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DOE OSTI2024

Arctic Ocean Hydroacoustics

As the Arctic warms and loses its perennial ice cover, it is becoming more attractive for a variety of human uses. Hydroacoustic monitoring of this activity will grow in importance over the coming years and decades. Changes to the physical environment affect acoustic propagation and noise, with ramifications for our ability to detect and locate events and activities of interest. In this report, we use two long-term data sets from the Beaufort Sea, western Arctic Ocean, to determine how acoustic propagation conditions and seismic source detections are impacted by the changing environment: 1) oceanographic observations from ice-tethered profilers, and 2) passive acoustic recordings from a hydrophone. We find that changes to Beaufort Sea thermohaline stratification is stabilizing a subsurface duct, leading to more focused acoustic energy arrivals. Detections of catalogued submarine earthquakes show geographic differences in signal strength between seismic and acoustic waves. Signal strength increases with earthquake magnitude, but relationships to other source and path factors are less clear. Ambient noise also has clear seasonal patterns in the Arctic, with relatively low noise in the spring, higher noise near 1 Hz in summer, and higher noise near 10 Hz in winter. Climate change is expected to modify these seasonal noise patterns, impacting event detection. Future work will further investigate the mechanisms of ice effects on sound and couple acoustic modeling to an Earth System Model.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1990

Whistlers in Neptune's magnetosphere: Evidence of atmospheric lightning

During the Voyager 2 flyby of Neptune, a series of 16 whistler-like events were detected by the plasma wave instrument near closest approach. These events were observed at radial distances from 1.30 to 1.99 R sub N and magnetic latitudes from -7 to 33 deg. The frequencies ranged from 6.1 to 12.0 kHz, and the dispersions fit the Eckersley law for lightning-generated whistlers. Lightning in the atmosphere of Neptune is the only known source of such signals. The frequency range of the whistlers (up to 12 kHz) indicates that the local electron densities are substantially higher (N sub e greater than 30 t0 100 per cu cm) than indicated by the in situ plasma measurements. The dispersion of the whistlers is very large, typically 26,000 sec Hz(exp 0.5). Based on existing plasma density models and measurements, the dispersions are too large to be accounted for by a single direct path from the lightning source to the spacecraft. Therefore, multiple bounces from one hemisphere to the other are required. The most likely propagation path probably involves a lightning source on the dayside of the planet, with repeated bounces through the dense dayside ionosphere at low L-values.

Gurnett, D. A.↗

NASA NTRS1990

Whistlers in Neptune's magnetosphere - Evidence of atmospheric lightning

During the Voyager 2 flyby of Neptune, a series of 16 whistler-like events were detected by the plasma wave instrument near closest approach. These events were observed at radial distances from 1.30 to 1.99 R sub N and magnetic latitudes from -7 to 33 deg. The frequencies ranged from 6.1 to 12.0 kHz, and the dispersions fit the Eckersley law for lightning-generated whistlers. Lightning in the atmosphere of Neptune is the only known source of such signals. The frequency range of the whistlers (up to 12 kHz) indicates that the local electron densities are substantially higher (N sub e greater than 30 to 100 per cu cm) than indicated by the in situ plasma measurements. The dispersion of the whistlers is very large, typically 26,000 sec Hz(exp 0.5). Based on existing plasma density models and measurements, the dispersions are too large to be accounted for by a single direct path from the lightning source to the spacecraft. Therefore, multiple bounces from one hemisphere to the other are required. The most likely propagation path probably involves a lightning source on the dayside of the planet, with repeated bounces through the dense dayside ionosphere at low L-values.

Gurnett, D. A.↗

NASA NTRS2022

Application of Compressive Sensing for Gravitational Microlensing Events

Compressive sensing is a novel mathematical technique for simultaneous data acquisition and compression. This technique is particularly apt for time-series photometric measurements. In this work we apply compressive sensing to time-series photometric measurements specifically obtained due to gravitational microlensing events. We show through simulation modelling the error sensitivity for detecting microlensing event parameters. Particularly, we show the relation of the amount of error and its impact on the microlensing parameters of interest. We derive statistical error bounds to apply those as a baseline for analyzing the effectiveness of compressive sensing application. Our results conclude that for single and binary microlensing events we can obtain error less than 1% over a 3-pixel radius of the center of the microlensing star by using 25% Nyquist rate measurements.

Compressive Sensing↗

NASA NTRS2002

Dynamically controlled crystal growth system

Crystal growth can be initiated and controlled by dynamically controlled vapor diffusion or temperature change. In one aspect, the present invention uses a precisely controlled vapor diffusion approach to monitor and control protein crystal growth. The system utilizes a humidity sensor and various interfaces under computer control to effect virtually any evaporation rate from a number of different growth solutions simultaneously by means of an evaporative gas flow. A static laser light scattering sensor can be used to detect aggregation events and trigger a change in the evaporation rate for a growth solution. A control/follower configuration can be used to actively monitor one chamber and accurately control replicate chambers relative to the control chamber. In a second aspect, the invention exploits the varying solubility of proteins versus temperature to control the growth of protein crystals. This system contains miniature thermoelectric devices under microcomputer control that change temperature as needed to grow crystals of a given protein. Complex temperature ramps are possible using this approach. A static laser light scattering probe also can be used in this system as a non-invasive probe for detection of aggregation events. The automated dynamic control system provides systematic and predictable responses with regard to crystal size. These systems can be used for microgravity crystallization projects, for example in a space shuttle, and for crystallization work under terrestial conditions. The present invention is particularly useful for macromolecular crystallization, e.g. for proteins, polypeptides, nucleic acids, viruses and virus particles.

Bray, Terry L.↗

NASA NTRS1977

On propagation direction of ring current proton ULF waves observed by ATS 6 at 6.6 R sub E

From June 11 to September 16, 1974, the NOAA low-energy proton detector on board the ATS 6 satellite observed 71 cases of ultralow-frequency oscillations of proton flux intensities. The oscillation periods varied from 40 s to 6 min, and the events were observed most frequently during moderate geomagnetic conditions. The flux oscillations occurred at various local times, yet almost two thirds of the events were detected in the near-dusk region of the magnetosphere. For a majority of the events in this set a substantial phase shift in flux oscillation was detected between different energy channels and/or between two oppositely oriented detector telescopes. The phase shift is mainly due to the finite gyroradius effect of the protons gyrating in the geomagnetic field. By examining this finite gyroradius effect on the perturbed particle distribution function associated with the wave in a nonuniform magnetic field, the propagation direction of the wave from particle observations made by a single spacecraft is determined

Su, S.-Y.↗

NASA NTRS2005

An Expert System for Autonomous Spacecraft Control

The Autonomous Sciencecraft Experiment (ASE), part of the New Millennium Space Technology 6 Project, is flying onboard the Earth Orbiter 1 (EO-1) mission. The ASE software enables EO-1 to autonomously detect and respond to science events such as: volcanic activity, flooding, and water freeze/thaw. ASE uses classification algorithms to analyze imagery onboard to detect chang-e and science events. Detection of these events is then used to trigger follow-up imagery. Onboard mission planning software then develops a response plan that accounts for target visibility and operations constraints. This plan is then executed using a task execution system that can deal with run-time anomalies. In this paper we describe the autonomy flight software and how it enables a new paradigm of autonomous science and mission operations. We will also describe the current experiment status and future plans.

utonomy flight software↗

DOE OSTI2026

FY 2026 Midyear Report: Seismic Monitoring of Underground Vibration Sources Using Distributed Acoustic Sensing and Seismometers

Safeguards-relevant temporal changes in underground facilities can be observed using geophysical monitoring techniques. Seismic waves, in particular, provide valuable insights into subsurface activities and can serve as an important tool for detecting anomalous events that may indicate containment breaches at geological repositories. This midyear report summarizes ongoing efforts to automatically and rapidly detect and locate anomalous vibration signals that could be indicative of potential containment breaches. Previous work during FY25 focused on compiling continuous seismic datasets from two underground sites and developing a database of continuous waveforms and ground-truth event data derived from multiple sensing modalities. Building on this foundation, we are adapting anomaly detection and geolocation algorithms to explore methods for monitoring underground activities using two relatively low-maintenance sensing technologies: a dense surface geophone array deployed at the Pleasant Gap mine in Pennsylvania, and a three-dimensional fiber-optic cable array for distributed acoustic sensing (DAS) installed in the subsurface at the Sanford Underground Research Facility (SURF) in South Dakota. This report summarizes work conducted during the first two quarters of FY26, during which we refined a dynamic power spectral density (PSD)-based detector, applied it independently to each geophone station, and then combined the per‑station detections with density-based spatial clustering of applications with noise (DBSCAN) to cluster events and produce spatial maps over a nine‑day interval. In addition, we outline plans for a field trial at the Waste Isolation Pilot Plant (WIPP) in New Mexico to compare traditional seismic monitoring approaches with DAS techniques and to evaluate the benefits of combined data analysis. Activities during the past two quarters have included the preparation and submission of a Field Test Plan to WIPP for approval, as well as submission to headquarters for review and feedback.

58 GEOSCIENCES↗

NASA NTRS2010

All-Sky Monitoring with the Fermi Gamma Ray Burst Monitor

We are currently monitoring the transient hard X-ray/soft gamma ray sky using the Gamma Ray Burst Monitor (GBM) on-board Fermi. The twelve GBM NaI detectors span 8 keV to 1MeV, while the two GBM BGO detectors span about 150 keV to 40 MeV. With GBM, we detect transient events on multiple timescales. Brief events, such as Gamma Ray Bursts, Solar flares, and magnetar bursts are detected with on-board triggers. On longer timescales, we use the Earth occultation technique to monitor a number of sources, including X-ray binaries, AGN, and solar flaring activity. To date we have detected 7 sources above 100 keV. Transient activity from accretion-powered pulsars is monitored using epoch-folding techniques. With GBM we track the pulsed flux and frequency for a number of pulsars. We will present highlights of GBM observations on various timescales.

Wilson-Hodge, Colleen A.↗

DOE OSTI2026

Expected sensitivity of the Light Dark Matter eXperiment to long-lived dark photons and axion-like particles

The Light Dark Matter eXperiment (LDMX) is an electron-beam fixed-target experiment primarily designed to achieve world-leading, model-independent sensitivity to sub-GeV dark matter particles. LDMX aims to identify dark sector particle production through the detection of events with substantial missing energy and momentum, a signature of invisible particles escaping detection. Beyond this primary objective, LDMX offers a complementary search strategy for long-lived, visibly decaying particles, such as dark photons and axion-like particles. We present the first detailed evaluation of the ability of LDMX to identify visibly decaying, long-lived particles that couple to electrons using a detailed simulation, based on the Geant 4-toolkit, that incorporates realistic detection efficiencies and background levels. We demonstrate that LDMX can achieve a sensitivity that is competitive with other experiments that are currently running. The models explored in this paper are distinct and complementary to those probed in the LDMX flagship missing-momentum analysis. Through searching for both invisible dark matter and visibly decaying long-lived signatures, LDMX will significantly advance the search for light dark matter and provide a broad exploration of the sub-GeV dark sector.[graphic not available: see fulltext]

Akesson, Torsten [Lund U.] (ORCID:0000000341415408↗

DOE OSTI2026

Synthesis of ARM User Facility Surface Rainfall Datasets to Construct a Best Estimate Value Added Product (PrecipBE)

Surface precipitation measurements are essential for Earth system model (ESM) evaluation and understanding cloud processes. An ever-growing need for robust, temporally evolving, and easy-to-use statistical datasets provides motivation for a baseline ground-based precipitation properties data product. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility operates an extensive suite of precipitation instruments with various sensitivities and operating mechanisms, which render the decision of which instrument to use based on one or more fixed thresholds challenging and prone to errors and bias. Using a long-term instrument inter-comparison from a unique per-precipitation event perspective, rather than instantaneous sample comparison, we demonstrate that ARM rainfall-measuring instruments are generally consistent with each other at the statistical level. Inter-instrument deviations at the single event level can be large, especially for specific rainfall event properties such as maximum precipitation rates. A machine-learning (ML) analysis using a random forest regressor indicates that in some cases, depending on instrument, local site climatology, and/or specific deployment configuration, certain atmospheric state variables influence the measured quantities in an unpredictable manner. Thus, a-priori weighting of different instruments does not necessarily lead to more accurate and less biased synthesis of instrument data. These results motivate the design of the ARM precipitation best-estimate (PrecipBE) value-added product, which incorporates all valid precipitation data while considering data quality and other instrument limitations. PrecipBE consists of time series and tabular statistics datasets in an easy-to-use and insightful per-precipitation event format. It provides a large set of precipitation event properties supplemented with ancillary data from ARM datasets that correspond to the detected precipitation events. We describe the PrecipBE algorithm and demonstrate its use via the examination of a single-day output as well as a long-term trend analysis of precipitation events at the ARM Southern Great Plains (SGP) site, covering more than 30 years of data. The trend analysis tentatively suggests a long-term temporal tendency for mainly shorter and less intense precipitation events at the SGP site, but a long-term increase in annual rainfall by more than 36 mm (5 %) per decade. This rainfall trend is catalyzed primarily by more extreme event properties of relatively rare, intense precipitation events, with event total and 1 min maximum precipitation rate at a 1 year timeframe increasing up to 5 mm and 9 mm h −1 (several percent) per decade, respectively. While the currently available PrecipBE datasets (at https://adc.arm.gov/discovery/, last access: 8 December 2025) cover rainfall from multiple ARM deployments up to March 2025, PrecipBE is planned to be expanded to include solid-phase precipitation and will soon become an operational product with a several-day lag from real-time. We invite the ARM user community to leverage this new product and welcome user feedback to further enhance the dataset.

Silber, Israel [Pacific Northwest National Laborat↗

NASA NTRS2003

Dying Pulse Trains in Cygnus XR-1: Initial Results of X-Ray Searches

Dying pulse trains (DPT's) are a signature of a black hole as described by general relativity. Detecting DPT's would establish the existence of black holes by ruling out more exotic objects in systems in which a neutron star or white dwarf component has already been excluded by maximum mass arguments. The positive identification of a black hole would also be an additional test of general relativity. Two possible DPT's were detected in W photometry of Cygnus XR-1, the leading candidate for a stellar mass sized BH, in 3 hours of observational data. A search of X-ray photometry of Cygnus XR-1 from the Ross1 X-ray Timing Explorer (RXTE) has begun. No DPT's have been detected in the first 4 hours of data searched. Because of the low event rate detected in the W data, these initial results are consistent with such disparate scenarios as the rate of DPT occurrence being dependent on the luminosity state of the system; or being more difficult to detect in the X-ray region relative to the W region; or occurring at the same rate in the W and X-ray regions; or even not occurring at all from Cygnus XR-1. The search for DPT's in RXTE photometry is continuing.

Dolan, Joseph F.↗

NASA NTRS2004

Autonomous Multi-sensor Coordination: The Science Goal Monitor

Next-generation science and exploration systems will employ new observation strategies that will use multiple sensors in a dynamic environment to provide high quality monitoring, self-consistent analyses and informed decision making. The Science Goal Monitor (SGM) is a prototype software tool being developed to explore the nature of automation necessary to enable dynamic observing of earth phenomenon. The tools being developed in SGM improve our ability to autonomously monitor multiple independent sensors and coordinate reactions to better observe the dynamic phenomena. The SGM system enables users to specify events of interest and how to react when an event is detected. The system monitors streams of data to identify occurrences of the key events previously specified by the scientist/user. When an event occurs, the system autonomously coordinates the execution of the users desired reactions between different sensors. The information can be used to rapidly respond to a variety of fast temporal events. Investigators will no longer have to rely on after-the-fact data analysis to determine what happened. Our paper describes a series of prototype demonstrations that we have developed using SGM and NASA's Earth Observing-1 (EO-1) satellite and Earth Observing Systems Aqua/Terra spacecrafts MODIS instrument. Our demonstrations show the promise of coordinating data from different sources, analyzing the data for a relevant event, autonomously updating and rapidly obtaining a follow-on relevant image. SGM is being used to investigate forest fires, floods and volcanic eruptions. We are now identifying new earth science scenarios that will have more complex SGM reasoning. By developing and testing a prototype in an operational environment, we are also establishing and gathering metrics to gauge the success of automating science campaigns.

Koratkar, Anuradha↗

NASA NTRS2018

Wind Event Warning System

A method of detecting a wind event utilizing Doppler lidar includes causing a Doppler lidar unit to operate in a course scan mode in which a lidar beam is scanned along several directions. Line-of-sight wind speed measurements are generated at each direction and a derivative with respect to distance of the line-of-sight wind speed measurements is calculated at each of the directions. A predicted angular position of a wind event is determined, and the lidar unit may then operate in a fine scan mode in which only a limited sector is scanned. The potential wind event is then quantified utilizing data obtained using the fine scan mode.

Koch, Grady J.↗

DOE OSTI2025

Detection and Association of Operational Events using DAS and Seismometers (FY 2025 Mid-Year Report)

This mid-year report summarizes ongoing work to identify anomalous vibration signals indicative of potential containment breaches. This work includes compiling continuous seismic datasets and testing and refining underground detection and geolocation techniques. In the first two quarters of FY25, we have completed two project work plan tasks: (1) creating a database of continuous waveforms and ground truth event data from multiple modalities and (2) refining and implementing a detection and association algorithm to create a catalog of anomalous underground activities. This report contains a summary of the seismic database including the continuous seismic data collected by a dense array of surface seismic stations above Pleasant Gap Mine, and continuous seismic data collected using subsurface distributed acoustic sensing (DAS) in the subsurface at Sanford Underground Research Facility (SURF) and the ground truth information gathered from both sites. This report also includes results from refining and applying a dynamic power spectral density detector to both continuous seismic datasets. Finally, the report provides an initial catalog of subsurface operational events from both sensing modalities.

58 GEOSCIENCES↗

NASA NTRS1976

Search for gamma ray bursts with coincident balloon flights

A search was conducted for cosmic gamma ray bursts of small size and of sufficient frequency of occurrence to be detected during a one day observation program. Two similar detectors, successfully balloon-borne from launch sites in South Dakota and Texas, achieved about 20 hours of simultaneous operation at several millibars atmospheric depth, with continuous separation of over 1,500 km. Fluctuations of the counting rates of less than 150 keV photons with temporal structures from microseconds to several minutes were compared in order to detect coincident or associated responses from the two instruments. No coincident gamma-ray burst events were detected. The resulting integral size spectrum of small bursts, from this and from all other searches, remains a spectrum of upper limits, consistent with an extrapolation of the size spectrum of the largest known bursts, fitting a power low of index -1.5.

Cline, T. L.↗

NASA NTRS1986

Large amplitude wave packets observed in the ionosphere in association with transverse ion acceleration

Very short duration, large amplitude bursts of monochromatic waves ('spikelets') were detected by the electric field experiment on the sounding rocket MARIE, launched in February 1985 from Churchill, Manitoba. About 35 events were detected, with an average time scale of 5 ms and an average amplitude of 100-150 mV/m. Their frequency varied between 7 and 18 kHz, and there is some evidence that the frequency is a decreasing function of altitude. The bursts are not correlated with any events on the payload, and their occurrence is not related to the rocket's spin or coning. The events were confined to the altitude range 450-650 kilometers. This coincides exactly with the altitude range for which perpendicular (90 deg) ion conics were detected by the particle experiment on the same payload. The 'spikelets' were also correlated one-to-one with small (10-100 mV/m) double-layerlike or shocklike features of similar time scale in the dc electric field data.

Labelle, J.↗

DOE OSTI2026

Employing Machine Learning for New Particle Formation Identification and Mechanistic Analysis: Insights From a Six‐Year Observational Study in the Southern Great Plains

We present a supervised machine learning (ML) framework to automatically identify new particle formation (NPF) events and analyze key atmospheric factors associated with their occurrence and growth. We applied ML to detect NPF events using start time and particle concentrations across size ranges, while identifying atmospheric variables including ambient temperature, relative humidity, solar radiation intensity (SRI), wind speed, wind direction, boundary layer height, total organics, sulfate, nitrate, total surface area concentration, sulfur dioxide, and turbulent kinetic energy (TKE). We analyzed a 6-year data set from the Atmospheric Radiation Measurement at the Southern Great Plains (SGP) site in Oklahoma, USA. Using long-term ground-based measurements, we identified NPF events and applied Random Forest Classifiers, which achieved 90%–95% prediction accuracy. Feature importance analysis highlighted SRI, relative humidity, and ambient temperature as the most influential variables, contributing normalized importances of 28%, 17%, and 10%. Partial Dependence Plots (PDPs) indicated that higher SRI and lower relative humidity were critical in promoting NPF formation at SGP. Seasonally, NPF events were more frequent in winter (42.1%) and spring (35.5%), and least in summer (4.0%). Particle growth rates also exhibited a seasonal variation, with the lowest in winter (below 2 nm hr −1 ) and highest in late spring and early summer (exceeding 5 nm hr −1 ). Temperature, turbulent kinetic energy, and aerosol properties were the primary factors of growth rate variability. This study advances predictive modeling of NPF, offers insights for future campaign deployments, and demonstrates the effectiveness of ML in understanding the formation and growth of atmospheric aerosols.

54 ENVIRONMENTAL SCIENCES↗