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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

The Effect of Missing Groups in the Calculation of the Solar Irradiance Deficit: Analysis of the Sunspot Areas from the SOON Network

Sunspot areas are one of the most important indices of solar activity. To obtain an extended time series covering multiple solar cycles one must combine data from different observatories after a proper comparison and calibration of the individual data sets. We compare the daily and group values of sunspot areas provided by the different stations from the Solar Optical Observing Network, SOON, which are determined using similar instruments and techniques. We investigate if there are systematic differences among the stations and whether the differences in the daily values can be attributed to missing groups in the records or errors in the measurements. We find significant differences among the stations of the SOON network in terms of sizes (average daily and group values), quality of observations and coverage (considering number of missing groups and data gaps). Our results indicate that calibration factors for daily values can be used with confidence to combine data sets from different stations. However, for some applications which require the location of the sunspot groups the same correction factors should not be used. We estimate the irradiance deficit due to sunspot through the Photometric Sunspot Index and compare the output from similar data sets to quantify the effect of missing groups. We find differences as high as 150 ppm during the maximum of solar cycle. The effect increases for sunspot groups near the center of the disk accounting for about 80% of the observed differences.

Luis Leuzzi↗
DOE OSTI2025

Real-Time event reconstruction for Nuclear Physics Experiments using Artificial Intelligence

Charged track reconstruction is a critical task in nuclear physics experiments, enabling the identification and analysis of particles produced in high-energy collisions. Machine learning (ML) has emerged as a powerful tool for this purpose, addressing the challenges posed by complex detector geometries, high event multiplicities, and noisy data. Traditional methods rely on pattern recognition algorithms like the Kalman filter, but ML techniques, such as neural networks, graph neural networks (GNNs), and recurrent neural networks (RNNs), offer improved accuracy and scalability. By learning from simulated and real detector data, ML models can identify and classify tracks, predict trajectories, and handle ambiguities caused by overlapping or missing hits. Moreover, ML-based approaches can process data in near-real-time, enhancing the efficiency of experiments at large-scale facilities like the Large Hadron Collider (LHC) and Jefferson Lab (JLAB). As detector technologies and computational resources evolve, ML-driven charged track reconstruction continues to push the boundaries of precision and discovery in nuclear physics. In these proceedings, we highlight advancements in charged track identification leveraging Artificial Intelligence within the CLAS12 detector, achieving a notable enhancement in experimental statistics compared to traditional methods. Additionally, we showcase real-time event reconstruction capabilities, including the inference of charged particle properties, such as momentum, direction, and species identification, at speeds matching data acquisition rates. These innovations enable the extraction of physics observables directly from the experiment in real-time.

Gavalian, Gagik (ORCID:0000000267385457)↗
DOE OSTI2025

Subspace-Driven Learning for Anomaly Detection in Process Transients

Nuclear power plant (NPP) monitoring and diagnostic centers are actively investigating and implementing automated anomaly detection algorithms to help plants catch anomalies sooner, thereby preventing or reducing the duration of unexpected shutdowns. Current machine learning-based anomaly detection methods are expected to be highly effective during stable, full-power operations because NPPs typically operate as baseload power generators, meaning there are extensive operating data available from plant equipment. However, it is expected that anomaly detection methods will face significant challenges during transient conditions (i.e., when power output falls below full power) because plants only occasionally operate at these lower power levels, generating sparse transient operational data, and resulting in false alarms or missed detections. Here, to address this issue, transfer learning is used, which for this problem leverages knowledge (in the form of learned features) from stable, full-power operations to improve detection accuracy during transient conditions, even with limited data. In this effort, a novel subspace approach is developed to transfer a subset of the data features from full power operation to transients. This approach is validated through experiments using synthetic data and was found to outperform two baseline transfer learning approaches in anomaly detection performance across a range of amounts of transient data used in the training process.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2024

From Text to Maps: LLM-Driven Extraction and Geotagging of Epidemiological Data

Epidemiological datasets are essential for public health analysis and decision-making, yet they remain scarce and often difficult to compile due to inconsistent data formats, language barriers, and evolving political boundaries. Traditional methods of creating such datasets involve extensive manual effort and are prone to errors in accurate location extraction. To address these challenges, we propose utilizing large language models (LLMs) to automate the extraction and geotagging of epidemiological data from textual documents. Our approach significantly reduces the manual effort required, limiting human intervention to validating a subset of records against text snippets and verifying the geotagging reasoning, as opposed to reviewing multiple entire documents manually to extract, clean, and geotag. Additionally, the LLMs identify information often overlooked by human annotators, further enhancing the dataset’s completeness. Our findings demonstrate that LLMs can be effectively used to semi-automate the extraction and geotagging of epidemiological data, offering several key advantages: (1) comprehensive information extraction with minimal risk of missing critical details; (2) minimal human intervention; (3) higher-resolution data with more precise geotagging; and (4) significantly reduced resource demands compared to traditional methods.

Harrod, Karly↗
DOE OSTI2019

Collective charge and spin dynamics in low-dimensional itinerant electron systems with spin-orbit coupling

This research project in theoretical condensed-matter physics aims to study the collective charge and spin excitations of itinerant quasi-two-dimensional electronic systems such as doped semiconductor nanostructures and carbon-based materials, in the presence of impurities and spin-orbit coupling. In previous work under DOE support, we had discovered an interesting interplay between many-body effects and Rashba and Dresselhaus spin-orbit interactions: contrary to many situation in spintronics where spin-orbit interactions lead to loss of spin coherence, collective spin modes are robust against such coherence losses, and behave as macroscopic quantum objects with a collective magnetic moment precessing about an effective spin-orbit magnetic field. Experimental data for GaAs and CdMnTe quantum wells, obtained by our collaborators at the University Pierre et Marie Curie in Paris, confirmed our predictions and suggested that the observed effect is universal. However, a full theoretical explanation of the effect is still missing, and a wealth of new experimental data remains to be analyzed. We therefore plan to address the following three interrelated objectives: 1. Theory of collective modes in chiral systems. We will explore the various ways in which spin-orbit interactions affect collective spin-density and spin-flip excitations in semiconductor quantum wells. The goal will be to develop a universal formalism for chiral collective modes, with a particular emphasis on analytic results for dispersion relations, which will be compared with experimental results from inelastic light scattering. We will calculate the linewidths of spin plasmons and spin-flip waves due to impurity scattering and spin Coulomb drag from first principles. 2.Many-body effects in noncollinear magnetism. We will describe electronic many-body effects using time-dependent density-functional theory (TDDFT) in the linear-response regime. Quantum wells with spin-orbit coupling are noncollinear itinerant magnetic systems at the crossover between two and three dimensions, which poses subtle challenges for standard exchange-correlation (xc) functionals such as the local-density approximation (LDA). We will develop new orbital-dependent xc functionals for noncollinear magnetic systems by generalizing the so-called STLS approach. 3. Beyond single semiconductor quantum wells. We will consider quantum well bilayer or multilayer systems with spin-orbit coupling, where the coupling between the quasi-2D electron systems leads to acoustic and optical modes (in phase and out of phase). How are these modes affected if the two layers have the same or different chirality? Plasmon modes have been widely studied in graphene and other topological materials, and we will study collective spin excitations in graphene and other carbon-based materials in the presence of spin-orbit coupling and magnetic fields. The potential impact of the proposed research will be both on a fundamental and a practical level. A universal theory of chiral collective modes will advance our understanding of the interplay between many-body effects and spin-orbit interactions in a wide variety of materials and systems. A generalized STLS approach for noncollinear magnetism will be of wide interest beyond the systems studied here. This work may also point towards new ways of manipulating and controlling spin waves in low-dimensional itinerant electron systems: this could be used to encode, transport and process information, which would be of interest to the field of magnon spintronics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Search for doubly and singly charged Higgs bosons decaying into vector bosons in multi-lepton final states with the ATLAS detector using proton-proton collisions at $\sqrt{s}$ = 13 TeV

A search for charged Higgs bosons decaying into W ± W ± or W ± Z bosons is performed, involving experimental signatures with two leptons of the same charge, or three or four leptons with a variety of charge combinations, missing transverse momentum and jets. A data sample of proton-proton collisions at a centre-of-mass energy of 13 TeV recorded with the ATLAS detector at the Large Hadron Collider between 2015 and 2018 is used. The data correspond to a total integrated luminosity of 139 fb -1 . The search is guided by a type-II seesaw model that extends the scalar sector of the Standard Model with a scalar triplet, leading to a phenomenology that includes doubly and singly charged Higgs bosons. Two scenarios are explored, corresponding to the pair production of doubly charged H ±± bosons, or the associated production of a doubly charged H ±± boson and a singly charged H ± boson. No significant deviations from the Standard Model predictions are observed. H ±± bosons are excluded at 95% confidence level up to 350 GeV and 230 GeV for the pair and associated production modes, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

A search for heavy Higgs bosons decaying into vector bosons in same-sign two-lepton final states in $pp$ collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

A search for heavy Higgs bosons produced in association with a vector boson and decaying into a pair of vector bosons is performed in final states with two leptons (electrons or muons) of the same electric charge, missing transverse momentum and jets. A data sample of proton–proton collisions at a centre-of-mass energy of 13 TeV recorded with the ATLAS detector at the Large Hadron Collider between 2015 and 2018 is used. The data correspond to a total integrated luminosity of 139 fb –1 . The observed data are in agreement with Standard Model background expectations. The results are interpreted using higher-dimensional operators in an effective field theory. Upper limits on the production cross-section are calculated at 95% confidence level as a function of the heavy Higgs boson’s mass and coupling strengths to vector bosons. Limits are set in the Higgs boson mass range from 300 to 1500 GeV, and depend on the assumed couplings. The highest excluded mass for a heavy Higgs boson with the coupling combinations explored is 900 GeV. Limits on coupling strengths are also provided.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Searches for pair production of charginos and top squarks in final states with two oppositely charged leptons in proton-proton collisions at $ \sqrt{s}=13$ TeV

A search for pair production of supersymmetric particles in events with two oppositely charged leptons (electrons or muons) and missing transverse momentum is reported. The data sample corresponds to an integrated luminosity of 35.9 fb$^{-1}$ of proton-proton collisions at $ \sqrt{s}=13 $ TeV collected with the CMS detector during the 2016 data taking period at the LHC. No significant deviation is observed from the predicted standard model background. The results are interpreted in terms of several simplified models for chargino and top squark pair production, assuming R-parity conservation and with the neutralino as the lightest supersymmetric particle. When the chargino is assumed to undergo a cascade decay through sleptons, with a slepton mass equal to the average of the chargino and neutralino masses, exclusion limits at 95% confidence level are set on the masses of the chargino and neutralino up to 800 and 320 GeV, respectively. For top squark pair production, the search focuses on models with a small mass difference between the top squark and the lightest neutralino. When the top squark decays into an off-shell top quark and a neutralino, the limits extend up to 420 and 360 GeV for the top squark and neutralino masses, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Solution of A. Einstein’s Problem on the Density of Matter in the Universe

On the basis of the special theory of relativity and a solution of the Schwarzschild equations of general relativity, a generalized equation of Friedmann–Einstein–de Sitter type has been obtained for the expanding Universe. As a result of our work on the boundary value problem for this equation, we have found the eigenvalue of the resulting eigenvalue problem, linking the density of the Universe in the comoving coordinates, the Hubble constant, and the gravitational constant. We have calculated a value for the density of matter in the Universe which is in agreement with the observational data. We have shown that the missing dark energy is the relativistic kinematic and potential energy of the expanding Universe.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Ultra-large-scale ab initio quantum chemical computation of bio-molecular systems: The case of spike protein of SARS-CoV-2 virus

The COVID-19 pandemic poses a severe threat to human health with an unprecedented social and economic disruption. Spike (S) glycoprotein of the SARS-CoV-2 virus is pivotal in understanding the virus anatomy, since it initiates the first contact with the ACE2 receptor in the human cell. We report results of ab initio computation of the spike protein, the largest ab initio quantum chemical computation to date on any bio-molecular system, using a divide and conquer strategy by focusing on individual structural domains. In this approach we divided the S-protein into seven structural domains: N-terminal domain (NTD), receptor binding domain (RBD), subdomain 1 (SD1), subdomain 2 (SD2), fusion peptide (FP), heptad repeat 1 with central helix (HR1-CH) and connector domain (CD). The entire Chain A has 14,488 atoms including the hydrogen atoms but excluding the amino acids with missing coordinates based on the PDB data (ID: 6VSB). The results include structural refinement, ab initio calculation of intramolecular bonding mechanism, 3- dimensional non-local inter-amino acid interaction with implications for the inter-domain interaction. Details of the electronic structure, interatomic bonding, partial charge distribution and the role played by hydrogen bond network are discussed. In the interaction among structural domains, we present new insights for crucial hinge-like movement and fusion process. Extension of such calculation to the interface between the S-protein binding domain and ACE2 receptor can provide a pathway for computational understanding of mutations and the design of therapeutic drugs to combat the COVID-19 pandemic.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗
DOE OSTI2017

Scientific data interpolation with low dimensional manifold model

Here, we propose to apply a low dimensional manifold model to scientific data interpolation from regular and irregular samplings with a significant amount of missing information. The low dimensionality of the patch manifold for general scientific data sets has been used as a regularizer in a variational formulation. The problem is solved via alternating minimization with respect to the manifold and the data set, and the Laplace–Beltrami operator in the Euler–Lagrange equation is discretized using the weighted graph Laplacian. Various scientific data sets from different fields of study are used to illustrate the performance of the proposed algorithm on data compression and interpolation from both regular and irregular samplings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

HPC formulations of optimization algorithms for tensor completion

Tensor completion is a powerful tool used to estimate or recover missing values in multi-way data. It has seen great success in domains such as product recommendation and healthcare. Tensor completion is most often accomplished via low-rank sparse tensor factorization, a computationally expensive non-convex optimization problem which has only recently been studied in the context of parallel computing. In this work, we study three optimization algorithms that have been successfully applied to tensor completion: alternating least squares (ALS), stochastic gradient descent (SGD), and coordinate descent (CCD++). We explore opportunities for parallelism on shared- and distributed-memory systems and address challenges such as memory- and operation-efficiency, load balance, cache locality, and communication. Among our advancements are a communication-efficient CCD++ algorithm, an ALS algorithm rich in level-3 BLAS routines, and an SGD algorithm which combines stratification with asynchronous communication. Furthermore, we show that introducing randomization during ALS and CCD++ can accelerate convergence. We evaluate our parallel formulations on a variety of real datasets on a modern supercomputer and demonstrate speedups through 16384 cores. Further, these improvements reduce time-to-solution from hours to seconds on real-world datasets. We show that after our optimizations, ALS is advantageous on parallel systems of small-to-moderate scale, while both ALS and CCD++ provide the lowest time-to-solution on large-scale distributed systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Search for gauge-mediated supersymmetry in events with at least one photon and missing transverse momentum in pp collisions at s = 13 TeV

A search for gauge-mediated supersymmetry (SUSY) in final states with photons and large missing transverse momentum is presented. The data sample of pp collisions at $\sqrt{s} = $ 13 TeV was collected with the CMS detector at the CERN LHC and corresponds to an integrated luminosity of 35.9 fb$^{-1}$. Data are compared with models in which the lightest neutralino has bino- or wino-like components, resulting in decays to photons and gravitinos, where the gravitinos escape detection. The event selection is optimized for both electroweak (EWK) and strong production SUSY scenarios. The observed data are consistent with standard model predictions, and limits are set in the context of a general gauge mediation model in which gaugino masses up to 980 GeV are excluded at 95% confidence level. Gaugino masses below 780 and 950 GeV are excluded in two simplified models with EWK production of mass-degenerate charginos and neutralinos. Stringent limits are set on simplified models based on gluino and squark pair production, excluding gluino (squark) masses up to 2100 (1750) GeV depending on the assumptions made for the decay modes and intermediate particle masses. This analysis sets the highest mass limits to date in the studied EWK models, and in the considered strong production models when the mass difference between the gauginos and the squarks or gluinos is small.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

The Need for a New LLNL Pulsed Sphere Neutron Leakage Spectra Series

Here, it is shown that spectra measured as part of the Lawrence Livermore National Laboratory Pulsed Sphere (LPS) program offer decisive information to locate formatting or physics issues in nuclear data of key interest for fusion reactor simulations. However, experiments from this measurement series are not benchmarks. For instance, their uncertainties are incomplete. There are also many open questions—e.g., on the setup, the detector response, and whether LPS are accurately modeled—that cannot be answered anymore given the limited documentation and that many of the experimenters are no longer actively working. This limited knowledge has implications when one tries to adjust nuclear data to LPS spectra. Usually, one adjusts to benchmarks representing an application with the hope to get more precise nuclear data for the application of interest where differential data might be scarce and/or to reduce nuclear data uncertainties in the application simulations. However, it is demonstrated that adjustment with LPS spectra without accounting for missing uncertainties and modeling potential biases in the experimental data leads to adjusted data that are highly unphysical. That means adjusted data differ significantly from evaluated data based on information from differential experiments; also, application quantities predicted with the adjusted data deviate distinctly from experimental ones. While we can approximate our limited knowledge on these experiments with Gaussian processes in the adjustment process, this modeling of bias is arbitrary rather than based on a physics explanation, calling into doubt the validity of resulting adjusted data. Thus, we discuss here the need for a new measurement series, learning from the strengths and weaknesses of the LPS program, to yield decisive and well-benchmarked integral experiments to support fusion reactor research.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

ATCA survey of ammonia in the galactic center: The temperatures of dense gas clumps between Sgr A* and Sgr B2

We present a large-scale, interferometric survey of ammonia (1, 1) and (2, 2) toward the Galactic center observed with the Australia Telescope Compact Array. The survey covers Δℓ ∼ 1° (∼150 pc at an assumed distance of 8.5 kpc) and Δb ∼ 0.°2 (∼30 pc) which spans the region between the supermassive black hole Sgr A* and the massive star forming region Sgr B2. The resolution is ∼20'' (∼0.8 pc) and emission at scales ≳ 2' (≳ 3.2 pc) is filtered out due to missing interferometric short spacings. Consequently, the data represent the denser, compact clouds and disregards the large-scale, diffuse gas. Many of the clumps align with the 100 pc dust ring and mostly anti-correlate with 1.2 cm continuum emission. We present a kinetic temperature map of the dense gas. The temperature distribution peaks at ∼38 K with a width at half maximum between 18 K and 61 K (measurements sensitive within T {sub kin} ∼ 10-80 K). Larger clumps are on average warmer than smaller clumps which suggests internal heating sources. Our observations indicate that the circumnuclear disk ∼1.5 pc around Sgr A* is supplied with gas from the 20 km s{sup –1} molecular cloud. This gas is substantially cooler than gas ∼3-15 pc away from Sgr A*. We find a strong temperature gradient across Sgr B2. Ammonia column densities correlate well with SCUBA 850 μm fluxes, but the relation is shifted from the origin, which may indicate a requirement for a minimum amount of dust to form and shield ammonia. Around the Arches and Quintuplet clusters we find shell morphologies with UV-influenced gas in their centers, followed by ammonia and radio continuum layers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Correlations of π N partial waves for multireaction analyses

In the search for missing baryonic resonances, many analyses include data from a variety of pion- and photon-induced reactions. For elastic πN scattering, however, usually the partial waves of the SAID (Scattering Analysis Interactive Database) or other groups are fitted, instead of data. We provide the partial-wave covariance matrices needed to perform correlated χ 2 fits, in which the obtained χ 2 equals the actual χ 2 up to nonlinear and normalization corrections. For any analysis relying on partial waves extracted from elastic pion scattering, this is a prerequisite to assess the significance of resonance signals and to assign any uncertainty on results. Lastly, the influence of systematic errors is also considered.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗