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NASA NTRS2017

Statistical Assessment of Cetacean Stranding Events in Cape Cod Area

Cetacean (whales, dolphins and porpoises) mass strandings are a longstanding mystery in the field of marine biology that continue to be recorded in coastal environments around the world. For each of these events, anywhere from a few to several hundred otherwise healthy animals strand in onshore environments, often for no apparent reason. While the causes of these events remain unclear, anthropogenic and naturogenic mechanisms have been suggested. We present results of an inter-disciplinary study that draws expertise from space weather, marine mammal biology and ecology, and marine mammal stranding response. This study assessed 16 years of cetacean stranding events in the Cape Cod (Massachusetts, USA) area concurrently with a large dataset of meteorological, geophysical, biological, oceanographic and space weather data to produce inferences about possible causes for these unexplained events.

Dolphi↗

NASA NTRS2017

Coronal Flux Ropes and their Interplanetary Counterparts

We report on a study comparing coronal flux ropes inferred from eruption data with their interplanetary counterparts constructed from in situ data. The eruption data include the source region magnetic field, post-eruption arcades, and coronal mass ejections (CMEs). Flux ropes were fit to the interplanetary CMEs (ICMEs) considered for the 2011 and 2012 Coordinated Data Analysis Workshops (CDAWs). We computed the total reconnected flux involved in each of the associated solar eruptions and found it to be closely related to flare properties, CME kinematics, and ICME properties. By fitting flux ropes to the white-light coronagraph data, we obtained the geometric properties of the flux ropes and added magnetic properties derived from the reconnected flux. We found that the CME magnetic field in the corona is significantly higher than the ambient magnetic field at a given heliocentric distance. The radial dependence of the flux rope magnetic field strength is faster than that of the ambient magnetic field. The magnetic field strength of the coronal flux rope is also correlated with that in interplanetary flux ropes constructed from in situ data, and with the observed peak magnetic field strength in ICMEs. The physical reason for the observed correlation between the peak field strength in MCs is the higher magnetic field content in faster coronal flux ropes and ultimately the higher reconnected flux in the eruption region. The magnetic flux ropes constructed from the eruption data and coronagraph observations provide a realistic input that can be used by various models to predict the magnetic properties of ICMEs at Earth and other destination in the heliosphere.

Gopalswamy, N.↗

NASA NTRS2020

The Evolution of Icelandic Volcano Emissions, as Observed From Space in the Era of NASA's Earth Observing System (EOS)

Volcanoes are natural phenomena that have global environmental impacts. Satellite remote sensing can help classify volcanic eruptions and track the dispersion of emissions. We assess multiple volcanic eruptions in Iceland (Eyjafjallajökull 2010, Grímsvötn 2011 and Holuhraun 2014-2015), using space-borne observations to infer information about the geological dynamics of each volcano and the properties and evolution of plume particles. We derive qualitative constraints on plume particle size, shape and light-absorption characteristics from Multi-angle Imaging SpectroRadiometer (MISR) space-borne imagery. With the MISR Research Algorithm (RA), we distinguish sulfate/water-dominated volcanic plumes from Holuhraun and ash-dominated plumes from Eyjafjallajökull and Grímsvötn, and even identify subtler changes in ash particle size and light-absorption within plumes. Additionally, plume heights are retrieved geometrically from MISR. These are combined with surface thermal anomalies from the MODerate resolution Imaging Spectroradiometer (MODIS) and SO2 concentrations derived from the Ozone Monitoring Instrument (OMI) to synthesize eruption remote-sensing chronologies. Signals related to differences in particle properties are identified and linked to evolving magma composition at Eyjafjallajökull. The results illustrate the potential to distinguish qualitative differences in eruptive magma composition based on particle light absorption and plume profile from remote-sensing. For the sulfate-rich Holuhraun plumes, the influence of aerosol hygroscopic growth during transport is inferred from such data. Three processes appear to dominate plume evolution in Iceland: downwind aerosol formation; particle hydration; and particle deposition. This work demonstrates enhanced MISR capabilities, and more generally, remote-sensing analysis that can be applied globally, especially where suborbital volcano observations are limited or entirely absent.

remote sensting↗

NASA NTRS2019

GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observedby LIGO and Virgo During the First and Second Observing Runs

We present the results from three gravitational-wave searches for coalescing compact binaries with component masses above1M during the first and second observing runs of the advanced gravitational-wave detector network. During the first observing run (O1), from September 12, 2015 to January 19, 2016, gravitational waves from three binary black hole mergers were detected. The second observing run (O2), which ran from November 30, 2016 to August 25, 2017, saw the first detection of gravitational waves from a binary neutron star in spiral, in addition to the observation of gravitational waves from a total of seven binary black hole mergers, four of which we report here for the first time: GW170729, GW170809, GW170818, and GW170823. For all significant gravitational-wave events, we provide estimates of the source properties. The detected binary black holes have total masses between18.6þ3.2−0.7M and 84.4þ15.8−11.1M and range in distance between 320þ120−110 and 2840þ1400−1360Mpc. No neutron star–black hole mergers were detected. In addition to highly significant gravitational-wave events, we also provide a list of marginal event candidates with an estimated false-alarm rate less than 1 per 30 days. From these results over the first two observing runs, which include approximately one gravitational-wave detection per 15 days of data searched, we infer merger rates at the 90% confidence intervals of 110−3840Gpc−3y−1 for binary neutron stars and 9.7−101Gpc−3y−1 for binary black holes assuming fixed population distributions and determine a neutron star–black hole merger rate 90% upper limit of 610Gpc−3y−1.

Astrophysics↗

NASA NTRS2021

Contribution of the World's Main Dust Source Regions to the Global Cycle of Desert Dust

Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, the relative contributions of the world's major source regions to the global dust cycle remain poorly constrained. This problem hinders accounting for the potentially large impact of regional differences in dust properties on clouds, the Earth's energy balance, and terrestrial and marine biogeochemical cycles. Here, we constrain the contribution of each of the world's main dust source regions to the global dust cycle. We use an analytical framework that integrates an ensemble of global aerosol model simulations with observationally informed constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth (DAOD). We obtain a dataset that constrains the relative contribution of nine major source regions to size-resolved dust emission, atmospheric loading, DAOD, concentration, and deposition flux. We find that the 22–29 Tg (1 standard error range) global loading of dust with a geometric diameter up to 20 µm is partitioned as follows: North African source regions contribute ∼ 50 % (11–15 Tg), Asian source regions contribute ∼ 40 % (8–13 Tg), and North American and Southern Hemisphere regions contribute ∼ 10 % (1.8–3.2 Tg). These results suggest that current models on average overestimate the contribution of North African sources to atmospheric dust loading at ∼ 65 %, while underestimating the contribution of Asian dust at ∼ 30 %. Our results further show that each source region's dust loading peaks in local spring and summer, which is partially driven by increased dust lifetime in those seasons. We also quantify the dust deposition flux to the Amazon rainforest to be ∼ 10 Tg yr−1, which is a factor of 2–3 less than inferred from satellite data by previous work that likely overestimated dust deposition by underestimating the dust mass extinction efficiency. The data obtained in this paper can be used to obtain improved constraints on dust impacts on clouds, climate, biogeochemical cycles, and other parts of the Earth system.

Dust↗

NASA NTRS2021

Upper Limits on the Isotropic Gravitational-Wave Background from Advanced LIGO and Advanced Virgo's Third Observing Run

We report results of a search for an isotropic gravitational-wave background (GWB) using data from Advanced LIGO's and Advanced Virgo's third observing run (O3) combined with upper limits from the earlier O1 and O2 runs. Unlike in previous observing runs in the advanced detector era, we include Virgo in the search for the GWB. The results of the search are consistent with uncorrelated noise, and therefore we place upper limits on the strength of the GWB. We find that the dimensionless energy density Ω(sub GW) ≤ 5.8 × 10(exp -9) at the 95% credible level for a at (frequency-independent) GWB, using a prior which is uniform in the log of the strength of the GWB, with 99% of the sensitivity coming from the band 20-76.6 Hz; Ω(sub GW)(f) ≤ 3.4 × 10(exp -9) at 25 Hz for a power-law GWB with a spectral index of 2/3 (consistent with expectations for compact binary coalescences), in the band 20-90.6 Hz; and Ω(sub GW)(f) ≤ 3.9 × 10(exp -10) at 25 Hz for a spectral index of 3, in the band 20-291.6 Hz. These upper limits improve over our previous results by a factor of 6.0 for a at GWB, 8.8 for a spectral index of 2/3, and 13.1 for a spectral index of 3. We also search for a GWB arising from scalar and vector modes, which are predicted by alternative theories of gravity; we do not find evidence of these, and place upper limits on the strength of GWBs with these polarizations. We demonstrate that there is no evidence of correlated noise of magnetic origin by performing a Bayesian analysis that allows for the presence of both a GWB and an effective magnetic background arising from geophysical Schumann resonances. We compare our upper limits to a fiducial model for the GWB from the merger of compact binaries, updating the model to use the most recent data-driven population inference from the systems detected during O3a. Finally, we combine our results with observations of individual mergers and show that, at design sensitivity, this joint approach may yield stronger constraints on the merger rate of binary black holes at z ≳ 2 than can be achieved with individually resolved mergers alone.

Ryan Abbott↗

DOE OSTI2024

Assessing Membership Inference Attacks under Distribution Shifts

Membership inference attacks (MIAs) exploit machine learning models to infer whether a data point was in the training set, posing significant privacy risks even with limited black-box access. These attacks rely on the attacker approximating the target model’s training distribution, yet the impact of distribution shifts between target and shadow models on MIA success remains underexplored. We systematically evaluate five types of distribution shifts —-cutout, jitter, Gaussian noise, label shift, and attribute shift —- at varying intensities. Our results reveal that these shifts affect MIA effectiveness in nuanced ways, with some reducing attack success while others exacerbate vulnerabilities, and the same shift can have opposite effects depending on the type of MIA. This highlights the complex interplay between distributional differences and attack performance, offering critical insights for improving model defenses against MIAs.

Shi, Yichuan [Massachusetts Institute of Technolog↗

NASA NTRS1984

Interstellar abundances and depletions inferred from observations of neutral atoms

Data on neutral atomic species are analyzed for the purpose of inferring relative elemental abundances and depletions in diffuse cloud cores, where it is assumed that densities are enhanced in comparison with mean densities over integrated lines of sight. Column densities of neutral atoms are compared to yield relative column densities of singly ionized species, which are assumed dominant in cloud cores. This paper incorporates a survey of literature data on neutral atomic abundances with the result that no systematic enhancement in the depletions of calcium or iron in cloud cores is found, except for zeta Ophiuchi. This may imply that depletions are not influenced by density, but other data argue against this interpretation. It is concluded either that in general all elements are depleted together in dense regions so that their relative abundances remain constant, or that typical diffuse clouds do not have significant cores, but instead are reasonably homogeneous. The data show a probable correlation between cloud-core depletion and hydrogen-molecular fraction, supporting the assumption that overall depletions are a function of density.

Snow, T. P.↗

NASA NTRS1983

The inference of tropical cyclone dynamics using GOES VISSR/VAS data

The sequence of events observed during tropical cyclone Emily, was suggested as a possible mechanism for cyclogenesis. Geostationary Operational Environmental Satellite (GOES) East VISSR/VAS sensors were used. The VISSR visible imagery obtained every 15 minutes was used to define the low tropospheric cyclonic vortex and upper tropospheric horizontal convergence. The VAS water vapor (channels 9 and 10) and carbon dioxide (channels 3 and 4) channels were used to infer upper and middle tropospheric subsidence by monitoring the Adiabatic compressional drying and warming, respectively, occurring within this layer. Evidence of an existing lower tropospheric cyclonic vortex was seen. The satellite derived wind vectors (length of vector is proportional to wind velocity, where the strongest winds were approximately 35 knots) are superimposed on the GOES visible image of tropical storm Emily. Vectors and low level clouds depict the center of the cyclonic vortex immediately south of the large convective cell in the center of the image. Upper tropospheric cloud tracers and rawinsonde reports along the Eastern United States suggest that the southwesterly environmental upper atmospheric flow is converging with the outflow from the convective cell north of the vortex.

Rodgers, E. B.↗

NASA NTRS1992

An expert system shell for inferring vegetation characteristics: Implementation of additional techniques (task E)

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG subgoal PROPORTION.GROUND.COVER has been completed and a number of additional techniques that infer the proportion ground cover of a sample have been implemented. Some techniques operate on sample data at a single wavelength. The techniques previously incorporated in VEG for other subgoals operated on data at a single wavelength so implementing the additional single wavelength techniques required no changes to the structure of VEG. Two techniques which use data at multiple wavelengths to infer proportion ground cover were also implemented. This work involved modifying the structure of VEG so that multiple wavelength techniques could be incorporated. All the new techniques were tested using both the VEG 'Research Mode' and the 'Automatic Mode.'

Harrison, P. Ann↗

DOE OSTI2024

PINN surrogate of Li-ion battery models for parameter inference, Part II: Regularization and application of the pseudo-2D model

Bayesian parameter inference is useful to improve Li-ion battery diagnostics and can help formulate battery aging models. However, it is computationally intensive and cannot be easily repeated for multiple cycles, multiple operating conditions, or multiple replicate cells. To reduce the computational cost of Bayesian calibration, numerical solvers for physics-based models can be replaced with faster surrogates. A physics-informed neural network (PINN) is developed as a surrogate for the pseudo-2D (P2D) battery model calibration. For the P2D surrogate, additional training regularization was needed as compared to the PINN single-particle model (SPM) developed in Part I. Both the PINN SPM and P2D surrogate models are exercised for parameter inference and compared to data obtained from a direct numerical solution of the governing equations. A parameter inference study highlights the ability to use these PINNs to calibrate scaling parameters for the cathode Li diffusion and the anode exchange current density. By realizing computational speed-ups of ~2250x for the P2D model, as compared to using standard integrating methods, the PINN surrogates enable rapid state-of-health diagnostics. Finally, in the low-data availability scenario, the testing error was estimated to ~2 mV for the SPM surrogate and ~10 mV for the P2D surrogate which could be mitigated with additional data.

25 ENERGY STORAGE↗

NASA NTRS2016

Thermal properties of Rhea's Poles: Evidence for a Meter-Deep Unconsolidated Subsurface Layer

Cassini's Composite Infrared Spectrometer (CIRS) observed both of Rhea's polar regions during a close (2000 km) flyby on 9th March 2013 during orbit 183. Rhea's southern pole was again observed during a more distant (51,000 km) flyby on 10th February 2015 during orbit 212. The results show Rhea's southern winter pole is one of the coldest places directly observed in our Solar System: surface temperatures of 25.4 +/-7.4 K and 24.7 +/-6.8 K are inferred from orbit 183 and 212 data, respectively. The surface temperature of the northern summer pole inferred from orbit 183 data is warmer: 66.6 +/-0.6 K. Assuming the surface thermophysical properties of the two polar regions are comparable then these temperatures can be considered a summer and winter seasonal temperature constraint for the polar region. Orbit 183 will provide solar longitude ( LS ) coverage at 133 deg and 313 deg for the summer and winter poles respectively, while orbit 212 provides an additional winter temperature constraint at LS 337 deg. Seasonal models with bolometric albedo values between 0.70 and 0.74 and thermal inertia values between 1 and 46 J m( exp −2) K( exp −1) s (exp −1/2) (otherwise known as MKS units) can provide adequate fits to these temperature constraints (assuming the winter temperature is an upper limit). Both these albedo and thermal inertia values agree within the uncertainties with those previously observed on both Rhea's leading and trailing hemispheres. Investigating the seasonal temperature change of Rhea's surface is particularly important, as the seasonal wave is sensitive to deeper surface temperatures ( approximately tens of centimeters to meter depths) than the more commonly reported diurnal wave (typically less than a centimeter), the exact depth difference dependent upon the assumed surface properties. For example, if a surface porosity of 0.5 and thermal inertia of 25 MKS is assumed then the depth of the seasonal thermal wave is 76 cm, which is much deeper than the approximately 0.5 cm probed by diurnal studies of Rhea ( Howett et al., 2010 ). The low thermal inertia derived here implies that Rhea's polar surfaces are highly porous even at great depths. Analysis of a CIRS focal plane 1 (10-600 cm(exp −1 ) stare observation, taken during the orbit 183 encounter between 16:22:33 and 16:23:26 UT centered on 71.7 deg W, 58.7 deg S provides the first analysis of a thermal emissivity spectrum on Rhea. The results show a flat emissivity spectrum with negligible emissivity features. A few possible explanations exist for this flat emissivity spectrum, but the most likely for Rhea is that the surface is both highly porous and composed of small particles ( less than approximately 50 micrometers).

Howett, C. J. A.↗

NASA NTRS2000

Improved Quality in Aerospace Testing Through the Modern Design of Experiments

This paper illustrates how, in the presence of systematic error, the quality of an experimental result can be influenced by the order in which the independent variables are set. It is suggested that in typical experimental circumstances in which systematic errors are significant, the common practice of organizing the set point order of independent variables to maximize data acquisition rate results in a test matrix that fails to produce the highest quality research result. With some care to match the volume of data required to satisfy inference error risk tolerances, it is possible to accept a lower rate of data acquisition and still produce results of higher technical quality (lower experimental error) with less cost and in less time than conventional test procedures, simply by optimizing the sequence in which independent variable levels are set.

DeLoach, R.↗

DOE OSTI2024

Kilonova light-curve interpolation with neural networks

Kilonovae are the electromagnetic transients created by the radioactive decay of freshly synthesized elements in the environment surrounding a neutron star merger. To study the fundamental physics in these complex environments, kilonova modeling requires, in part, the use of radiative transfer simulations. The microphysics involved in these simulations results in high computational cost, prompting the use of emulators for parameter inference applications. Utilizing a training set of 22 248 high-fidelity simulations (composed of 412 unique ejecta parameter combinations evaluated at 54 viewing angles), we use a neural network to efficiently train on existing radiative transfer simulations and predict light curves for new parameters in a fast and computationally efficient manner. Our neural network can generate millions of new light curves in under a minute. We discuss our emulator's degree of off-sample reliability and parameter inference of the AT2017gfo observational data. Finally, we discuss tension introduced by multiband inference in the parameter inference results, particularly with regard to the neural network's recovery of viewing angle. Published by the American Physical Society 2024

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2002

Evolution of Ionospheric Convection during a Double Transpolar Arc Phenomenon on February 11, 1999

An evolution of ionospheric convection was studied for a double transpolar arc phenomenon on February 11, 1999. While one transpolar arc split from the auroral oval in the morning sector and drifted duskward, another arc appeared in the evening sector. The convection was investigated with three velocity data sets: E B drift velocities from the ASTRID-2 satellite; Ion Driftmeter data from the DMSP satellites; and Doppler-shift data from the Super-DARN radars. We inferred convection cells from these data sets and found that the number of convection cells changed from three to four as the dominance of IMF changed from a negative By to a positive Bz. Our result suggests that the ionospheric convection that has been so far discussed for various conditions of IMF may be applied even to the cases accompanied by transpolar arcs.

Narita, Y.↗

DOE OSTI2026

Extracting the Breakout Distance from the ECOT Trajectories: Gaussian Process Regression Approach

Enhanced Corner Turning (ECOT) experiments provide an important metric of performance of high explosive (HE) formulations. The breakout distance is a single scalar value that characterizes the corner turning efficiency of an HE. Extracting the breakout distance from the raw ECOT results, whether experimental or simulated, is a conceptually straightforward procedure which, however, is non-unique, especially in the presence of noise. More specifically, this procedure involves numerical smoothing and selecting particular values for parameters of this smoothing introduces human bias. In this work, we propose to use the Gaussian process regression to analyze ECOT results. This analysis involves the effective smoothing of the data, thus allowing for accurate extraction of the breakout distance. Most importantly, the parameters of this smoothing can be inferred from the ECOT data itself, rendering the approach effectively parameter-free and thus diminishing the human bias. An additional benefit of the Gaussian process regression, being a statistical inference method, is that not just the value of the breakout distance, but also its confidence interval can be extracted from the data. This report introduces the Gaussian process regression, as applied to ECOT, and demonstrates its usefulness by extracting the breakout distances for a selection of experimental and simulated data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2025

AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold networks

We propose the artificial intelligence velocimetry-thermometry (AIVT) method to reconstruct a continuous and differentiable representation of the temperature and velocity in turbulent convection from measured three-dimensional (3D) velocity data. AIVT is based on physics-informed Kolmogorov-Arnold networks and trained by optimizing a loss function that minimizes residuals of the velocity data, boundary conditions, and governing equations. We apply AIVT to a set of simultaneously measured 3D temperature and velocity data of Rayleigh-Bénard convection, obtained by combining particle image thermometry and Lagrangian particle tracking. This enables us to directly compare machine learning results to true volumetric, simultaneous temperature and velocity measurements. We demonstrate that AIVT can reconstruct and infer continuous, instantaneous velocity and temperature fields and their gradients from sparse experimental data at a high resolution, providing an additional approach for understanding thermal turbulence.

Science & Technology - Other Topics↗

DOE OSTI2025

A Markov chain Monte Carlo (MCMC) Bayesian inference approach to analyze apparent activation barriers and reaction orders from microreactor data

Statistical analysis of steady-state catalytic kinetic data is often limited by data sparsity due to the slow pace at which the data is collected. Data sparsity and limitations in statistical analysis make it difficult to differentiate between mechanistic models and catalytic sites. A Bayesian inference tool is reported for catalysis researchers to estimate error in the determination of reaction orders from steady state microreactor data. The benefits of a Bayesian inference approach are discussed, as an alternative to the more common frequentist approach. The approach incorporates prior knowledge of the system and the data collected to form an error estimate on reaction orders. We investigated the effects of three distinct data treatments—individual fitting of trials, pooled analysis, and constrained regression methods—on the precision and uncertainty of reaction order determinations. To assess the robustness of our findings, we conducted sensitivity analyses to evaluate the influence of Bayesian parameters on uncertainty estimation. Additionally, we utilized synthetic data to illustrate how data quality impacts the precision of uncertainty assessments. We show Bayesian analysis can obtain a more precise estimation of error with a sparse data set than a frequentist analysis. Finally, this work provides strong evidence that the adoption of Bayesian analysis of kinetic data may help researchers make more precise arguments as to the strength of their evidence for a particular mechanistic hypothesis, or in comparing across different catalysts.

42 ENGINEERING↗