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NASA NTRS1985

Fault models

A major problem in the qualification of integrated circuit cells and in the development of adequate tests for the circuits is to lack of information on the nature and density of fault models. Some of this information is being obtained from the test structures. In particular, the Pinhole Array Capacitor is providing values for the resistance of gate oxide shorts, and the Addressable Inverter Matrix is providing values for parameter distributions such as noise margins. Another CMOS fault mode, that of the open-gated transistor, is examined and the state of the transistors assessed. Preliminary results are described for a number of open-gated structures such as transistors, inverters, and NAND gates. Resistor faults are applied to various CMOS gates and the time responses are noted. The critical value for the resistive short to upset the gate response was determined.

Sayah, H. R.↗

NASA NTRS1990

A test of the galactic origin of gamma-ray bursts

Models in which gamma-ray bursters are young neutron stars may be difficult to reconcile with the apparently isotropic distribution of observed bursts. To justify this statement, the distribution of old neutron stars in the Galaxy was calculated by integrating numerically some 90,000 orbits in the Galactic gravitational potential for up to 10 to the 10th yr. Other calculations included the dipole moment in the angular distribution of neutron stars as a convenient measure of their concentration toward the Galactic center, the quadrupole moment in their angular distribution as a convenient measure of their concentration toward the Galactic plane, and the average value of V/Vmax as a convenient measure of the radial distribution of neutron stars. Models that require accretion from a cold circumstellar disk or from a close companion are only marginally consistent with the isotropic distribution of the observed bursts. Models that require accretion of interstellar matter are ruled out due to a very strong dipole anisotropy. Models that suggest the Galactic halo origin of gamma-ray bursts are briefly discussed. The GRANAT and GRO missions should provide evidence for or against the association of gamma-ray bursts with the Galactic disk neutron stars.

Paczynski, Bohdan↗

NASA NTRS2001

Thermal Electron Contributions to Current-Driven Instabilities: SCIFER Observations in the 1400-km Cleft Ion Fountain and Their Implications to Thermal Ion Energization

SCIFER TECHS observations of the variations in the thermal electron distribution in the 1400-km altitude cleft are associated with periods of intense ion heating and field-aligned currents. Energization of the thermal ion plasma in the mid-altitude cleft occurs within density cavities accompanied by enhanced thermal electron temperatures, large field-aligned thermal electron plasma flows and broadband low-frequency electric fields. Variations in the thermal electron contribution to field-aligned current densities indicate small scale (approximately 100's m) filamentary structure embedded within the ion energization periods. TECHS observations of the field-aligned drift velocities and temperatures of the thermal electron distribution are presented to evaluate the critical velocity thresholds necessary for the generation of electrostatic ion cyclotron and ion acoustic instabilities. This analysis suggests that, during periods of thermal ion energization, sufficient drift exists in the thermal electron distribution to excite the electrostatic ion cyclotron instability. In addition, brief periods exist within the same interval where the drift of the thermal electron distribution is sufficient to marginally excite the ion acoustic instability. In addition, the presence an enhancement in Langmuir emission at the plasma frequency at the center of the ion energization region, accompanied by the emission's second-harmonic, and collocated with observations of high-frequency electric field solitary structures suggest the presence of electron beam driven decay of Langmuir waves to ion acoustic modes as an additional free energy source for ion energization.

Adrian, Mark L.↗

NASA NTRS2011

Satellite-Derived Distributions, Inventories and Fluxes of Dissolved and Particulate Organic Matter Along the Northeastern U.S. Continental Margin

Estuaries and the coastal ocean experience a high degree of variability in the composition and concentration of particulate and dissolved organic matter (DOM) as a consequence of riverine and estuarine fluxes of terrigenous DOM, sediments, detritus and nutrients into coastal waters and associated phytoplankton blooms. Our approach integrates biogeochemical measurements, optical properties and remote sensing to examine the distributions and inventories of organic carbon in the U.S. Middle Atlantic Bight and Gulf of Maine. Algorithms developed to retrieve colored DOM (CDOM), Dissolved (DOC) and Particulate Organic Carbon (POC) from NASA's MODIS-Aqua and SeaWiFS satellite sensors are applied to quantify the distributions and inventories of DOC and POC. Horizontal fluxes of DOC and POC from the continental margin to the open ocean are estimated from SeaWiFS and MODIS-Aqua distributions of DOC and POC and horizontal divergence fluxes obtained from the Northeastern North Atlantic ROMS model. SeaWiFS and MODIS imagery reveal the importance of estuarine outflow to the export of CDOM and DOC to the coastal ocean and a net community production of DOC on the shelf.

Mannino, A.↗

NASA NTRS1987

A study of a flux-limited sample of IRAS galaxies

Redshift data and accurate four-color infrared photometry are presented for a complete IRAS sample of galaxies brighter than 2 Jy at 60 microns. A simple power law provides a good fit to the distribution for luminosities from 10 to the 10th to 10 to the 12th solar. There is no indication of an exponential dropoff in the luminosity function at high energies. A flattening of the luminosity function occurs at L less than 10 to the 20th solar. The highest luminosity galaxies typically are found in multiple, possibly interacting, systems and exhibit marginally narrower infrared spectral energy distributions than the isolated spirals which predominate at low luminosities. Infrared-bright galaxies come from a different population than the majority of optically bright galaxies. In particular, galaxies of low blue luminosity are not strong infrared emitters.

Smith, Beverly J.↗

NASA NTRSDate not supplied

Sampling Functions from Gaussian Processes and Structured Covariance Gaussian Networks

When learning aerodynamic models from data, it is critical to incorporate estimates of model uncertainty. This motivates the design of probabilistic aerodynamic databases which can be sampled to generate physically and statistically plausible aerodynamic models. In this talk we discuss how to sample deterministic functions from two different kinds of probabilistic models and demonstrate their use. First, Gaussian Process Regressors (GPRs) are a widely used probabilistic kernel-based model which can be thought of as Gaussian distributions over functions. GPRs are generally trained by maximizing the marginal likelihood of seeing the training data over the kernel parameter space. Sample functions are easily generated by drawing points from the Gaussian distribution at desired input points. However, when the points are not known ahead of time, the classical sampling approach is not possible since successive function samples will generate different function realizations. We present an approach for sampling consistent function evaluations from a GPR over multiple samples. Second, we describe a neural network architecture which learns a conditional Gaussian distribution by maximizing the marginal likelihood at each point in the input space. We then discuss and compare several options for generating sample functions which match this distribution. Finally, we demonstrate the use of these probabilistic aerodynamic models in an atmospheric reentry simulation.

Gaussian process regression↗

DOE OSTI2022

Exploiting non-linear scales in galaxy–galaxy lensing and galaxy clustering: A forecast for the dark energy survey

ABSTRACT The combination of galaxy–galaxy lensing (GGL) and galaxy clustering is a powerful probe of low-redshift matter clustering, especially if it is extended to the non-linear regime. To this end, we use an N-body and halo occupation distribution (HOD) emulator method to model the redMaGiC sample of colour-selected passive galaxies in the Dark Energy Survey (DES), adding parameters that describe central galaxy incompleteness, galaxy assembly bias, and a scale-independent multiplicative lensing bias Alens. We use this emulator to forecast cosmological constraints attainable from the GGL surface density profile ΔΣ(rp) and the projected galaxy correlation function wp, gg(rp) in the final (Year 6) DES data set over scales $r_p=0.3\!-\!30.0\, h^{-1} \, \mathrm{Mpc}$. For a $3{{\ \rm per\ cent}}$ prior on Alens we forecast precisions of $1.9{{\ \rm per\ cent}}$, $2.0{{\ \rm per\ cent}}$, and $1.9{{\ \rm per\ cent}}$ on Ωm, σ8, and $S_8 \equiv \sigma _8\Omega _m^{0.5}$, marginalized over all halo occupation distribution (HOD) parameters as well as Alens. Adding scales $r_p=0.3\!-\!3.0\, h^{-1} \, \mathrm{Mpc}$ improves the S8 precision by a factor of ∼1.6 relative to a large scale ($3.0\!-\!30.0\, h^{-1} \, \mathrm{Mpc}$) analysis, equivalent to increasing the survey area by a factor of ∼2.6. Sharpening the Alens prior to $1{{\ \rm per\ cent}}$ further improves the S8 precision to $1.1{{\ \rm per\ cent}}$, and it amplifies the gain from including non-linear scales. Our emulator achieves per cent-level accuracy similar to the projected DES statistical uncertainties, demonstrating the feasibility of a fully non-linear analysis. Obtaining precise parameter constraints from multiple galaxy types and from measurements that span linear and non-linear clustering offers many opportunities for internal cross-checks, which can diagnose systematics and demonstrate the robustness of cosmological results.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Convex relaxation for Fokker–Planck equation

We propose an approach to directly estimate the moments or marginals for a high-dimensional equilibrium distribution in statistical mechanics by solving the high-dimensional Fokker–Planck equation in terms of low-order cluster moments or marginals. With this approach, we bypass the exponential complexity of estimating the full high-dimensional distribution and directly solve the simplified partial differential equations for low-order moments/marginals. Moreover, the proposed moment/marginal relaxation is fully convex and can be solved via off-the-shelf solvers. We further propose a time-dependent version of the convex programs to study non-equilibrium dynamics. In a specific setting, we show the proposed method can recover a mean-field-type equilibrium density. Numerical results are provided to demonstrate the performance of the proposed algorithm for high-dimensional systems.

Chen, Yian↗

DOE OSTI2022

Challenges in Markov Chain Monte Carlo for Bayesian Neural Networks

Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews the main challenges in sampling from the parameter posterior of a neural network via MCMC. Such challenges culminate to lack of convergence to the parameter posterior. Nevertheless, this paper shows that a nonconverged Markov chain, generated via MCMC sampling from the parameter space of a neural network, can yield via Bayesian marginalization a valuable posterior predictive distribution of the output of the neural network. Further, classification examples based on multilayer perceptrons showcase highly accurate posterior predictive distributions. The postulate of limited scope for MCMC developments in BNNs is partially valid; an asymptotically exact parameter posterior seems less plausible, yet an accurate posterior predictive distribution is a tenable research avenue.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

Lin, Xinming↗

DOE OSTI2024

Statistical Association between the Candidate Repeating FRB 20200320A and a Galaxy Group

Abstract We present results from angular cross correlations between select samples of CHIME/FRB repeaters and galaxies in three photometric galaxy surveys, which have shown correlations with the first CHIME/FRB catalog containing repeating and nonrepeating sources: Wide-field Infrared Survey Explorer (WISE) × SCOS, DESI-BGS, and DESI-LRG. We find a statistically significant correlation (p-value <0.001, after accounting for look-elsewhere factors) between a sample of repeaters with an extragalactic dispersion measure (DM) > 395 pc cm −3 and WISE × SCOS galaxies with redshiftz> 0.275. We demonstrate that the correlation arises surprisingly because of a statistical association between FRB 20200320A (extragalactic DM ≈ 550 pc cm −3 ) and a galaxy group in the same dark matter halo at redshiftz≈ 0.32. We estimate that the host halo, along with an intervening halo at redshiftz≈ 0.12, accounts for at least ∼30% of the extragalactic DM. Our results strongly motivate incorporating galaxy group and cluster catalogs into direct host association pipelines for FRBs with ≲ 1 ′ localization precision, effectively utilizing the two-point information to constrain FRB properties such as their redshift and DM distributions. In addition, we find marginal evidence for a negative correlation at 99.4% confidence limit between a sample of repeating FRBs with baseband data (median extragalactic DM = 354 pc cm −3 ) and DESI-LRG galaxies with redshift 0.3 ≤z< 0.45, suggesting that the repeaters might be more prone than apparent nonrepeaters to propagation effects in FRB–galaxy correlations due to intervening free electrons over angular scales ∼0.°5.

Astronomy & Astrophysics↗

NASA NTRS2014

How Dusty Is Alpha Centauri? Excess or Non-excess over the Infrared Photospheres of Main-sequence Stars

Context. Debris discs around main-sequence stars indicate the presence of larger rocky bodies. The components of the nearby, solar-type binary Centauri have metallicities that are higher than solar, which is thought to promote giant planet formation. Aims. We aim to determine the level of emission from debris around the stars in the Cen system. This requires knowledge of their photospheres.Having already detected the temperature minimum, Tmin, of CenA at far-infrared wavelengths, we here attempt to do the same for the moreactive companion Cen B. Using the Cen stars as templates, we study the possible eects that Tmin may have on the detectability of unresolveddust discs around other stars. Methods.We used Herschel-PACS, Herschel-SPIRE, and APEX-LABOCA photometry to determine the stellar spectral energy distributions in thefar infrared and submillimetre. In addition, we used APEX-SHeFI observations for spectral line mapping to study the complex background around Cen seen in the photometric images. Models of stellar atmospheres and of particulate discs, based on particle simulations and in conjunctionwith radiative transfer calculations, were used to estimate the amount of debris around these stars. Results. For solar-type stars more distant than Cen, a fractional dust luminosity fd LdustLstar 2 107 could account for SEDs that do not exhibit the Tmin eect. This is comparable to estimates of fd for the Edgeworth-Kuiper belt of the solar system. In contrast to the far infrared,slight excesses at the 2:5 level are observed at 24 m for both CenA and B, which, if interpreted as due to zodiacal-type dust emission, wouldcorrespond to fd (13) 105, i.e. some 102 times that of the local zodiacal cloud. Assuming simple power-law size distributions of the dustgrains, dynamical disc modelling leads to rough mass estimates of the putative Zodi belts around the Cen stars, viz.4106 M$ of 4 to 1000 msize grains, distributed according to n(a) a3:5. Similarly, for filled-in Tmin emission, corresponding Edgeworth-Kuiper belts could account for103 M$ of dust. Conclusions. Our far-infrared observations lead to estimates of upper limits to the amount of circumstellar dust around the stars CenA and B.Light scattered andor thermally emitted by exo-Zodi discs will have profound implications for future spectroscopic missions designed to searchfor biomarkers in the atmospheres of Earth-like planets. The far-infrared spectral energy distribution of Cen B is marginally consistent with thepresence of a minimum temperature region in the upper atmosphere of the star. We also show that an Cen A-like temperature minimum mayresult in an erroneous apprehension about the presence of dust around other, more distant stars.

metallicities↗

DOE OSTI2023

Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making

One of the great challenges with decision making tasks on real world systems is the fact that data is sparse and acquiring additional data is expensive. In these cases, it is often crucial to make a model of the environment to assist in making decisions. At the same time, limited data means that learned models are erroneous, making it just as important to equip the model with good predictive uncertainties. In the context of learning sequential decision making policies, these uncertainties can prove useful for informing which data to collect for the greatest improvement in policy performance \citep{mehta2021experimental, mehta2022exploration} or informing the policy about unsure regions of state and action space to avoid during test time \citep{yu2020mopo}. Additionally, assuming that realistic samples of the environment can be drawn, an adaptable policy can be trained that attempts to make optimal decisions for any given possible instance of the environment \citep{ghosh2022offline, chen2021offline}. In this work, we examine the so-called ``probabilistic neural network'' (PNN) model that is ubiquitous in model-based reinforcement learning (MBRL) works. We argue that while PNN models may have good marginal uncertainties, they form a distribution of non-smooth transition functions. Not only are these samples unrealistic and may hamper adaptability, but we also assert that this leads to poor uncertainty estimates when predicting multiple step trajectory estimates. To address this issue, we propose a simple sampling method that can be implemented on top of pre-existing models.We evaluate our sampling technique on a number of environments, including a realistic nuclear fusion task, and find that, not only do smooth transition function samples produce more calibrated uncertainties, but they also lead to better downstream performance for an adaptive policy.

Offline Reinforcement Learning↗

DOE OSTI2026

Beyond Point Estimates: Benchmarking Uncertainty Quantification Methods on the AION-1 Astronomical Foundation Model

Foundation models for astronomical surveys offer powerful learned representations that can be transferred to downstream regression tasks such as galaxy property estimation. However, point predictions alone are insufficient for scientific inference; reliable uncertainty quantification (UQ) is essential. We compare seven UQ methods on galaxy property regression using frozen AION-1 foundation-model embeddings, predicting redshift, stellar mass, stellar-population age, gas-phase metallicity, and specific star-formation rate, from Legacy Survey photometry/imaging and DESI spectra, with PROVABGS-derived labels. Distribution-free conformal methods achieve marginal coverage within $\sim$1 pp of the nominal 90% across all properties, while non-conformal baselines (Deep Ensembles, MC~Dropout) fail to calibrate reliably. Among conformal approaches, Conformalized Quantile Regression (CQR) delivers the best coverage in the bin with the poorest model predictions. More importantly, only the Locally Valid and Discriminative (LVD) framework -- particularly when operating on AION-1 embeddings -- also provides finite-sample \emph{local validity}, producing intervals that adapt to each galaxy's local prediction difficulty rather than relying on marginal guarantees alone. These results establish conformal prediction, and LVD in particular, as the preferred UQ framework for uncertainty-aware inference on foundation-model embeddings in astrophysics.

Tame-Narvaez, Karla [Fermilab] (ORCID:000000022249↗

DOE OSTI2022

ARDP Natrium Neutronic Methodology: Argonne Neutronic Assessment of ABR-1000

The Natrium Sodium-cooled Fast Reactor (SFR) concept developed by TerraPower, LLC was selected as one of the advanced reactor designs for demonstration under the Advanced Reactor Demonstration Program Natrium Demo project. In collaboration with TerraPower, the Argonne National Laboratory (ANL) team provide independent reviews of the neutronic methodology being used for the Natrium design. As part of this activity, both the TerraPower and ANL teams agreed to perform independent neutronics analyses of the ABR-1000 reactor problem as a demonstration on the usage of the neutronics methodologies. Comparison of the results from TerraPower and ANL would expose any methodological differences in the modeling approaches. This report describes and presents the Argonne analysis work on the ABR-1000, and will be provided to TerraPower, along with data files, to complete the comparison study. The comparison will cover the methodology to evaluate the reactivity coefficients for a safety analysis, the shutdown margin, and the reconstructed pin power distribution. The methods and models used to calculate the kinetic parameters and the reactivity coefficients (density, Doppler, axial/radial expansion, etc.) are described in detail with recommendations of specific method options and modelling techniques. The control system reactivity worth and the excess reactivity due to temperature defect are calculated for the shutdown margin analysis. The method to evaluate the excess reactivity is discussed in detail. The pin power reconstruction methodology used in SE2ANL, SE2RCT, and DASSH are discussed in this report. The pin power distribution evaluated by different methods are compared and discussed in the report.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

On the Feasibility of Market Manipulation and Energy Storage Arbitrage via Load-Altering Attacks

Around the globe, electric power networks are transforming into complex cyber–physical energy systems (CPES) due to the accelerating integration of both information and communication technologies (ICT) and distributed energy resources. While this integration improves power grid operations, the growing number of Internet-of-Things (IoT) controllers and high-wattage appliances being connected to the electric grid is creating new attack vectors, largely inherited from the IoT ecosystem, that could lead to disruptions and potentially energy market manipulation via coordinated load-altering attacks (LAAs). In this article, we explore the feasibility and effects of a realistic LAA targeted at IoT high-wattage loads connected at the distribution system level, designed to manipulate local energy markets and perform energy storage (ES) arbitrage. Realistic integrated transmission and distribution (T&D) systems are used to demonstrate the effects that LAAs have on locational marginal prices at the transmission level and in distribution systems adjacent to the targeted network.

25 ENERGY STORAGE↗

DOE OSTI2025

Continuing Development of the Nuclear Data Processing Code AMPX [Poster]

The ENDF/B-VIII.1 evaluation library has seen a great growth in the thermal neutron scattering sub-library. The SCALE code system has traditionally approached CE transport by assuming that the CE library on disk represented the fully expanded cumulative probability distributions, conditional on exiting angle and marginal on exiting energy. While this is a complete description of the data, it comes at the potential cost of large amounts of on-disk storage. This approach was strained by several TSL files in ENDF/B-VIII.1, such as graphite, which contained data for a large number of Bragg edges. In the fully expanded probability distributions, this was found to be a disproportionately large fraction of the SCALE CE library.

GNDS↗