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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Legacy Survey of Space and Time Data Preview 1: dia_object_forced_source dataset type

The Legacy Survey of Space and Time Data Preview 1 (DP1) is the first release of data from the NSF-DOE Vera C. Rubin Observatory. It consists of raw and calibrated single-epoch images, co-adds, difference images, detection catalogs, and other derived data products. DP1 is based on 1792 science-grade optical/near-infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera, LSSTComCam, on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile during the first on-sky commissioning campaign in late 2024. DP1 covers a total of approximately 15 sq. deg. over seven roughly equally-sized non-contiguous fields, each independently observed in six broad photometric bands, ugrizy, spanning a range of stellar densities and latitudes and overlapping with external reference datasets. This dataset is a subset of the full data release consisting of the dia_object_forced_source dataset type. These are forced measurements in visit and difference images, at the coordinates of all DIA objects. This release contains 597 datasets of this type.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Solving multiphysics-based inverse problems with learned surrogates and constraints

Abstract Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically. We overcome these challenges by combining computationally cheap learned surrogates with learned constraints. Not only does this combination lead to vastly improved inversions for the important fluid-flow property, permeability, it also provides a natural platform for inverting multimodal data including well measurements and active-source time-lapse seismic data. By adding a learned constraint, we arrive at a computationally feasible inversion approach that remains accurate. This is accomplished by including a trained deep neural network, known as a normalizing flow, which forces the model iterates to remain in-distribution, thereby safeguarding the accuracy of trained Fourier neural operators that act as surrogates for the computationally expensive multiphase flow simulations involving partial differential equation solves. By means of carefully selected experiments, centered around the problem of geological carbon storage, we demonstrate the efficacy of the proposed constrained optimization method on two different data modalities, namely time-lapse well and time-lapse seismic data. While permeability inversions from both these two modalities have their pluses and minuses, their joint inversion benefits from either, yielding valuable superior permeability inversions and CO 2 plume predictions near, and far away, from the monitoring wells.

Yin, Ziyi (ORCID:0000000250248771)↗
DOE OSTI2025

Assessing Dynamic Time Warping Techniques for Discriminating Seismic Sources at Local and Regional Distances

Effective monitoring of seismic explosions and hazard assessment relies heavily on the accurate discrimination of underground seismic sources. This study investigates the application of novel nonlinear alignment techniques, specifically Dynamic Time Warping (DTW), for event-type discrimination at regional and local distances. Building on prior research that used DTW and Elastic Shape Analysis (ESA) in discrimination at regional distances, we evaluate the performance of recently developed variants of DTW, including a method that employs Pearson cross-correlation as a measure of warping distance and a time distortion coefficient that quantifies the type and degree of time distortion between signals. By analyzing observational datasets that include different source types, we assess the performance of these approaches for realistic monitoring scenarios. Specifically, we consider a dataset recorded at regional distances in the Korean Peninsula and a local-distance subset from the Unconstrained Utah Event Bulletin catalog to evaluate DTW-based discrimination across multiple distance scales. Additionally, we introduce the maximum cross-correlations of warped waveforms as a similarity metric for event classification. Through hierarchical cluster analysis and dendrogram interpretation, we present our findings, highlighting the strengths and limitations of these techniques in seismic event classification.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

IceCube-Gen2: the window to the extreme Universe

The observation of electromagnetic radiation from radio to γ-ray wavelengths has provided a wealth of information about the Universe. However, at PeV (10 15 eV) energies and above, most of the Universe is impenetrable to photons. New messengers, namely cosmic neutrinos, are needed to explore the most extreme environments of the Universe where black holes, neutron stars, and stellar explosions transform gravitational energy into non-thermal cosmic rays. These energetic particles havemillions of times higher energies than those produced in the most powerful particle accelerators on Earth. As neutrinos can escape from regions otherwise opaque to radiation, they allow an unique view deep into exploding stars and the vicinity of the event horizons of black holes. The discovery of cosmic neutrinos with IceCube has opened this new window on the Universe. IceCube has been successful in finding first evidence for cosmic particle acceleration in the jet of an active galactic nucleus. Yet, ultimately, its sensitivity is too limited to detect even the brightest neutrino sources with high significance, or to detect populations of less luminous sources. In thiswhite paper, we present an overview of a next-generation instrument, IceCube-Gen2, which will sharpen our understanding of the processes and environments that govern the Universe at the highest energies. IceCube-Gen2 is designed to: (a) Resolve the high-energy neutrino sky from TeV to EeV energies (b) Investigate cosmic particle acceleration through multi-messenger observations (c) Reveal the sources and propagation of the highest energy particles in the Universe (d) Probe fundamental physics with high-energy neutrinos IceCube-Gen2 will enhance the existing IceCube detector at the South Pole. It will increase the annual rate of observed cosmic neutrinos by a factor of ten compared to IceCube, and will be able to detect sources five times fainter than its predecessor. Furthermore, through the addition of a radio array, IceCube- Gen2 will extend the energy range by several orders of magnitude compared to IceCube. Construction will take 8 years and cost about $350M. The goal is to have IceCube-Gen2 fully operational by 2033. IceCube-Gen2 will play an essential role in shaping the new era of multimessenger astronomy, fundamentally advancing our knowledge of the highenergy Universe. Overall, this challenging mission can be fully addressed only through the combination of the information from the neutrino, electromagnetic, and gravitational wave emission of high-energy sources, in concert with the new survey instruments across the electromagnetic spectrum and gravitational wave detectors which will be available in the coming years.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Electrical Fault and Power Quality Detection Algorithms and Customer-Owned DERs Monitoring with a Cyber Grid Guard System and DLT

In this study, the electrical fault and power quality detection algorithms and customer-owned DERs monitoring use cases were implemented, with a Cyber Grid Guard system and DLT. Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substations, and are associated with customer-owned distributed energy resources (DERs). Data from these IEDs, such as power meters and protection relays, must be kept confidential and of high integrity. Blockchain technology has the potential to increase microgrid resilience by enhancing data sharing security. The growing use of IEDs and customer-owned renewable energy sources (DERs) may make it necessary to connect Distributed Ledger Technology (DLT) with power system applications. We implemented the electrical faulted phase detection and power quality monitoring algorithms with a Cyber Grid Guard (CGG) system using DLT. In addition, the DERs (wind turbine farms) use case and protective relay cyber-event tests were assessed, by using the CGG system with DLT. In the experimental model, the testbed was created by using a real-time simulator and CGG system with power meters/ protective relays in the loop. The data collected from the CGG system and IEDs were compared with the same time stamp source. These results showed the successful assessment of protection, control and monitoring applications using a CGG system with DLT. In the future, power system applications for the ESGT with DERs and the CGG system will be based on executing smart contracts between electrical utilities and customer-owned DERs.

Piesciorovsky, Emilio↗
DOE OSTI2023

Electrical substation grid testbed for DLT applications of electrical fault detection, power quality monitoring, DERs use cases and cyber-events

Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substation, and are associated with customer-owned distributed energy resources (DERs). The integrity and confidentiality of data from these IEDs, like power meters and protective relays, is crucial. Blockchain technology could improve the resilience of microgrids by improving the security of data sharing. The penetration of customer-owned DERs (renewable energy sources) and the increasing deployment of IEDs can lead to integrate power system applications with Distributed Ledger Technology (DLT). In this study, we implemented the electrical faulted phase detection and power quality monitoring algorithms with a Cyber Grid Guard (CGG) system using DLT. In addition, the DERs (wind turbine farms) use case and protective relay cyber-event tests were assessed, by using the CGG system with DLT. In the experimental model, the testbed was created by using a real-time simulator and CGG system with power meters/ protective relays in-the-loop. The data collected from the CGG system and IEDs were compared with the same time stamp source. These results had shown the successful assessment of protection, control and monitoring applications using a CGG system with DLT. In the future, the ESGT with DERs and the CGG system will be used in other power system applications, based on implementing smart contracts between electrical utilities with customer-owned DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest

Civil unrest can range from peaceful protest to violent furor, and researchers are working to monitor, forecast, and assess such events to allocate resources better. Twitter has become a real-time data source for forecasting civil unrest because millions of people use the platform as a social outlet. Additionally, daily word counts are used as model features, and predictive terms contextualize the reasons for the protest. To forecast civil unrest and infer the reasons for the protest, we consider the problem of Bayesian variable selection for the dynamic logistic regression model and propose using penalized credible regions to select parameters of the updated state vector. This method avoids the need for shrinkage priors, is scalable to high-dimensional dynamic data, and allows the importance of variables to vary in time as new information becomes available. A substantial improvement in both precision and F1-score using this approach is demonstrated through simulation. Finally, we apply the proposed model fitting and variable selection methodology to the problem of forecasting civil unrest in Latin America. Our dynamic logistic regression approach shows improved accuracy compared to the static approach currently used in event prediction and feature selection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

COVID-19 Lead Time: Evaluating the timeliness and reliability of reported COVID-19 cases and hospitalizations as leading indicators of hospitalizations and death in the US

The COVID-19 pandemic prompted a reliance on real-time data sources to understand the global spread and impact of the SARS-CoV-2 virus. Reported cases were presumed leading indicators for hospitalizations, while hospitalizations were considered predictive of deaths. However, studies have questioned the consistency of reported cases as leading indicators. This study systematically assesses the reliability of United States (US) reported cases and hospitalizations as leading indicators for hospitalizations and deaths respectively, examining the first 2.5 years of the pandemic (January 2020 - June 2022) across different phases of the pandemic and states. Using correlation analysis, population data, and forecasting accuracy measures, we investigate the temporal relationships and identify possible determinants of lead time variability. Notably, we found that the average lead time between reported cases and hospitalizations across US states is relatively short at 1.76 days, implying that reported cases might not be as effective a leading indicator for hospitalizations as previously believed. Populations with higher comorbidity burdens, such as proportion of smokers, are expected on average to have shorter lead times, possibly due to shorter time to hospitalization among these vulnerable populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

(U) A Theoretical Study of Asay Foil Trajectories (V.02)

We consider the trajectory of a generic Asay foil ejecta momentum diagnostic sensor, for scenarios where ejecta are produced at a planar surface and fly ballistically through a perfect vacuum to the sensor. To do so, we build upon a previously established mathematical formalism derived for the analytic study of stationary sensors (i.e., piezopins). First, we derive the momentum conservation equation for the problem, in a form amenable to accelerating sensors, in terms of a generic ejecta source areal mass function (“source model”). This defines an integro-differential equation (IDE) for the foil trajectory. When ejecta production is instantaneous - as is generally assumed in momentum diagnostic data analyses - the IDE leads to an implicit and easily calculable closed-form solution for the foil trajectory in a perfect system, as long as the ejecta particle velocity distribution is twice-integrable. General properties of the instant-production trajectory solution indicate the existence of a boundary condition the particle velocity distribution must satisfy in order for the analytically predicted foil trajectories to be compatible with certain features commonly observed in foil data. This boundary condition is identical to one derived previously from a consideration of piezopin data. Armed with the analytic solution for instant production, we also consider various techniques used to extract time-dependent cumulative ejecta masses from foil trajectories, and derive an expression for the error imposed by using an approximated equation of motion. This analytic trajectory solution furthermore makes it possible to examine the common practice of presenting inferred cumulative ejecta masses as a function of a normalized implied velocity; we derive conditions under which this methodology is and is not meaningful. We also propose a strategy for extending the instant-production trajectory solution to time-dependent source functions.

42 ENGINEERING↗
DOE OSTI2023

Measuring the properties of f - mode oscillations of a protoneutron star by third-generation gravitational-wave detectors

Core-collapse supernovae are among the astrophysical sources of gravitational waves that could be detected by third-generation gravitational-wave detectors. Here, we analyze the gravitational-wave strain signals from two- and three-dimensional simulations of core-collapse supernovae generated using the code FORNAX. A subset of the two-dimensional simulations has non-zero core rotation at the core bounce. A dominant source of time changing quadrupole moment is the l = 2 fundamental mode (f- mode) oscillation of the proto-neutron star. From the time-frequency spectrogram of the gravitational-wave strain we see that, starting ~ 400 ms after the core bounce, most of the power lies within a narrow track that represents the frequency evolution of the f-mode oscillations. The f-mode frequencies obtained from linear perturbation analysis of the angle averaged profile of the protoneutron star corroborate what we observe in the spectrograms of the gravitational wave signal. We explore the measurability of the f-mode frequency evolution of protoneutron star for a supernova signal observed in the third-generation gravitational-wave detectors. Measurement of the frequency evolution can reveal information about the masses, radii, and densities of the proto-neutron stars. We find that if the third generation detectors observe a supernova within 10 kpc, we can measure these frequencies to within ~90% accuracy. Here, we can also measure the energy emitted in the fundamental f-mode using the spectrogram data of the strain signal. We find that the energy in the f-mode can be measured to within 20% error for signals observed by Cosmic Explorer using simulations with successful explosion, assuming source distances within 10 kpc.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Time-integrated Southern-sky Neutrino Source Searches with 10 yr of IceCube Starting-track Events at Energies Down to 1 TeV

In the IceCube Neutrino Observatory, a signal of astrophysical neutrinos is obscured by backgrounds from atmospheric neutrinos and muons produced in cosmic-ray interactions. IceCube event selections used to isolate the astrophysical neutrino signal often focus on the morphology of the light patterns recorded by the detector. The analyses presented here use the new IceCube Enhanced Starting Track Event Selection (ESTES), which identifies events likely generated by muon–neutrino interactions within the detector geometry, focusing on neutrino energies of 1–500 TeV with a median angular resolution of 1.4°. Selecting for starting-track events filters out not only the atmospheric-muon background but also the atmospheric-neutrino background in the southern sky. This improves IceCube’s muon–neutrino sensitivity to southern-sky neutrino sources, especially for Galactic sources that are not expected to produce a substantial flux of neutrinos above 100 TeV. In this work, the ESTES sample was applied for the first time to search for astrophysical sources of neutrinos, including a search for diffuse neutrino emission from the Galactic plane. No significant excesses were identified from any of the analyses; however, constraining limits are set on the hadronic emission from TeV gamma-ray Galactic plane objects and models of the diffuse Galactic plane neutrino flux.

Abbasi, R. [Loyola University, Chicago, IL (United↗
DOE OSTI2023

Particle Size Optimization of Thermochemical Salt Hydrates for High Energy Density Thermal Storage

Thermal energy storage (TES) solutions offer opportunities to reduce energy consumption, greenhouse gas emissions, and cost. Specifically, they can help reduce the peak load and address the intermittency of renewable energy sources by time shifting the load, which are critical toward zero energy buildings. Thermochemical materials (TCMs) as a class of TES undergo a solid–gas reversible chemical reaction with water vapor to store and release energy with high storage capacities (600 kWh m −3 ) and negligible self‐discharge that makes them uniquely suited as compact, stand‐alone units for daily or seasonal storage. However, TCMs suffer from instabilities at the material (salt particles) and reactor level (packed beds of salt), resulting in poor multi‐cycle efficiency and high‐levelized cost of storage. In this study, a model is developed to predict the pulverization limit or R crit of various salt hydrates during thermal cycling. This is critical as it provides design rules to make mechanically stable TCM composites as well as enables the use of more energy‐efficient manufacturing process (solid‐state mixing) to make the composites. The model is experimentally validated on multiple TCM salt hydrates with different water content, and effect of R crit on hydration and dehydration kinetics is also investigated.

25 ENERGY STORAGE↗
DOE OSTI2024

Light–Material Interactions Using Laser and Flash Sources for Energy Conversion and Storage Applications

Abstract This review provides a comprehensive overview of the progress in light–material interactions (LMIs), focusing on lasers and flash lights for energy conversion and storage applications. We discuss intricate LMI parameters such as light sources, interaction time, and fluence to elucidate their importance in material processing. In addition, this study covers various light-induced photothermal and photochemical processes ranging from melting, crystallization, and ablation to doping and synthesis, which are essential for developing energy materials and devices. Finally, we present extensive energy conversion and storage applications demonstrated by LMI technologies, including energy harvesters, sensors, capacitors, and batteries. Despite the several challenges associated with LMIs, such as complex mechanisms, and high-degrees of freedom, we believe that substantial contributions and potential for the commercialization of future energy systems can be achieved by advancing optical technologies through comprehensive academic research and multidisciplinary collaborations.

Materials Science↗
DOE OSTI2021

Nonparametric, data-based kernel interpolation for particle-tracking simulations and kernel density estimation

Traditional interpolation techniques for particle tracking include binning and convolutional formulas that use pre-determined (i.e., closed-form, parameteric) kernels. In many instances, the particles are introduced as point sources in time and space, so the cloud of particles (either in space or time) is a discrete representation of the Green’s function of an underlying PDE. As such, each particle is a sample from the Green’s function; therefore, each particle should be distributed according to the Green’s function. In short, the kernel of a convolutional interpolation of the particle sample “cloud” should be a replica of the cloud itself. This idea gives rise to an iterative method by which the form of the kernel may be discerned in the process of interpolating the Green’s function. When the Green’s function is a density, this method is broadly applicable to interpolating a kernel density estimate based on random data drawn from a single distribution. We formulate and construct the algorithm and demonstrate its ability to perform kernel density estimation of skewed and/or heavy-tailed data including breakthrough curves.

42 ENGINEERING↗
DOE OSTI2025

Light nuclei femtoscopy and baryon interactions in 3 GeV Au+Au collisions at RHIC

We report the measurements of proton-deuteron (𝑝-𝑑) and deuteron-deuteron (𝑑-𝑑) correlation functions in Au + Au collisions at $\sqrt{s_{NN}}$ = 3 GeV using fixed-target mode with the STAR experiment at the Relativistic Heavy-Ion Collider (RHIC). For the first time, the source size (𝑅 𝐺 ), scattering length (𝑓 0 ), and effective range (𝑑 0 ) are extracted from the measured correlation functions with a simultaneous fit. The spin-averaged 𝑓 0 for 𝑝-𝑑 and 𝑑-𝑑 interactions are determined to be -5.28 ± 0.11(stat.) ± 0.82(syst.) fm and -2.62 ± 0.02(stat.) ±0.24(syst.) fm, respectively. The measured 𝑝-𝑑 interaction is consistent with theoretical calculations and low energy scattering experiment results, demonstrating the feasibility of extracting interaction parameters using the femtoscopy technique. The reasonable agreement between the experimental data and the calculations from the transport model indicates that deuteron production in these collisions is primarily governed by nucleon coalescence.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Transforming US agriculture for carbon removal with enhanced weathering

Abstract Enhanced weathering (EW) with agriculture uses crushed silicate rocks to drive carbon dioxide removal (CDR) 1,2 . If widely adopted on farmlands, it could help achieve net-zero emissions by 2050 2–4 . Here we show, with a detailed US state-specific carbon cycle analysis constrained by resource provision, that EW deployed on agricultural land could sequester 0.16–0.30 GtCO 2 yr −1 by 2050, rising to 0.25–0.49 GtCO 2 yr −1 by 2070. Geochemical assessment of rivers and oceans suggests effective transport of dissolved products from EW from soils, offering CDR on intergenerational timescales. Our analysis further indicates that EW may temporarily help lower ground-level ozone and concentrations of secondary aerosols in agricultural regions. Geospatially mapped CDR costs show heterogeneity across the USA, reflecting a combination of cropland distance from basalt source regions, timing of EW deployment and evolving CDR rates. CDR costs are highest in the first two decades before declining to about US$100–150 tCO 2 −1 by 2050, including for states that contribute most to total national CDR. Although EW cannot be a substitute for emission reductions, our assessment strengthens the case for EW as an overlooked practical innovation for helping the USA meet net-zero 2050 goals 5,6 . Public awareness of EW and equity impacts of EW deployment across the USA require further exploration 7,8 and we note that mobilizing an EW industry at the necessary scale could take decades.

Science & Technology - Other Topics↗
DOE OSTI2026

SAIGE-GPU: accelerating genome- and phenome-wide association studies using GPUs

Genome-wide association studies (GWAS) at biobank scale are computationally intensive, especially for admixed populations requiring robust statistical models. SAIGE is a widely used method for generalized linear mixed-model GWAS but is limited by its CPU-based implementation, making phenome-wide association studies impractical for many research groups. We developed SAIGE-GPU, a GPU-accelerated version of SAIGE that replaces CPU-intensive matrix operations with GPU-optimized kernels. The core innovation is distributing genetic relationship matrix calculations across GPUs and communication layers. Applied to 2068 phenotypes from 635 969 participants in the Million Veteran Program, including diverse and admixed populations, SAIGE-GPU achieved a 5-fold speedup in mixed model fitting on supercomputing infrastructure and cloud platforms. We further optimized the variant association testing step through multi-core and multi-trait parallelization. Deployed on Google Cloud Platform and Azure, the method provided substantial cost and time savings. Source code and binaries are available for download at https://github.com/saigegit/SAIGE/tree/SAIGE-GPU-1.3.3. A code snapshot is archived at Zenodo for reproducibility (DOI: [10.5281/zenodo.17642591]). SAIGE-GPU is available in a containerized format for use across HPC and cloud environments and is implemented in R/C++ and runs on Linux systems.

Rodriguez, Alex [Argonne National Laboratory (ANL)↗
DOE OSTI2025

First constraints on a pixelated universe in light of DESI observations

Pixelated dark energy is a string theory scenario with a quantum mechanically stable cosmological constant. The number of pixels that make up the universe slowly increases, manifesting as a time-dependent source of dark energy. DESI has recently reported evidence for dynamical dark energy that fits within this framework. In light of this, we perform the first cosmological analysis of the pixelated model. We find that the simplest model where the pixel growth rate is constant is able to accommodate the data, providing a marginally better fit than Λ⁢ CDM; and we show that models where the pixel growth rate is increasing and of order the Hubble constant today could provide better fits. Our analysis helps to clarify the features of UV constructions of dark energy necessary to accommodate the data.

dark energy↗