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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Inference of the Mass Composition of Cosmic Rays with Energies from 10 18.5 to 10 20 eV Using the Pierre Auger Observatory and Deep Learning

We present measurements of the atmospheric depth of the shower maximum X max , inferred for the first time on an event-by-event level using the surface detector of the Pierre Auger Observatory. Using deep learning, we were able to extend measurements of the X max distributions up to energies of 100 EeV ( 10 20 eV ), not yet revealed by current measurements, providing new insights into the mass composition of cosmic rays at extreme energies. Gaining a 10-fold increase in statistics compared to the fluorescence detector data, we find evidence that the rate of change of the average X max with the logarithm of energy features three breaks at 6.5 ± 0.6 ( stat ) ± 1 ( syst ) EeV , 11 ± 2 ( stat ) ± 1 ( syst ) EeV , and 31 ± 5 ( stat ) ± 3 ( syst ) EeV , in the vicinity to the three prominent features (ankle, instep, suppression) of the cosmic-ray flux. The energy evolution of the mean and standard deviation of the measured X max distributions indicates that the mass composition becomes increasingly heavier and purer, thus being incompatible with a large fraction of light nuclei between 50 and 100 EeV. Published by the American Physical Society 2025

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Search for low mass dark matter in DarkSide-50: the bayesian network approach

Abstract We present a novel approach for the search of dark matter in the DarkSide-50 experiment, relying on Bayesian Networks. This method incorporates the detector response model into the likelihood function, explicitly maintaining the connection with the quantity of interest. No assumptions about the linearity of the problem or the shape of the probability distribution functions are required, and there is no need to morph signal and background spectra as a function of nuisance parameters. By expressing the problem in terms of Bayesian Networks, we have developed an inference algorithm based on a Markov Chain Monte Carlo to calculate the posterior probability. A clever description of the detector response model in terms of parametric matrices allows us to study the impact of systematic variations of any parameter on the final results. Our approach not only provides the desired information on the parameter of interest, but also potential constraints on the response model. Our results are consistent with recent published analyses and further refine the parameters of the detector response model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1976

X-ray observation of 3U 1700-37

X-ray observations with Copernicus reveal three categories of flux variability in 3U 1700-37. High amplitude hourly variations are energy independent in the 3-11 keV range while a change in the low energy absorbing column causes variations in flux level on an orbital time scale. This absorption is most severe prior to eclipse ingress, suggesting that the distribution of absorbing material around the X-ray source is asymmetrical with respect to the line of centers of the binary system. The absorbing material may be identical with a high density region inferred from optical observations of HD 153919. In the third category, the maximum source intensity per binary cycle is variable by at least a factor of two between observations. Measurement of the eclipse duration on three occasions indicate that it is significantly less than when observed by Uhuru.

Mason, K. O.↗
NASA NTRS1987

Lithospheric loading and tectonics of the lunar irregular maria

Many of the tectonic features associated with the circular maria are the result of stresses generated by loading of the lunar lithosphere by mare basalt units. The hypothesis is tested that the irregular lunar maria share with the circular maria the processes leading to the formation of associated graben and mare ridge systems. A formulation of the lithospheric flexure problem that accounts for the variable distribution of basalt loads for the irregular maria is applied. On the basis of the tectonic structures and geologic history of each of the major irregular maria, as well as models for the distribution of mare basalt for each region, the predicted stress fields are compared with the distribution of tectonic features for a range of assumed values of the thickness of the elastic lithosphere. The best fitting values for lithospheric thickness beneath the regular maria are then compared with those previously inferred for the circular maria.

Hall, J. Lynn↗
NASA NTRS2017

Electron Scattering by High-Frequency Whistler Waves at Earth's Bow Shock

Electrons are accelerated to non-thermal energies at shocks in space and astrophysical environments. While different mechanisms of electron acceleration have been proposed, it remains unclear how non-thermal electrons are produced out of the thermal plasma pool. Here, we report in situ evidence of pitch-angle scattering of non-thermal electrons by whistler waves at Earths bow shock. On 2015 November 4, the Magnetospheric Multiscale (MMS) mission crossed the bow shock with an Alfvn Mach number is approximately 11 and a shock angle of approximately 84deg. In the ramp and overshoot regions, MMS revealed bursty enhancements of non-thermal (0.52 keV) electron flux, correlated with high-frequency (0.2 - 0.4 Omega(sub ce), where Omega(sub ce) is the cyclotron frequency) parallel-propagating whistler waves. The electron velocity distribution (measured at 30 ms cadence) showed an enhanced gradient of phase-space density at and around the region where the electron velocity component parallel to the magnetic field matched the resonant energy inferred from the wave frequency range. The flux of 0.5 keV electrons (measured at 1ms cadence) showed fluctuations with the same frequency. These features indicate that non-thermal electrons were pitch-angle scattered by cyclotron resonance with the high-frequency whistler waves. However, the precise role of the pitch-angle scattering by the higher-frequency whistler waves and possible nonlinear effects in the electron acceleration process remains unclear.

Oka, M.↗
NASA NTRS2021

The First Widespread Solar Energetic Particle Event Observed By Solar Orbiter on 2020 November 29

Context. On 2020 November 29, the first widespread solar energetic particle (SEP) event of solar cycle 25 was observed at four widely separated locations in the inner (. 1AU) heliosphere. Relativistic electrons as well as protons with energies > 50MeV were observed by Solar Orbiter (SolO), Parker Solar Probe (PSP), the Solar Terrestrial Relations Observatory (STEREO)-A and multiple near-Earth spacecraft. The SEP event was associated with an M4.4 class X-ray flare and accompanied by a coronal mass ejection (CME) and an extreme ultraviolet (EUV) wave as well as a type II radio burst and multiple type III radio bursts. Aims. We present multi-spacecraft particle observations and place them in context with source observations from remote sensing instruments and discuss how such observations may further our understanding of particle acceleration and transport in this widespread event. Methods. Velocity dispersion analysis (VDA) and time shift analysis (TSA) were used to infer the particle release times at the Sun. Solar wind plasma and magnetic field measurements were examined to identify structures that influence the properties of the energetic particles such as their intensity. Pitch angle distributions and first-order anisotropies were analyzed in order to characterize the particle propagation in the interplanetary medium. Results. We find that during the 2020 November 29 SEP event, particles spread over more than 230° in longitude close to 1AU. The particle onset delays observed at the different spacecraft are larger as the flare–footpoint angle increases and are consistent with those from previous STEREO observations. Comparing the timing when the EUV wave intersects the estimated magnetic footpoints of each spacecraft with particle release times from TSA and VDA, we conclude that a simple scenario where the particle release is only determined by the EUV wave propagation is unlikely for this event. Observations of anisotropic particle distributions at SolO, Wind, and STEREO-A do not rule out that particles are injected over a wide longitudinal range close to the Sun. However, the low values of the first-order anisotropy observed by near-Earth spacecraft suggest that diffusive propagation processes are likely involved.

A Kollhoff↗
DOE OSTI2020

Data Collection and Analysis Challenges and Mitigation Strategies for Quantitative Human Factors Research Studies in Nuclear Power Plant Modernization

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program Plant Modernization Pathway is conducting targeted research and development (R&D) to address aging and reliability concerns with the legacy instrumentation and control and related information systems of the U.S. LWR fleet. In this effort, the application of human factors engineering (HFE) provides an important role in ensuring new digital plant technologies enable broad innovation and business improvement with continued operational safety. Evaluation is a key activity in HFE, which often occurs iteratively through the system design lifecycle. While qualitative methods are important in collecting information of how users perform tasks through observations, quantitative methods are equally important in assessing system design based on performance. In collecting and analyzing this quantitative data, there are notable challenges in the nuclear HFE domain that may threaten the validity and reliability of the inferences made in these studies. Notable challenges include small sample size and limited resources, large error variance and small effect size, an “adding test features to losing degrees of freedom” dilemma, non-normal distribution, and heterogeneity of variance. The results in control room usability studies are often statistically non-significant, which makes it hard to interpret. This work discusses these challenges across different scientific viewpoints and provides real-world examples of these challenges in practice. Collectively, the objective of this work is to position these challenges to the larger data science community as a means of identifying future opportunities to address these issues.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

A Novel Modeling Framework for Computationally Efficient and Accurate Real-Time Ensemble Flood Forecasting With Uncertainty Quantification

A novel modeling framework that simultaneously improves accuracy, predictability, and computational efficiency is presented. It embraces the benefits of three modeling techniques integrated together for the first time: surrogate modeling, parameter inference, and data assimilation. The use of polynomial chaos expansion (PCE) surrogates significantly decreases computational time. Parameter inference allows for model faster convergence, reduced uncertainty, and superior accuracy of simulated results. Ensemble Kalman filters assimilate errors that occur during forecasting. To examine the applicability and effectiveness of the integrated framework, we developed 18 approaches according to how surrogate models are constructed, what type of parameter distributions are used as model inputs, and whether model parameters are updated during the data assimilation procedure. We conclude that (1) PCE must be built over various forcing and flow conditions, and in contrast to previous studies, it does not need to be rebuilt at each time step; (2) model parameter specification that relies on constrained, posterior information of parameters (so-called Selected specification) can significantly improve forecasting performance and reduce uncertainty bounds compared to Random specification using prior information of parameters; and (3) no substantial differences in results exist between single and dual ensemble Kalman filters, but the latter better simulates flood peaks. The use of PCE effectively compensates for the computational load added by the parameter inference and data assimilation (up to ~80 times faster). Therefore, the presented approach contributes to a shift in modeling paradigm arguing that complex, high-fidelity hydrologic and hydraulic models should be increasingly adopted for real-time and ensemble flood forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Deep compressed seismic learning for fast location and moment tensor inferences with natural and induced seismicity

Fast detection and characterization of seismic sources is crucial for decision-making and warning systems that monitor natural and induced seismicity. However, besides the laying out of ever denser monitoring networks of seismic instruments, the incorporation of new sensor technologies such as Distributed Acoustic Sensing (DAS) further challenges our processing capabilities to deliver short turnaround answers from seismic monitoring. In response, this work describes a methodology for the learning of the seismological parameters: location and moment tensor from compressed seismic records. In this method, data dimensionality is reduced by applying a general encoding protocol derived from the principles of compressive sensing. The data in compressed form is then fed directly to a convolutional neural network that outputs fast predictions of the seismic source parameters. Thus, the proposed methodology can not only expedite data transmission from the field to the processing center, but also remove the decompression overhead that would be required for the application of traditional processing methods. An autoencoder is also explored as an equivalent alternative to perform the same job. We observe that the CS-based compression requires only a fraction of the computing power, time, data and expertise required to design and train an autoencoder to perform the same task. Implementation of the CS-method with a continuous flow of data together with generalization of the principles to other applications such as classification are also discussed.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1995

VLF waves in the foreshock

Plasma waves observed in the VLF range upstream of planetary bow shocks not only modify the particle distributions, but also provide important information about the acceleration processes that occur at the bow shock. Electron plasma oscillations observed near the tangent field line in the electron foreshock are generated by electrons reflected at the bow shock through a process that has been referred to as Fast Fermi acceleration. Fast Fermi acceleration is the same as shock-drift acceleration, which is one of the mechanisms by which ions are energized at the shock. We have generated maps of the VLF emissions upstream of the Venus bow shock, using these maps to infer properties of the shock energization processes. We find that the plasma oscillations extend along the field line up to a distance that appears to be controlled by the shock scale size, implying that shock curvature restricsts the flux and energy of reflected electrons. We also find that the ion acoustic waves are observed in the ion foreshock, but at Venus these emissions are not detected near the ULF forshock boundary. Through analogy with terrestrial ion observations, this implies that the ion acoustic waves are not generated by ion beams, but are instead generated by diffuse ion distributions found deep within the ion foreshock. However, since the shock is much smaller at Venus, and there is no magnetosphere, we might expect ion distributions within the ion foreshock to be different than at the Earth. Mapping studies of the terrestrial foreshock similar to those carried out at Venus appear to be necessary to determine if the inferences drawn from Venus data are applicable to other foreshocks.

Strangeway, R. J.↗
DOE OSTI2026

Hierarchical Gaussian Random Field Sampling for Multilevel Markov Chain Monte Carlo: Coupling Stochastic Partial Differential Equation and the Karhunen–Loève Decomposition

This work introduces structure preserving hierarchical decompositions for sampling Gaussian random fields (GRFs) within the context of multilevel Bayesian inference in high-dimensional space. Existing scalable hierarchical sampling methods, such as those based on stochastic partial differential equations (SPDEs), often reduce the dimensionality of the sample space at the cost of accuracy of inference. Other approaches, such that those based on Karhunen-Loève (KL) expansions, offer sample space dimensionality reduction but sacrifice GRF representation accuracy and ergodicity of the Markov chain Monte Carlo (MCMC) sampler and are computationally expensive for high-dimensional problems. The proposed method integrates the dimensionality reduction capabilities of KL expansions with the scalability of SPDE-based sampling, thereby providing a robust, unified framework for high-dimensional uncertainty quantification (UQ) that is scalable and accurate, preserves ergodicity, and offers dimensionality reduction of the sample space. The hierarchy in our multilevel algorithm is derived from the geometric multigrid hierarchy. By constructing a hierarchical decomposition that maintains the covariance structure across the levels in the hierarchy, the approach enables efficient coarse-to-fine sampling while ensuring that all samples are drawn from the desired distribution. The effectiveness of the proposed method is demonstrated on a benchmark subsurface flow problem, demonstrating its effectiveness in improving computational efficiency and statistical accuracy. Furthermore, our proposed technique is more efficient and accurate and displays better convergence properties than existing methods for high-dimensional Bayesian inference problems.

Gaussian random fields↗
DOE OSTI2025

Bayesian Inferring Nucleon Gravitational Form Factors via Near-Threshold 𝐽/𝜓 Photoproduction

With Bayesian inference, we investigate the impact of recent near-threshold 𝐽/𝜓 production measurements by the 𝐽/𝜓⁢007 experiment and GlueX Collaboration on the extraction of proton gravitational form factors. We apply the generalized parton distribution framework at the next to leading order and demonstrate a stable expansion for the near-threshold kinematics. We find that the experimental constraints are in good agreement with the state-of-the-art lattice simulations, where negative 𝐶 𝑞 ⁡(𝑡) and 𝐶 𝑔 ⁡(𝑡) are strongly preferred. This highlights a great potential to extract them from future high-precision experiments.

QCD phenomenology↗
DOE OSTI2026

Photon temporal-mode readout for inference of neutron star merger remnant gravitational waves

Gravitational waves emitted after neutron star binary coalescences and the information they carry about dense matter are a high-priority target for next-generation detectors. Even though such detectors are expected to observe millions of signals, detectable postmerger emission will remain rare. Here, in this work, we explore postmerger detectability and inference through an alternative detector readout scheme for data dominated by quantum-noise, which is the case above 1 kHz; photon-counting. In such a readout, signals and noise become quantized into discrete distributions corresponding to the detection of single photons measured in a chosen basis of modes. Through simulated data, we demonstrate that photon counting can be efficient even for weak signals. We find ∼1 in 100 signals with a postmerger signal-to-noise ratio of 0.2 can result in a single photon and thus be detected. Furthermore, after 2 ×10 4 signals—equivalent to 10 −2 to 1.5 years of observation—photon counting results in a twofold improvement in the measurement of the radius of a 1.6⁢𝑀 ⊙ neutron star. Constraints can be further tightened if the detector classical noise is reduced. Photon counting offers a promising alternative to traditional homodyne readout techniques for extracting information from low signal-to-noise ratio postmerger signals.

gravitational wave detection↗
DOE OSTI2023

Uncovering dark matter density profiles in dwarf galaxies with graph neural networks

Dwarf galaxies are small, dark-matter-dominated galaxies, some of which are embedded within the Milky Way. Their lack of baryonic matter (e.g., stars and gas) makes them perfect test beds for probing the properties of dark matter—understanding the spatial dark matter distribution in these systems can be used to constrain microphysical dark matter interactions that influence the formation and evolution of structures in our Universe. Here, we introduce a new method that leverages simulation-based inference and graph-based machine learning in order to infer the dark matter density profiles of dwarf galaxies from observable kinematics of stars gravitationally bound to these systems. Our approach aims to address some of the limitations of established methods based on dynamical Jeans modeling. We show that this novel method can place stronger constraints on dark matter profiles and, consequently, has the potential to weigh in on some of the ongoing puzzles associated with the small-scale structure of dark matter halos, such as the core-cusp discrepancy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Isospin dependence of the nuclear EMC effect from a global QCD analysis

We perform a new global QCD analysis of unpolarized parton distribution functions (PDFs) in the nucleon from proton, deuteron, and A = 3 data, including recent measurements of He 3 / D and H 3 / D cross section ratios from the MARATHON experiment at Jefferson Lab. Simultaneously inferring the PDFs and nucleon off-shell corrections allows both to be determined consistently, without theoretical assumptions about the isospin dependence of nuclear effects. The analysis provides strong evidence for the need of nucleon off-shell corrections to describe the A = 3 data, with large isoscalar and a suggestion of nonzero isovector contributions in A ≤ 3 nuclei. We find that the extracted EMC ratios of nuclear to nucleon structure functions for A = 2 and 3 differ from those naively extrapolated from heavy nuclei down to low A .

Cocuzza, C. [William & Mary] (ORCID:00000003492292↗
DOE OSTI2026

Iterative HOMER with uncertainties

We present iHOMER, an iterative version of the HOMER method to extract Lund fragmentation functions from experimental data. Through iterations, we address the information gap between latent and observable phase spaces and systematically remove bias. To quantify uncertainties on the inferred weights, we use a combination of Bayesian neural networks and uncertainty-aware regression. We find that the combination of iterations and uncertainty quantification produces well-calibrated weights that accurately reproduce the data distribution. A parametric closure test shows that the iteratively learned fragmentation function is compatible with the true fragmentation function.

Butter, Anja [Heidelberg Univ. (Germany); Sorbonne↗
DOE OSTI2020

First Images of the Protoplanetary Disk around PDS 201

Scattered light imaging has revealed nearly a dozen circumstellar disks around young Herbig Ae/Be stars—enabling studies of structures in the upper disk layers as potential signs of ongoing planet formation. We present the first images of the disk around the variable Herbig Ae star PDS 201 (V* V351 Ori) and an analysis of the images and spectral energy distribution through 3D Monte Carlo radiative transfer simulations and forward modeling. The disk is detected in three data sets with the Large Binocular Telescope Interferometer/Large Binocular Telescope mid-infrared camera at the Large Binocular Telescope, including direct observations in the Ks and L′ filters, and an L′ observation with the 360° vector apodizing phase plate coronagraph. The scattered light disk extends to a very large radius of ∼250 au, which places it among the largest of such disks. Exterior to the disk, we establish detection limits on substellar companions down to ∼5 M {sub Jup} at ≳1.″5 (≳500 au), assuming the Baraffe et al. models. The images show a radial gap extending to ∼0.″4 (∼140 au at a distance of 340 pc) that is also evident in the spectral energy distribution. The large gap is a possible signpost of multiple high-mass giant planets at orbital distances (∼60–100 au) that are unusually massive and widely separated compared to those of planet populations previously inferred from protoplanetary disk substructures.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1984

Ge abundances in the lunar mantle and implications for the origin of the Moon

Regardless of the origin of the Moon, metal segregation must have occurred within the moon in order to account for its low siderophile element abundances relative to the Earth or chondrites. Germanium is a strongly siderophile element whose bulk distribution coefficient indicates that it is not fractionated during igneous processes on the Moon. The variability in absolute Ge abundances in mare basalts and pristine highland rocks, rather than elemental ratios, can be used to infer lunar mantle abundances and processes. Literature data have been compiled for Ge abundances in mare basalts and pristine highland rocks. For some landing sites, samples with 12 ppb Ge were considered to be extreme outliers and are not included. The Apollo 15 samples are enriched in Ge by a factor of 2.5 over the Apollo 12, 16 and 17 samples. Other siderophile element variations have been found in the Moon. Based on this data, best estimate of the average Ge abundance in the silicate portion of the Moon is 3.52 ppb. The Moon is depleted, relative to chondritic abundances, by a factor of 38,000 normalized to Si. Two possible explanations for the observed variations in Ge abundance in the Moon are: (1) more metal may have segregated from some regions of the Moon than from others; or Ge-bearing material may been been added later in the evolution of the Moon.

Dickinson, T.↗