavy; it is suggested that this discontinuity may reflect a major event in biological evolution.

Oehler, D. Z.↗

NASA NTRS1975

Positron annihilation in solar flares

The gamma ray line at 0.51 MeV originates from the annihilation of positrons. When a fraction of the positrons annihilate from bound states of positronium, the 0.51-MeV line is accompanied by a continuum of 3-gamma annihilation radiation at energies up to 0.51 MeV. Accurate calculations of the rates of free annihilation and positronium formation in a solar flare plasma are presented and positronium formation by charge exchange is discussed. The observability of the 3-gamma annihilation is increased by the inherent delay in the production and slowing down time of the positrons. It was concluded that such radiation could be detected at times late in solar gamma ray events when the continuum and prompt line emissions have essentially disappeared.

Crannell, C. J.↗

NASA NTRS1976

Short time transient periodicities from Cyg X-1

The temporal behavior of three new events of modulated optical emission from Cyg X1, detected in July 1975, is presented. Short time periodicities in the optical band are investigated. Single photon pulses from the photomultiplier are recorded on magnetic tape together with a very accurate 1 kHz reference frequency. During playback of the tape, the reference signal gives a 1 ms timing to a scaler interfaced with a small processor and the number of photon per millisecond is recorded on a digital tape.

Auriemma, G.↗

NASA NTRS1975

Energy, frequency, and distance of moonquakes at the Apollo 17 site

'Thermal' moonquakes have been detected at the Apollo 17 site. A typical event releases about 1 to 10 million ergs of energy. The annual seismic energy release for the events observed in the Taurus-Littrow Valley is estimated at 100 billion ergs. Such small events would not cause impossibly rapid erosion on either the North Massif or within the craters of the Central Cluster. Seismic events become increasingly frequent after sunrise and reach a maximum at sunset. The largest events, however, occur most commonly near lunar noon. Rise times of the seismic signals, after being calibrated by the well-located Lunar Seismic Profiling Experiment (LSPE) seismic sources, were used to estimate distances to the seismic events. Most seismic events (about 90%) appear to occur within 2.5 km of the seismometer array.

Cooper, M. R.↗