SEARCH · Search NASA

Results for “ordered search”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Optimization and Multimachine Learning Algorithms to Predict Nanometal Surface Area Transfer Parameters for Gold and Silver Nanoparticles

Interactions between gold metallic nanoparticles and molecular dyes have been well described by the nanometal surface energy transfer (NSET) mechanism. However, the expansion and testing of this model for nanoparticles of different metal composition is needed to develop a greater variety of nanosensors for medical and commercial applications. In this study, the NSET formula was slightly modified in the size-dependent dampening constant and skin depth terms to allow for modeling of different metals as well as testing the quenching effects created by variously sized gold, silver, copper, and platinum nanoparticles. Overall, the metal nanoparticles followed more closely the NSET prediction than for Förster resonance energy transfer, though scattering effects began to occur at 20 nm in the nanoparticle diameter. To further improve the NSET theoretical equation, an attempt was made to set a best-fit line of the NSET theoretical equation curve onto the Au and Ag data points. An exhaustive grid search optimizer was applied in the ranges for two variables, 0.1≤C≤2.0 and 0≤α≤4, representing the metal dampening constant and the orientation of donor to the metal surface, respectively. Three different grid searches, starting from coarse (entire range) to finer (narrower range), resulted in more than one million total calculations with values C=2.0 and α=0.0736. The results improved the calculation, but further analysis needed to be conducted in order to find any additional missing physics. With that motivation, two artificial intelligence/machine learning (AI/ML) algorithms, multilayer perception and least absolute shrinkage and selection operator regression, gave a correlation coefficient, R2, greater than 0.97, indicating that the small dataset was not overfitting and was method-independent. This analysis indicates that an investigation is warranted to focus on deeper physics informed machine learning for the NSET equations.

Demers, Steven M. E. (ORCID:0000000192213246)↗

DOE OSTI2024

EFIT-Prime: Probabilistic and physics-constrained reduced-order neural network model for equilibrium reconstruction in DIII-D

We introduce EFIT-Prime, a novel machine learning surrogate model for EFIT (Equilibrium FIT) that integrates probabilistic and physics-informed methodologies to overcome typical limitations associated with deterministic and ad hoc neural network architectures. EFIT-Prime utilizes a neural architecture search-based deep ensemble for robust uncertainty quantification, providing scalable and efficient neural architectures that comprehensively quantify both data and model uncertainties. Physically informed by the Grad–Shafranov equation, EFIT-Prime applies a constraint on the current density J tor and a smoothness constraint on the first derivative of the poloidal flux, ensuring physically plausible solutions. Furthermore, the spatial location of the diagnostics is explicitly incorporated in the inputs to account for their spatial correlation. Extensive evaluations demonstrate EFIT-Prime's accuracy and robustness across diverse scenarios, most notably showing good generalization on negative-triangularity discharges that were excluded from training. Timing studies indicate an ensemble inference time of 15 ms for predicting a new equilibrium, offering the possibility of plasma control in real-time, if the model is optimized for speed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Automated annotation of scientific texts for ML-based keyphrase extraction and validation

Advanced omics technologies and facilities generate a wealth of valuable data daily; however, the data often lack the essential metadata required for researchers to find, curate, and search them effectively. The lack of metadata poses a significant challenge in the utilization of these data sets. Machine learning (ML)–based metadata extraction techniques have emerged as a potentially viable approach to automatically annotating scientific data sets with the metadata necessary for enabling effective search. Text labeling, usually performed manually, plays a crucial role in validating machine-extracted metadata. However, manual labeling is time-consuming and not always feasible; thus, there is a need to develop automated text labeling techniques in order to accelerate the process of scientific innovation. This need is particularly urgent in fields such as environmental genomics and microbiome science, which have historically received less attention in terms of metadata curation and creation of gold-standard text mining data sets. In this paper, we present two novel automated text labeling approaches for the validation of ML-generated metadata for unlabeled texts, with specific applications in environmental genomics. Our techniques show the potential of two new ways to leverage existing information that is only available for select documents within a corpus to validate ML models, which can then be used to describe the remaining documents in the corpus. The first technique exploits relationships between different types of data sources related to the same research study, such as publications and proposals. The second technique takes advantage of domain-specific controlled vocabularies or ontologies. In this paper, we detail applying these approaches in the context of environmental genomics research for ML-generated metadata validation. Our results show that the proposed label assignment approaches can generate both generic and highly specific text labels for the unlabeled texts, with up to 44% of the labels matching with those suggested by a ML keyword extraction algorithm.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2024

Absorption of Axion Dark Matter in a Magnetized Medium

Detection of axion dark matter heavier than an meV is hindered by its small wavelength, which limits the useful volume of traditional experiments. This problem can be avoided by directly detecting in-medium excitations, whose ∼ meV – eV energies are decoupled from the detector size. We show that for any target inside a magnetic field, the absorption rate of electromagnetically coupled axions into in-medium excitations is determined by the dielectric function. As a result, the plethora of candidate targets previously identified for sub-GeV dark matter searches can be repurposed as broadband axion detectors. We find that a kg yr exposure with noise levels comparable to recent measurements is sufficient to probe parameter space currently unexplored by laboratory tests. Noise reduction by only a few orders of magnitude can enable sensitivity to the QCD axion in the ∼ 10 meV – 10 eV mass range. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Optimal Twirling Depth for Classical Shadows in the Presence of Noise

The classical shadows protocol is an efficient strategy for estimating properties of an unknown state p using a small number of state copies and measurements. In its original form, it involves twirling the state with unitaries from some ensemble and measuring the twirled state in a fixed basis. It was recently shown that for computing local properties, optimal sample complexity (copies of the state required) is remarkably achieved for unitaries drawn from shallow depth circuits composed of local entangling gates, as opposed to purely local (zero depth) or global twirling (infinite depth) ensembles. Here, we consider the sample complexity as a function of the depth of the circuit, in the presence of noise. We find that this noise has important implications for determining the optimal twirling ensemble. Under fairly general conditions, we (i) show that any single-site noise can be accounted for using a depolarizing noise channel with an appropriate damping parameter f, (ii) compute thresholds f th at which optimal twirling reduces to local twirling for Pauli operators, (iii) nth order Renyi entropies (n ≥2), and (iv) provide a meaningful upper bound t max on the optimal circuit depth for any finite noise strength f, which applies to observables and entanglement entropy measurements. In conclusion, these thresholds strongly constrain the search for optimal strategies to implement shadow tomography and are easily tailored to the experimental system at hand.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields

Model ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields. However, their widespread adoption is hindered by high computational costs and overconfident error estimates. In this work, we address these challenges by leveraging distributions of per-sample errors obtained during training and employing a distance-based similarity search in the model latent space. Our method, which we call LTAU (Loss Trajectory Analysis for Uncertainty), efficiently estimates the full probability distribution function of errors for any test point using the logged training errors, achieving speeds that are 2–3 orders of magnitudes faster than typical ensemble methods and allowing it to be used for tasks where training or evaluating multiple models would be infeasible. We apply LTAU towards estimating parametric uncertainty in atomistic force fields (LTAU-FF), demonstrating that it produces well-calibrated confidence intervals and predicts errors that correlate strongly with the true errors for data near the training domain. Furthermore, we show that the errors predicted by LTAU-FF can be used in practical applications for detecting out-of-domain data, tuning model performance, and predicting failure during simulations. We believe that LTAU will be a valuable tool for uncertainty quantification in atomistic force fields and is a promising method that should be further explored in other domains of machine learning.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Detection of Millimeter-wavelength Flares from Two Accreting White Dwarf Systems in the SPT-3G Galactic Plane Survey

Blind discoveries of millimeter-wave transient events in nontargeted surveys, as opposed to follow-up or pointed observations, have only become possible in the past decade using cosmic microwave background surveys. Here we present the first results from the SPT-3G Galactic Plane Survey—the first dedicated high-sensitivity, wide-field, time-domain, millimeter-wave survey of the Galactic Plane, conducted with the South Pole Telescope (SPT) using the SPT-3G camera. The survey field covers approximately 100 deg 2 near the Galactic center. In 2023 and 2024, this survey consisted of roughly 1500 individual 20 minute observations in three bands centered at 95, 150, and 220 GHz, with plans for more observations in the coming years. We report the detection of two transient events exceeding a 5σ threshold in both the 95 and 150 GHz bands in the first 2 yr of SPT-3G Galactic Plane Survey data. Both events are unpolarized and exhibit durations of approximately 1 day, with peak flux densities at 150 GHz of at least 50 mJy. The peak isotropic luminosities at 150 GHz are on the order of 10 31 erg s −1 . Both events are associated with previously identified accreting white dwarfs. Magnetic reconnection in the accretion disk is a likely explanation for the observed millimeter flares. In the future, we plan to expand the transient search in the Galactic Plane by lowering the detection threshold, enabling single-band detections, analyzing lightcurves on a range of timescales, and including additional data from future observations.

Wan, Y. [Univ. of Illinois at Urbana-Champaign, IL↗

DOE OSTI2025

Exploring Photoproduced $\eta^{(}{'^{)}} \pi^{0}$ Systems in the Search for Exotic Hadrons at GlueX

Physicists have long been captivated by the spectrum of hadrons, aiming to better comprehend the fundamental building blocks of matter. While various experiments have laid the foundation for this spectrum, Lattice Quantum Chromodynamics has revealed new states with forbidden JPC values. This has challenged the constituent quark model, suggesting spin-exotic hybrid mesons that could reshape our understanding of hadronic structure and quark-gluon interactions through gluonic excitations. The GlueX experiment at Jefferson Lab has played a pivotal role in this pursuit, with its efforts centered on analyzing photoproduction data. In order to investigate the lightest predicted exotic state with JPC=1−+ , known as the π1(1600) meson, significant attention has been directed towards both ηπ0 and η′π0 systems. Detailed ongoing amplitude analysis studies of γp→η(′)π0p , leveraging the polarization of the photon beam at the GlueX experiment will be highlighted. Furthermore, first studies on the moments of angular distributions for these channels will also be shown, along with the differential cross section results for the a2(1320) meson. These results aid in identifying the dominant production mechanisms and offer valuable insights into complex hadronic interactions. Ultimately, this contributes to the ongoing search for and future identification of exotic hybrid meson candidates.

Baldwin, Zachary↗

DOE OSTI2025

Optimizing the optimizer for physics-informed neural networks and Kolmogorov-Arnold networks

Physics-Informed Neural Networks (PINNs) have revolutionized the computation of PDE solutions by integrating partial differential equations (PDEs) into the neural network’s training process as soft constraints, becoming an important component of the scientific machine learning (SciML) ecosystem. More recently, physics-informed Kolmogorv-Arnold networks (PIKANs) have also shown to be effective and comparable in accuracy with PINNs. In their current implementation, both PINNs and PIKANs are mainly optimized using first-order methods like Adam, as well as quasi-Newton methods such as BFGS and its low-memory variant, L-BFGS. However, these optimizers often struggle with highly nonlinear and non-convex loss landscapes, leading to challenges such as slow convergence, local minima entrapment, and (non)degenerate saddle points. In this study, we investigate the performance of Self- Scaled BFGS (SSBFGS), Self-Scaled Broyden (SSBroyden) methods and other advanced quasi-Newton schemes, including BFGS and L-BFGS with different line search strategies. These methods dynamically rescale updates based on historical gradient information, thus enhancing training efficiency and accuracy. We systematically compare these optimizers – using both PINNs and PIKANs – on key challenging PDEs, including the Burgers, Allen-Cahn, Kuramoto-Sivashinsky, Ginzburg-Landau, and Stokes equations. Additionally, we evaluate the performance of SSBFGS and SSBroyden for Deep Operator Network (DeepONet) architectures, demonstrating their effectiveness for data-driven operator learning. Our findings provide state-of-the-art results with orders-of-magnitude accuracy improvements without the use of adaptive weights or any other enhancements typically employed in PINNs. More broadly, our work reveal insights into the effectiveness of quasi-Newton optimization strategies in significantly improving the convergence and accurate generalization of PINNs and PIKANs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Subsurface cosmogenic and radiogenic production of 42 Ar

Radioactive decays from 42 Ar and its progeny 42 K are potential background sources in large-scale liquid-argon-based neutrino and dark matter experiments. In the atmosphere, 42 Ar is produced primarily by cosmogenic activation on 40 Ar. The use of low radioactivity argon from cosmogenically shielded underground sources can expand the reach and sensitivity of liquid-argon-based rare event searches. We estimate 42 Ar production underground by nuclear reactions induced by natural radioactivity and cosmic-ray muon-induced interactions. At 3,000 mwe, 42 Ar production rate is 1.8×10 -3 atoms per ton of crust per year, 7 orders of magnitude smaller than the 39 Ar production rate at a similar depth in the crust. In conclusion, by comparing the calculated production rate of 42 Ar to that of 39 Ar for which the concentration has been measured in an underground gas sample, we estimate the activity of 42 Ar in gas extracted from 3,000 mwe depth to be less than two decays per ton of argon per year.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2025

Studies for the selection of fully contained muon neutrino CC events in the ICARUS T600 detector at Fermilab

The ICARUS T600 LAr-TPC detector has started its new physics runs in June 2022 at Fermilab within the SBN program. This detector is recording neutrino interactions from the Booster BNB neutrino beam in order to definitively clarify the open questions related to the possible existence of sterile neutrinos, as suggested by numerous observed experimental anomalies. In addition the neutrinos events recorded from the NuMI off-axis beam are under study in order to perform neutrino-Argon cross section measurements. At Fermilab, ICARUS is facing a challenging experimental condition: the detector is presently installed essentially at Earth surface where cosmic ray particles can become a serious source of background for the neutrino event search. This condition makes it necessary to deploy suitable automatic tools for the identification, selection, and measurement of the neutrino events among the millions of events triggered by cosmics. In this contribution, two automatic selection procedures devoted to the identification of fully contained muon neutrino interactions in the BNB neutrino beam will be presented, together with the first results of their application on simulated and on recently recorded events in the T600 detector.

Farnese, Christian [INFN, Padua] (ORCID:0000000163↗

DOE OSTI2025

Studies for the Selection of Fully Contained Muon Neutrino CC Events in the ICARUS T600 Detector at Fermilab

Farnese, Christian [INFN, Padua]↗

DOE OSTI2024

Quantum Spin Dynamics Due to Strong Kitaev Interactions in the Triangular-Lattice Antiferromagnet CsCeSe 2

The extraordinary properties of the Kitaev model have motivated an intense search for new physics in materials that combine geometrical and bond frustration. In this Letter, we employ inelastic neutron scattering, spin wave theory, and exact diagonalization to study the spin dynamics in the perfect triangular-lattice antiferromagnet (TLAF) CsCeSe 2 . This material orders into a stripe phase, which is demonstrated to arise as a consequence of the off-diagonal bond-dependent terms in the spin Hamiltonian. By studying the spin dynamics at intermediate fields, we identify an interaction between the single-magnon state and the two-magnon continuum that causes decay of coherent magnon excitations, level repulsion, and transfer of spectral weight to the continuum that are controlled by the strength of the magnetic field. Our results provide a microscopic mechanism for the stabilization of the stripe phase in TLAF and show how complex many-body physics can be present in the spin dynamics in a magnet with strong Kitaev coupling even in an ordered ground state. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Phase transitions and dimensional cross-over in layered confined solids

The nature of solid phases and cross-over of order–disorder phase transitions from two-dimensional (2D) layers to three-dimensional (3D) bulk in confined atomic systems remain largely unexplained. To this end, we consider noble gases and aluminum confined between graphene sheets at different pressures and temperatures. Using crystal structure search methods and molecular dynamics based on machine-learned potentials with quantum-mechanical accuracy, we identify structures of multilayer confined solids that deviate from simple close packing. Upon heating, we find that confined 2D monolayers melt according to the two-step continuous Kosterlitz–Thouless–Halperin–Nelson–Young theory. However, multilayer solids transition continuously into an intermediate layered-hexatic phase before melting discontinuously into an isotropic liquid. This intermediate phase persists at least up to 12 layers studied here. This change can be qualitatively understood based on the cross-over from 2D topological defects toward 3D ones during melting as the number of layers increases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Calibrating two jets at once

Jet-energy calibration is an important aspect of many measurements and searches at the LHC. Currently, these calibrations are performed on a per-jet basis, i.e., agnostic to the properties of other jets in the same event. In this work, we propose taking advantage of the correlations induced by momentum conservation between jets in order to improve their jet-energy calibration. By fitting the p T asymmetry of dijet events in simulation, while remaining agnostic to the p T spectra themselves, we are able to obtain correlation-improved maximum likelihood estimates. This approach is demonstrated with simulated jets from the CMS detector, yielding a 3%–5% relative improvement in the jet-energy resolution, corresponding to a quadrature improvement of approximately 35%. Published by the American Physical Society 2024

Gambhir, Rikab (ORCID:0000000251080448)↗

DOE OSTI2026

Observing dark matter decays to gravitons via graviton-photon conversion

Since dark matter is only known to have gravitational interactions, it may plausibly decay to gravitons on cosmological timescales. Although such a scenario can be easily realized, there are currently no known limits on this possibility based on indirect-detection methods. We propose a new search strategy that exploits the Gertsenshtein effect, whereby the gravitons produced in dark matter decays can convert to photons in large-scale magnetic fields along the line of sight to an observer. These conversions primarily occur within cosmological filaments which occupy a large (order unity) volume fraction and contain ∼ 10 – 100 nG fields with ∼ Mpc coherence lengths. Thus, dark matter decays to gravitons predict an irreducible population of extragalactic photons, which we constrain using the extragalactic gamma-ray background measured by the Fermi-LAT telescope. Using this method, we derive the first-ever constraints on the dark matter lifetime in the 0.1 GeV - 10 8 GeV mass range, contingent on the assumed filament magnetic field strength. We also make projections for the Advanced Particle-astrophysics Telescope, which can improve sensitivity to this dark matter decay channel by an order of magnitude beyond those we set using Fermi-LAT data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Dark Matter Search in Pu-241 Beta Decays

Our universe is predominantly composed of mysterious unknowns: Dark Matter (DM) and Dark Energy. DM constitutes approximately 27% of the universe, yet its nature and origins remain elusive. Among the various candidates for dark matter, this project focuses on the search for keV-scale sterile neutrinos. A sterile neutrino is a hypothetical particle that belongs to the neutrino family and may possess a higher mass than ordinary neutrinos. They are considered a compelling warm dark matter candidate if their mass is on the order of kilo-electronvolts. Existence of sterile neutrinos can be investigated via high precision beta spectroscopy. Sterile neutrinos can be produced during nuclear beta decay processes, similar to ordinary active neutrinos, but they uniquely alter the beta decay spectrum. The emission of sterile neutrinos will introduce a distinctive "kink" structure in the middle of the beta spectrum. This project investigated the existence of sterile neutrino dark matter by employing high-precision decay energy spectroscopy of Pu-241 beta decays using magnetic microcalorimeters (MMCs). The Pu-241 source material is embedded within the microcalorimeter detectors, enabling the measurement of full beta decay energies with ultra-high energy resolution. In this project, the most precise Pu-241 spectrum to date was obtained by collecting a total of 1 billion beta decay events in the MMC detectors. This extensive data set allows us to investigate evidence of keV sterile neutrino emission in beta decays with unprecedented sensitivity.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Bosenovae with quadratically-coupled scalars in quantum sensing experiments

Abstract Ultralight dark matter (ULDM) particles of massm ϕ ≲ 1 eV can form boson stars in DM halos. Collapse of boson stars leads to explosive bosenova emission of copious relativistic ULDM particles. In this work, we analyze the sensitivity of terrestrial and space-based experiments to detect such relativistic scalar ULDM particles interacting through quadratic couplings with Standard Model constituents, including electrons, photons, and gluons. We highlight key differences with searches for linear ULDM couplings. Screening of ULDM with quadratic couplings near the surface of the Earth can significantly impact observations in terrestrial experiments, motivating future space-based experiments. We demonstrate excellent ULDM discovery prospects, especially for quantum sensors, which can probe quadratic couplings orders below existing constraints by detecting bosenova events in the ULDM mass range 10 −23 eV ≲m ϕ ≲ 10 −5 eV. We also report updated constraints on quadratic couplings of ULDM in case it comprises cold DM.

Physics↗