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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Radio Afterglow Detection and AI-driven Response (RADAR): A Federated Framework for Gravitational-wave Event Follow-up

The landmark detection of both gravitational waves (GWs) and electromagnetic (EM) radiation from the binary neutron star merger GW170817 has spurred efforts to streamline the follow-up of GW alerts in current and future observing runs of ground-based GW detectors. Within this context, the radio band of the EM spectrum presents unique challenges. Sensitive radio facilities capable of detecting the faint radio afterglow seen in GW170817, and with sufficient angular resolution, have small fields of view compared to typical GW localization areas. Additionally, theoretical models predict that the radio emission from binary neutron star mergers can evolve over weeks to years, necessitating long-term monitoring to probe the physics of the various postmerger ejecta components. These constraints, combined with limited radio observing resources, make the development of more coordinated follow-up strategies essential—especially as the next generation of GW detectors promises a dramatic increase in detection rates. Here, we present RADAR, a framework designed to address these challenges by promoting community-driven information sharing, federated data analysis, and system resilience, while integrating AI methods for both GW signal identification and radio data aggregation. We show that it is possible to preserve data rights while sharing models that can help design and/or update follow-up strategies. We demonstrate our approach through a case study of GW170817, and discuss future directions for refinement and broader application.

Gravitational waves↗
DOE OSTI2021

SANS quantification of bound water in water-soluble polymers across multiple concentration regimes

Contrast-variation small-angle neutron scattering (CV-SANS) is a widely used technique for quantifying hydration water in soft matter systems, but it is predominantly applied in the dilute regime or for systems with a well-defined structure factor. Here, CV-SANS was used to quantify the number of hydration water molecules associating with three water-soluble polymers with different critical solution temperatures and types of water–solute interactions in dilute, semidilute, and concentrated solution through the exploration of novel methods of data fitting and analysis. Multiple SANS fitting workflows with varying levels of model assumptions were evaluated and compared to give insight into SANS model selection. These fitting pathways ranged from general, model-free algorithms to more standard form and structure factor fitting. In addition, Monte Carlo bootstrapping was evaluated as a method to estimate parameter uncertainty through simulation of technical replicates. The most robust fitting workflow for dilute solutions was found to be form factor fitting without CV-SANS ( i.e. polymer in 100% D 2 O). For semidilute and concentrated solutions, while the model-free approach can be mathematically defined for CV-SANS data, the addition of a structure factor imposes physical constraints on the optimization problem, suggesting that the optimal fitting pathway should include appropriate form and structure factor models. The measured hydration numbers were consistent with the number of tightly bound water molecules associated with each monomer unit, and the concentration dependence of the hydration number was largely governed by the chemistry-specific interactions between water and polymer. Polymers with weaker water–polymer interactions ( i.e. those with fewer hydration water molecules) were found to have more bound water at higher concentrations than those with stronger water–polymer interactions due to the increase in the number of forced water–polymer contacts in the concentrated system. This SANS-based method to count hydration water molecules can be applied to polymers in any concentration regime, which will lead to improved understanding of water–polymer interactions and their impact on materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Wavelet Denoising of Radio Observations of Rotating Radio Transients (RRATs): Improved Timing Parameters for Eight RRATs

Rotating radio transients (RRATs) are sporadically emitting pulsars detectable only through searches for single pulses. While over 100 RRATs have been detected, only a small fraction (roughly 20%) have phase-connected timing solutions, which are critical for determining how they relate to other neutron star populations. Detecting more pulses in order to achieve solutions is key to understanding their physical nature. Astronomical signals collected by radio telescopes contain noise from many sources, making the detection of weak pulses difficult. Applying a denoising method to raw time series prior to performing a single-pulse search typically leads to a more accurate estimation of their times of arrival (TOAs). Taking into account some features of RRAT pulses and noise, we present a denoising method based on wavelet data analysis, an image-processing technique. Assuming that the spin period of an RRAT is known, we estimate the frequency spectrum components contributing to the composition of RRAT pulses. This allows us to suppress the noise, which contributes to other frequencies. We apply the wavelet denoising method including selective wavelet reconstruction and wavelet shrinkage to the de-dispersed time series of eight RRATs with existing timing solutions. The signal-to-noise ratio (S/N) of most pulses are improved after wavelet denoising. Compared to the conventional approach, we measure 12%–69% more TOAs for the eight RRATs. The new timing solutions for the eight RRATs show 16%–90% smaller estimation error of most parameters. Thus, we conclude that wavelet analysis is an effective tool for denoising RRATs signal.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Absolute photoneutron cross sections of Sm isotopes

Photoneutron cross sections for seven samarium isotopes, {sup 144}Sm, {sup 147}Sm, {sup 148}Sm, {sup 149}Sm, {sup 150}Sm, {sup 152}Sm and {sup 154}Sm, have been investigated near neutron emission threshold using quasimonochromatic laser-Compton scattering γ-rays produced at the synchrotron radiation facility NewSUBARU. The results are important for nuclear astrophysics calculations and also for probing γ-ray strength functions in the vicinity of neutron threshold. Here we describe the neutron detection system and we discuss the related data analysis and the necessary method improvements for adapting the current experimental method to the working parameters of the future Gamma Beam System of Extreme Light Infrastructure - Nuclear Physics facility.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

TIME-AVERAGE-BASED METHODS FOR MULTI-ANGULAR SCALE ANALYSIS OF COSMIC-RAY DATA

Over the past decade, a number of experiments dealt with the problem of measuring the arrival direction distribution of cosmic rays, looking for information on the propagation mechanisms and the identification of their sources. Any deviation from the isotropy may be regarded to as a signature of unforeseen or unknown phenomena, mostly if well localized in the sky and occurring at low rigidity. It induced experimenters to search for excesses down to angular scales as narrow as 10 Degree-Sign , disclosing the issue of properly filtering contributions from wider structures. A solution commonly envisaged was based on time-average methods to determine the reference value of cosmic-ray flux. Such techniques are nearly insensitive to signals wider than the time window in use, thus allowing us to focus the analysis on medium- and small-scale signals. Nonetheless, the signal often cannot be excluded in the calculation of the reference value, which induces systematic errors. The use of time-average methods recently revealed important discoveries about the medium-scale cosmic-ray anisotropy, present both in the northern and southern hemispheres. It is known that the excess (or deficit) is observed as less intense than in reality and that fake deficit zones are rendered around true excesses because of the absolute lack of knowledge a priori of which signal is true and which is not. This work is an attempt to critically review the use of time-average-based methods for observing extended features in the cosmic-ray arrival distribution pattern.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Gravitational waves from rotating neutron stars: Current limits and prospects

Rotating neutron stars that emit continuous gravitational waves are among promising targets of the LIGO and Virgo detectors: sufficiently large non-axisymmetrie deformation w.r.t. the axis of rotation of a star generates a time-varying mass-quadrupole moment and henceforth gravitational wave emission. The departure from an axisymmetrie shape may be caused by the internal magnetic field and/or elastic stresses in the crust/core. If detected, it will provide novel insight into the presently obscured details of the interior neutron-star structure. This talk presents basic types of searches for such signals: from targeted searches from known pulsars to all-sky wide parameter searches for unknown objects. Selected methods used in the data analysis and to calculate the upper limits on the gravitational waves in the initial phase of LIGO and Virgo projects are briefly described. Observational results of LIGO and Virgo collaborations include “beating” the spin-down limit for the Crab and Vela pulsars [7, 11], search for a coherent signal from the direction of the Cas A supernova remnant [5], as well as the Galactic center [1], SN1987A and the Sco-X1 binary with the cross-correlation method [6], and the all-sky searches for signals of unknown position and frequency [2, 4, 8].

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Production and discovery of neutron-rich isotopes by fragmentation of 198 Pt

Production cross sections were measured for fragments produced by an 85 MeV/u 198 Pt beam incident on a beryllium target. Event-by-event particle identification of A, Z, and q for the reaction products was performed by employing energy loss, time-of-flight, magnetic rigidity, and total kinetic energy measurements. Over 70 nuclei in the Hf-Pt region were identified, including three isotopes first observed in this work: 191,192 Hf and 189 Lu. Due to the existence of multiple charge states between H-like and C-like ions, a new analysis method was introduced, incorporating Monte Carlo calculations of charge state fractions for a given charge state of the projectile residue just after the reaction. For the first time, charge-state probability distribution functions after the reaction have been deduced from experimental data. Furthermore, this study provides insight into how to produce key nuclides near N = 126 and the ability of a fragmentation residue to retain electrons from the primary beam.

190 ≤ A ≤ 219↗
DOE OSTI2025

Systematic characterization of unknown compounds via dimensionality reduction of time series

Analysis of ambient aerosols provides valuable insight into particle sources and formation chemistry. However, due to the complexity of atmospheric data and the dynamic nature of aerosol composition, a substantial fraction of data often become discarded by conventional analysis methods. Furthermore, a large fraction of chemical species within those data are unidentifiable due to a lack of matching spectral information, resulting in suboptimal characterization of chemical composition. Previous work has demonstrated techniques for cataloging analytes in a chromatographic dataset by deconvolution of mass spectra, but integration of these analytes throughout a large dataset remains time consuming. Here, we present a method to automatically identify an ion for quantitation for single-ion chromatogram based peak fitting and integration, enabling comprehensive integration of analytes with minimal user interaction. The resulting time series are clustered with a machine-learning based dimensionality reduction technique to systematically investigate the underlying characteristics of the categorized analytes and gain new insights into the chemical composition and physicochemical properties of the unidentifiable analytes. We apply these methods to existing atmospheric datasets collected in Manacapuru, Brazil during the GoAmazon2014/5 campaign to identify new analytes and interpret their variability and transformations in the atmosphere. The analysis results generate 408 time series from cataloged analytes of interest, and the clustering of those time series with spherical k-means results in 8 distinct clusters. We find the analytes form clusters based on their distinct physicochemical properties, demonstrating the method’s ability to systematically identify and selectively filter contaminants and instrumental analytes and characterize the unidentifiable analytes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Sample preparation and biomass determination of SRF model mixture using cryogenic milling and the adapted balance method

Highlights: • An alternative sample comminution procedure for SRF is tested. • Proof of principle is shown on a SRF model mixture. • The biogenic content of the SRF is analyzed with the adapted balance method. • The novel method combines combustion analysis and a data reconciliation algorithm. • Factors for the variance of the analysis results are statistically quantified. - Abstract: The biogenic fraction of a simple solid recovered fuel (SRF) mixture (80 wt% printer paper/20 wt% high density polyethylene) is analyzed with the in-house developed adapted balance method (aBM). This fairly new approach is a combination of combustion elemental analysis (CHNS) and a data reconciliation algorithm based on successive linearisation for evaluation of the analysis results. This method shows a great potential as an alternative way to determine the biomass content in SRF. However, the employed analytical technique (CHNS elemental analysis) restricts the probed sample mass to low amounts in the range of a few hundred milligrams. This requires sample comminution to small grain sizes (<200 μm) to generate representative SRF specimen. This is not easily accomplished for certain material mixtures (e.g. SRF with rubber content) by conventional means of sample size reduction. This paper presents a proof of principle investigation of the sample preparation and analysis of an SRF model mixture with the use of cryogenic impact milling (final sample comminution) and the adapted balance method (determination of biomass content). The so derived sample preparation methodology (cutting mills and cryogenic impact milling) shows a better performance in accuracy and precision for the determination of the biomass content than one solely based on cutting mills. The results for the determination of the biogenic fraction are within 1–5% of the data obtained by the reference methods, selective dissolution method (SDM) and {sup 14}C-method ({sup 14}C-M)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

A Generalized Bio-inspired Method for Discovering Sequence-based Signatures

Many phenomena that we wish to discover are comprised of sequences of events or event primitives. Often signatures are constructed to identify such phenomena using either distributions or frequencies of attributes, or specific subsequences that are known to correlate to the phenomena. Distribution-based identification does not capture the essence of the sequence of behaviors and therefore may suffer from lack of specificity. At the other extreme, using specific subsequences to identify target phenomena is often too specific and suffers from lower sensitivity when natural variations arise in the phenomena, measuring process, or data analysis. We introduce here a method for discovering signatures for phenomena that are well characterized by sequences of event primitives. In this paper, we describe the steps taken and lessons learned in generalizing a sequence analysis method, BLAST, for use on non-biological datasets including expressing and operating on alphabets of varying length, constructing a reward/penalty model for arbitrary datasets, and discovering low complexity segments in sequence data by extending BLAST’s native low-complexity estimating algorithms. We also present several case studies that demonstrate the utility of this method to discovering signatures in a wide array of applications including network traffic, software analysis, server characterization, and others. Finally, we demonstrate how signatures discovered using this method can be expressed using a variety of model formalisms, each having its own relative benefit.

Signature Science↗
DOE OSTI2018

Results from the Atacama B-mode Search (ABS) experiment

We report the Atacama B-mode Search is an experiment designed to measure the cosmic microwave background polarization at large angular scales (0ℓ>4). It observes at 145 GHz from a site at 5,190 m elevation in northern Chile. The noise equivalent polarization temperature, or NEQ, is 41 μK√s. One of the unique features of ABS is its use of a rapidly rotating ambient-temperature half-wave plate (HWP) {as the first optical element}. {The HWP spins} at 2.55 Hz to modulate the incident polarized signal at frequencies above where instrument white noise dominates over atmospheric fluctuations and other sources of low-frequency noise. We report here on the analysis of data from a 2,400 deg 2 region of sky. We perform a blind analysis to reduce potential bias. After unblinding, we find agreement with the Planck TE and EE measurements on the same region of sky, {with a derived calibration factor of 00.89 ± 0.1}. We marginally detect polarized dust emission {(at 3.2 σ for EE and 2.2 σ for BB)} and give an upper limit on the tensor-to-scalar ratio of r<2.3 (95% confidence level) with the equivalent of 100 on-sky days of observation. Finally, we also present a new measurement of the polarization of Tau A and introduce new methods for calibration and data analysis associated with HWP-based observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Online data analysis and reduction: An important co-design motif for extreme-scale computers

A growing disparity between supercomputer computation speeds and I/O rates means that it is rapidly becoming infeasible to analyze supercomputer application output only after that output has been written to a file system. Instead, data-generating applications must run concurrently with data reduction and/or analysis operations, with which they exchange information via high-speed methods such as interprocess communications. The resulting parallel computing motif, online data analysis and reduction (ODAR), has important implications for both application and HPC systems design. Here we introduce the ODAR motif and its co-design concerns, describe a co-design process for identifying and addressing those concerns, present tools that assist in the co-design process, and present case studies to illustrate the use of the process and tools in practical settings.

Data Analysis↗
DOE OSTI2022

Disaster risk and artificial intelligence: A framework to characterize conceptual synergies and future opportunities

Artificial intelligence (AI) methods have revolutionized and redefined the landscape of data analysis in business, healthcare, and technology. These methods have innovated the applied mathematics, computer science, and engineering fields and are showing considerable potential for risk science, especially in the disaster risk domain. The disaster risk field has yet to define itself as a necessary application domain for AI implementation by defining how to responsibly balance AI and disaster risk. (1) How is AI being used for disaster risk applications; and how are these applications addressing the principles and assumptions of risk science, (2) What are the benefits of AI being used for risk applications; and what are the benefits of applying risk principles and assumptions for AI-based applications, (3) What are the synergies between AI and risk science applications, and (4) What are the characteristics of effective use of fundamental risk principles and assumptions for AI-based applications? This study develops and disseminates an online survey questionnaire that leverages expertise from risk and AI professionals to identify the most important characteristics related to AI and risk, then presents a framework for gauging how AI and disaster risk can be balanced. This study is the first to develop a classification system for applying risk principles for AI-based applications. This classification contributes to understanding of AI and risk by exploring how AI can be used to manage risk, how AI methods introduce new or additional risk, and whether fundamental risk principles and assumptions are sufficient for AI-based applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

Purpose: A previously proposed method to reduce radiation dose to patient in dynamic contrast-enhanced (DCE) CT is enhanced by principal component analysis (PCA) filtering which improves the signal-to-noise ratio (SNR) of time-concentration curves in the DCE-CT study. The efficacy of the combined method to maintain the accuracy of kinetic parameter estimates at low temporal resolution is investigated with pixel-by-pixel kinetic analysis of DCE-CT data. Methods: The method is based on DCE-CT scanning performed with low temporal resolution to reduce the radiation dose to the patient. The arterial input function (AIF) with high temporal resolution can be generated with a coarsely sampled AIF through a previously published method of AIF estimation. To increase the SNR of time-concentration curves (tissue curves), first, a region-of-interest is segmented into squares composed of 3 × 3 pixels in size. Subsequently, the PCA filtering combined with a fraction of residual information criterion is applied to all the segmented squares for further improvement of their SNRs. The proposed method was applied to each DCE-CT data set of a cohort of 14 patients at varying levels of down-sampling. The kinetic analyses using the modified Tofts’ model and singular value decomposition method, then, were carried out for each of the down-sampling schemes between the intervals from 2 to 15 s. The results were compared with analyses done with the measured data in high temporal resolution (i.e., original scanning frequency) as the reference. Results: The patients’ AIFs were estimated to high accuracy based on the 11 orthonormal bases of arterial impulse responses established in the previous paper. In addition, noise in the images was effectively reduced by using five principal components of the tissue curves for filtering. Kinetic analyses using the proposed method showed superior results compared to those with down-sampling alone; they were able to maintain the accuracy in the quantitative histogram parameters of volume transfer constant [standard deviation (SD), 98th percentile, and range], rate constant (SD), blood volume fraction (mean, SD, 98th percentile, and range), and blood flow (mean, SD, median, 98th percentile, and range) for sampling intervals between 10 and 15 s. Conclusions: The proposed method of PCA filtering combined with the AIF estimation technique allows low frequency scanning for DCE-CT study to reduce patient radiation dose. The results indicate that the method is useful in pixel-by-pixel kinetic analysis of DCE-CT data for patients with cervical cancer.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Simulations of a PSD Plastic Neutron Collar for Assaying Fresh Fuel

The potential performance of a notional active coincidence collar for assaying uranium fuel based on segmented detectors constructed from the new PSD plastic fast organic scintillator with pulse shape discrimination capability was investigated in simulation. Like the International Atomic Energy Agency's present Uranium Neutron Collar for LEU (UNCL), the PSD plastic collar would also function by stimulating fission in the 235 U content of the fuel with a moderated 241 Am/Li neutron source and detecting instances of induced fission via neutron coincidence counting. In contrast to the moderated detectors of the UNCL, the fast time scale of detection in the scintillator eliminates statistical errors due to accidental coincidences that limit the performance of the UNCL. However, the potential to detect a single neutron multiple times historically has been one of the properties of organic scintillator detectors that has prevented their adoption for international safeguards applications. Consequently, as part of the analysis of simulated data, a method was developed by which true neutron-neutron coincidences can be distinguished from inter-detector scatter that takes advantage of the position and timing resolution of segmented detectors. Then, the performance of the notional simulated coincidence collar was evaluated for assaying a variety of fresh fuels, including some containing burnable poisons and partial defects. In these simulations, particular attention was paid to the analysis of fast mode measurements. In fast mode, a Cd liner is placed inside the collar to shield the fuel from the interrogating source and detector moderators, thereby eliminating the thermal neutron flux that is most sensitive to the presence of burnable poisons that are ubiquitous in modern nuclear fuels. The simulations indicate that the predicted precision of fast mode measurements is similar to what can be achieved by the present UNCL in thermal mode. For example, the statistical accuracy of a ten-minute measurement of fission coincidences collected in fast mode will be approximately 1% for most fuels of interest, yielding a ~1.4% error after subtraction of a five minute measurement of the spontaneous fissions from 238 U in the fuel, a ~2% error in analyzed linear density after accounting for the slope of the calibration curve, and a ~2.9% total error after addition of an assumed systematic error of 2%.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Archetype-based Redshift Estimation for the Dark Energy Spectroscopic Instrument Survey

We present a computationally efficient galaxy archetype-based redshift estimation and spectral classification method for the Dark Energy Survey Instrument (DESI) survey. The DESI survey currently relies on a redshift fitter and spectral classifier using a linear combination of principal component analysis–derived templates, which is very efficient in processing large volumes of DESI spectra within a short time frame. However, this method occasionally yields unphysical model fits for galaxies and fails to adequately absorb calibration errors that may still be occasionally visible in the reduced spectra. Our proposed approach improves upon this existing method by refitting the spectra with carefully generated physical galaxy archetypes combined with additional terms designed to absorb data reduction defects and provide more physical models to the DESI spectra. We test our method on an extensive data set derived from the survey validation (SV) and Year 1 (Y1) data of DESI. Our findings indicate that the new method delivers marginally better redshift success for SV tiles while reducing catastrophic redshift failure by 10%–30%. At the same time, results from millions of targets from the main survey show that our model has relatively higher redshift success and purity rates (0.5%–0.8% higher) for galaxy targets while having similar success for QSOs. These improvements also demonstrate that the main DESI redshift pipeline is generally robust. Additionally, it reduces the false-positive redshift estimation by 5%–40% for sky fibers. We also discuss the generic nature of our method and how it can be extended to other large spectroscopic surveys, along with possible future improvements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

AUTOMATIC CLASSIFICATION OF VARIABLE STARS IN CATALOGS WITH MISSING DATA

We present an automatic classification method for astronomical catalogs with missing data. We use Bayesian networks and a probabilistic graphical model that allows us to perform inference to predict missing values given observed data and dependency relationships between variables. To learn a Bayesian network from incomplete data, we use an iterative algorithm that utilizes sampling methods and expectation maximization to estimate the distributions and probabilistic dependencies of variables from data with missing values. To test our model, we use three catalogs with missing data (SAGE, Two Micron All Sky Survey, and UBVI) and one complete catalog (MACHO). We examine how classification accuracy changes when information from missing data catalogs is included, how our method compares to traditional missing data approaches, and at what computational cost. Integrating these catalogs with missing data, we find that classification of variable objects improves by a few percent and by 15% for quasar detection while keeping the computational cost the same.

79 ASTRONOMY AND ASTROPHYSICS↗