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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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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

The Chandra Large Area Synoptic X-ray Survey (CLASXS) of the Lockman Hole-Northwest: The X-ray Catalog

We present the X-ray catalog and basic results from our Chandra Large Area Synoptic X-ray Survey (CLASXS) of the Lockman Hole-Northwest field. Our 9 ACIS-I fields cover a contiguous solid angle of approx. 0.4 sq deg and reach fluxes of 5 x 10(exp -16) ergs/sq cm/s (0.4-2 keV) and 3 x 10(exp -15) ergs/sq cm/s (2-8 keV). Our survey bridges the gap between ultradeep pencil-beam surveys, such as the Chandra Deep Fields (CDFs), and shallower, large area surveys, allowing a better probe of the X-ray sources that contribute most of the 2-10 keV cosmic X-ray background (CXB). We find a total of 525 X-ray point sources and 4 extended sources. At approx. 10(exp -14)ergs/sq cm/s (2-8 keV), our number counts are significantly higher than those of several non-contiguous, large area surveys. Such a large difference is an indication of clustering in the X-ray sources. On the other hand, the integrated flux from the CLASXS field, combined with ASCA and Chandra ultradeep surveys, is consistent with results from other large area surveys, within the variance of the CXB. We see spectral evolution in the hardening of the sources at fluxes below 10(exp -14) ergs/sq cm/s, which agrees with previous observations from Chandra and XMM-Newton. About 1/3 of the sources in the CLASXS field have multiple observations, allowing variability tests. Above 4 x 10(exp -14) ergs/sq cm/s (0.4-8 keV), approx. 61% of the sources are variable. We also investigated the spectral variability of the variable sources. While most show spectral softening with increasing flux, or no significant spectral change, there are a few sources that show a different trend. Four extended sources in CLASXS is consistent with the previously measured LogN-LogS of galaxy clusters. Using X-ray spectra and optical colors, we argue that 3 of the 4 extended sources are galaxy clusters or galaxy groups. We report the discovery of a gravitational lensing arc associated with one of these sources. Using red sequence and brightest cluster galaxy methods, we find that the redshifts of the extended sources are in the range z approx. 0.5 - 1. The inferred masses within the Einstein radii are consistent with the mass profiles of local groups scaled to the same virial radii.

Yang, Y.↗
DOE OSTI2015

Leptonic Decays of Charged Pseudoscalar Mesons - 2015

We review the physics of purely leptonic decays of $\pi^\pm$, $K^\pm$, $D^{\pm}$, $D_s^\pm$, and $B^\pm$ pseudoscalar mesons. The measured decay rates are related to the product of the relevant weak-interaction-based CKM matrix element of the constituent quarks and a strong interaction parameter related to the overlap of the quark and antiquark wave-functions in the meson, called the decay constant $f_P$. The leptonic decay constants for $\pi^\pm$, $K^\pm$, $D^{\pm}$, $D_s^\pm$, and $B^\pm$ mesons can be obtained with controlled theoretical uncertainties and high precision from {\it ab initio} lattice-QCD simulations. The combination of experimental leptonic decay-rate measurements and theoretical decay-constant calculations enables the determination of several elements of the CKM matrix within the standard model. These determinations are competitive with those obtained from semileptonic decays, and also complementary because they are sensitive to different quark flavor-changing currents. They can also be used to test the unitarity of the first and second rows of the CKM matrix. Conversely, taking the CKM elements predicted by unitarity, one can infer "experimental" values for $f_P$ that can be compared with theory. These provide tests of lattice-QCD methods, provided new-physics contributions to leptonic decays are negligible at the current level of precision. This review is the basis of the article in the Particle Data Group's 2016 edition, updating the versions in Refs. [1-3].

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Do graph neural networks learn traditional jet substructure?

At the CERN LHC, the task of jet tagging, whose goal is to infer the origin of a jet given a set of final-state particles, is dominated by machine learning methods. Graph neural networks have been used to address this task by treating jets as point clouds with underlying, learnable, edge connections between the particles inside. We explore the decision-making process for one such state-of-the-art network, ParticleNet, by looking for relevant edge connections identified using the layerwise-relevance propagation technique. As the model is trained, we observe changes in the distribution of relevant edges connecting different intermediate clusters of particles, known as subjets. The resulting distribution of subjet connections is different for signal jets originating from top quarks, whose subjets typically correspond to its three decay products, and background jets originating from lighter quarks and gluons. This behavior indicates that the model is using traditional jet substructure observables, such as the number of prongs -- energetic particle clusters -- within a jet, when identifying jets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2013

Note: In situ measurement of vacuum window birefringence by atomic spectroscopy

We present an in situ method to measure the birefringence of a single vacuum window by means of microwave spectroscopy on an ensemble of cold atoms. Stress-induced birefringence can cause an ellipticity in the polarization of an initially linearly polarized laser beam. The amount of ellipticity can be reconstructed by measuring the differential vector light shift of an atomic hyperfine transition. Measuring the ellipticity as a function of the linear polarization angle allows us to infer the amount of birefringence Δn at the level of 10{sup −8} and identify the orientation of the optical axes. The key benefit of this method is the ability to separately characterize each vacuum window, allowing the birefringence to be precisely compensated in existing vacuum apparatuses.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2015

Hazard Assessment from Storm Tides and Rainfall on a Tidal River Estuary

Here, we report on methods and results for a model-based flood hazard assessment we have conducted for the Hudson River from New York City to Troy/Albany at the head of tide. Our recent work showed that neglecting freshwater flows leads to underestimation of peak water levels at up-river sites and neglecting stratification (typical with two-dimensional modeling) leads to underestimation all along the Hudson. As a result, we use a three-dimensional hydrodynamic model and merge streamflows and storm tides from tropical and extratropical cyclones (TCs, ETCs), as well as wet extratropical cyclone (WETC) floods (e.g. freshets, rain-on-snow events). We validate the modeled flood levels and quantify error with comparisons to 76 historical events. A Bayesian statistical method is developed for tropical cyclone streamflows using historical data and consisting in the evaluation of (1) the peak discharge and its pdf as a function of TC characteristics, and (2) the temporal trend of the hydrograph as a function of temporal evolution of the cyclone track, its intensity and the response characteristics of the specific basin. A k-nearest-neighbors method is employed to determine the hydrograph shape. Out of sample validation tests demonstrate the effectiveness of the method. Thus, the combined effects of storm surge and runoff produced by tropical cyclones hitting the New York area can be included in flood hazard assessment. Results for the upper Hudson (Albany) suggest a dominance of WETCs, for the lower Hudson (at New York Harbor) a case where ETCs are dominant for shorter return periods and TCs are more important for longer return periods (over 150 years), and for the middle-Hudson (Poughkeepsie) a mix of all three flood events types is important. However, a possible low-bias for TC flood levels is inferred from a lower importance in the assessment results, versus historical event top-20 lists, and this will be further evaluated as these preliminary methods and results are finalized. Future funded work will quantify the influences of sea level rise and flood adaptation plans (e.g. surge barriers). It would also be valuable to examine how streamflows from tropical cyclones and wet cool-season storms will change, as this factor will dominate at upriver locations.

Hazard assessment↗
DOE OSTI2023

Stand validation of lidar forest inventory modeling for a managed southern pine forest

We evaluated area-based approaches (ABAs) to light detection and ranging (lidar) predictions of plot- and stand-level forest attributes (tree count, height, basal area, volume, aboveground biomass, broadleaf/conifer, and diameter at breast height — “diameter”). ABA methods included post-stratification (PS), ordinary least squares (OLSs) regression, k nearest neighbors ( kNN), and random forest (RF). This study was conducted on the Savannah River Site in South Carolina, USA. Plot- and stand-level predictions were validated against fixed-radius 0.04 ha (0.1 acre) plots in 49 ≈2.0 ha (5 acre) stands. Our findings demonstrate that lidar can be incorporated operationally into forest inventory systems to provide stand-level inferences for a wide range of forest attributes. Volume predictions for specific diameter classes, however, often fared poorly (root mean squared error (RMSE) > 100%) for the methods we explored, especially for larger (less common) diameter trees. Stand-level results were consistently better than pixel-level results (10–200+ percentage points). kNN and RF performed similarly and better than OLS and PS, but RF was the most robust to model configurations, while kNN has practical advantages such as simultaneous predictions of many attributes.

Forestry↗
DOE OSTI2018

Study of Photocatalytic Nano-Particle Effects on the Low Temperature Plasma Ionization Mass Spectrometry

Although the low temperature plasma mass spectrometry (LTP-MS) is widely used as an analysis tool for many biochemical samples, its application window is somehow limited to the analytes of low molecular mass and high volatility. For this reason, there have been attempts to enhance the ionization/desorption efficiencies with extra heating, for instance. In this study, another enhancement method was suggested using the photocatalytic nano-particles (NPs). In order to assess the NP effects on the LTP-MS, two fatty acid ethyl ester samples of ethyl myristate and ethyl palmitate were used, and the NP of titanium dioxide (TiO{sub 2}) was mainly employed. The results showed that the signal intensities of the LTP-MS were largely increased with the TiO{sub 2} addition for both samples. In addition, the cholesterol sample was analyzed using the TiO{sub 2} assisted LTP-MS, also resulting in the enhancement of the signal intensity. The overall results inferred that the photocatalytic NP confirmed its role as an effective assist tool for the LTP-MS, especially suitable because of the facile method and the heat-free nature. .

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

The cosmic waltz of Coma Berenices and Latyshev 2 (Group X)

Context. Open clusters (OCs) are fundamental benchmarks where theories of star formation and stellar evolution can be tested and validated. Coma Berenices (Coma Ber) and Latyshev 2 (Group X) are the second and third OCs closest to the Sun, making them excellent targets to search for low-mass stars and ultra-cool dwarfs. In addition, this pair will experience a flyby in 10–16 Myr, making it a benchmark to test pair interactions of OCs. Aims. We aim to analyse the membership, luminosity, mass, phase-space (i.e. positions and velocities), and energy distributions for Coma Ber and Latyshev 2 and test the hypothesis of the mixing of their populations at the encounter time. Methods. We developed a new phase-space membership methodology and applied it to Gaia data. With the recovered members, we inferred the phase-space, luminosity, and mass distributions using publicly available Bayesian inference codes. Then, with a publicly available orbit integration code and members’ positions and velocities, we integrated their orbits 20 Myr into the future. Results. In Coma Ber, we identified 302 candidate members distributed in the core and tidal tails. The tails are dynamically cold and asymmetrically populated. The stellar system called Group X is made of two structures: the disrupted OC Latyshev 2 (186 candidate members) and a loose stellar association called Mecayotl 1 (146 candidate members), and both of them will fly by Coma Ber in 11.3 ± 0.5 Myr and 14.0 ± 0.6 Myr, respectively, and each other in 8.1 ± 1.3 Myr. Conclusions. We study the dynamical properties of the core and tails of Coma Ber and also confirm the existence of the OC Latyshev 2 and its neighbour stellar association Mecayotl 1. Although these three systems will experience encounters, we find no evidence supporting the mixing of their populations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

DOME: Directional medical embedding vectors from Electronic Health Records

Motivation: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts. Methods: We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to correspondingly encode the pairwise prior and posterior dependencies between medical concepts. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts. Results: We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example achieving a relative gain of 5.5% in the area under the receiver operating characteristic (AUROC) for lung cancer. Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, correspondingly achieving relative AUROC gain over the state-of-the-art methods by 10.8% and 6.6%. Finally, DOME effectively constructs directional knowledge graphs, which distinguish disease risk factors from comorbidities, thereby revealing disease progression trajectories. The source codes are provided at https://github.com/celehs/Directional-EHRembedding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Flood hazard model calibration using multiresolution model output

Riverine floods pose a considerable risk to many communities. Improving flood hazard projections has the potential to inform the design and implementation of flood risk management strategies. Current flood hazard projections are uncertain, especially due to uncertain model parameters. Calibration methods use observations to quantify model parameter uncertainty. With limited computational resources, researchers typically calibrate models using either relatively few expensive model runs at high spatial resolutions or many cheaper runs at lower spatial resolutions. This leads to an open question: is it possible to effectively combine information from the high and low resolution model runs? We propose a Bayesian emulation–calibration approach that assimilates model outputs and observations at multiple resolutions. As a case study for a riverine community in Pennsylvania, we demonstrate our approach using the LISFLOOD-FP flood hazard model. Here, the multiresolution approach results in improved parameter inference over the single resolution approach in multiple scenarios. Results vary based on the parameter values and the number of available models runs. Our method is general and can be used to calibrate other high dimensional computer models to improve projections.

multiresolution↗
DOE OSTI2024

Neural lumped parameter differential equations with application in friction-stir processing

Lumped parameter methods aim to simplify the evolution of spatially-extended or continuous physical systems to that of a “lumped” element representative of the physical scales of the modeled system. For systems where the definition of a lumped element or its associated physics may be unknown, modeling tasks may be restricted to full-fidelity physics simulations. Here, in this work, we consider data-driven modeling tasks with limited point-wise measurements of otherwise continuous systems. We build upon the notion of the Universal Differential Equation (UDE) to construct data-driven models for reducing dynamics to that of a lumped parameter and inferring its properties. The flexibility of UDEs allow for composing various known physical priors suitable for application-specific modeling tasks, including lumped parameter methods. The motivating example for this work is the plunge and dwell stages for friction-stir welding; specifically, (i) mapping power input into the tool to a point-measurement of temperature and (ii) using this learned mapping for process control.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Quantitative radiography for determining density fluctuations in HED experiments

We have developed a method to extract density fluctuation measurements from x-ray radiographs of high-energy density (HED) instability growth and turbulence experiments. We use this information to calculate density fluctuation statistics for constraining the performance of turbulent mix models in HED systems. The density calculation combines image filtering, removal of systemic effects such as backlighter variation, calculation of transmission across multiple materials, and use of tracer materials to generate an approximate single-material density field. From the density map, we calculate both average density and a variance-like moment b (density-specific-volume covariance), which we compare to our models. We infer both quantities from a single image, which is significantly more information than the historic single scalar mix width measurements. We also develop a method of analyzing simulation outputs that incorporate both the density fluctuation metric from a turbulence model and the bulk material maps from the hydrodynamic code. This analysis helps address the question of how to initialize the simulations for best comparison to data from systems with large separations of scale in the mixing perturbation initial condition. We find that our data analysis method yields 1D average density and b curves with similar morphology and amplitudes as those from preliminary simulation comparisons.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Electron-beam energy reconstruction for neutrino oscillation measurements

Neutrinos exist in one of three types or ‘flavours’—electron, muon and tau neutrinos—and oscillate from one flavour to another when propagating through space. This phenomena is one of the few that cannot be described using the standard model of particle physics (reviewed in ref. 1), and so its experimental study can provide new insight into the nature of our Universe (reviewed in ref. 2). Neutrinos oscillate as a function of their propagation distance (L) divided by their energy (E). Therefore, experiments extract oscillation parameters by measuring their energy distribution at different locations. As accelerator-based oscillation experiments cannot directly measure E, the interpretation of these experiments relies heavily on phenomenological models of neutrino–nucleus interactions to infer E. Here we exploit the similarity of electron–nucleus and neutrino–nucleus interactions, and use electron scattering data with known beam energies to test energy reconstruction methods and interaction models. We find that even in simple interactions where no pions are detected, only a small fraction of events reconstruct to the correct incident energy. More importantly, widely used interaction models reproduce the reconstructed energy distribution only qualitatively and the quality of the reproduction varies strongly with beam energy. This shows both the need and the pathway to improve current models to meet the requirements of next-generation, high-precision experiments such as Hyper-Kamiokande (Japan) and DUNE (USA).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Fast inference of deep neural networks in FPGAs for particle physics

Here, recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the use of such techniques in low-latency, low-power FPGA (Field Programmable Gate Array) hardware has only just begun. FPGA-based trigger and data acquisition systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable, among many other physics scenarios, searches for new dark sector particles and novel measurements of the Higgs boson. While we focus on a specific example, the lessons are far-reaching. A companion compiler package for this work is developed based on High-Level Synthesis (HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to identify the problems in particle physics that would benefit from performing neural network inference with FPGAs. For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

The Simons Observatory: validation of reconstructed power spectra from simulated filtered maps for the small aperture telescope survey

We present a transfer function-based method to estimate angular power spectra from filtered maps for cosmic microwave background (CMB) surveys. This is especially relevant for experiments targeting the faint primordial gravitational wave signatures in CMB polarisation at large scales, such as the Simons Observatory (SO) small aperture telescopes. While timestreams can be filtered to mitigate the contamination from low-frequency noise, usual methods that calculate the mode coupling at individual multipoles can be challenging for experiments covering large sky areas or reaching few-arcminute resolution. The method we present here, although approximate, is more practical and faster for larger data volumes. We validate it through the use of simulated observations approximating the first year of SO data, going from half-wave plate-modulated timestreams to maps, and using simulations to estimate the mixing of polarisation modes induced by an example of time-domain filtering. We show its performance through an example null test and with an end-to-end pipeline that performs inference on cosmological parameters, including the tensor-to-scalar ratio r. The performance demonstration uses simulated observations at multiple frequency bands. We find that the method can recover unbiased parameters for our simulated noise levels.

CMBR experiments↗
NASA NTRS2009

Using GPS to Detect Imminent Tsunamis

A promising method of detecting imminent tsunamis and estimating their destructive potential involves the use of Global Positioning System (GPS) data in addition to seismic data. Application of the method is expected to increase the reliability of global tsunami-warning systems, making it possible to save lives while reducing the incidence of false alarms. Tsunamis kill people every year. The 2004 Indian Ocean tsunami killed about 230,000 people. The magnitude of an earthquake is not always a reliable indication of the destructive potential of a tsunami. The 2004 Indian Ocean quake generated a huge tsunami, while the 2005 Nias (Indonesia) quake did not, even though both were initially estimated to be of the similar magnitude. Between 2005 and 2007, five false tsunami alarms were issued worldwide. Such alarms result in negative societal and economic effects. GPS stations can detect ground motions of earthquakes in real time, as frequently as every few seconds. In the present method, the epicenter of an earthquake is located by use of data from seismometers, then data from coastal GPS stations near the epicenter are used to infer sea-floor displacements that precede a tsunami. The displacement data are used in conjunction with local topographical data and an advanced theory to quantify the destructive potential of a tsunami on a new tsunami scale, based on the GPS-derived tsunami energy, much like the Richter Scale used for earthquakes. An important element of the derivation of the advanced theory was recognition that horizontal sea-floor motions contribute much more to generation of tsunamis than previously believed. The method produces a reliable estimate of the destructive potential of a tsunami within minutes typically, well before the tsunami reaches coastal areas. The viability of the method was demonstrated in computational tests in which the method yielded accurate representations of three historical tsunamis for which well-documented ground-motion measurements were available. Development of a global tsunami-warning system utilizing an expanded network of coastal GPS stations was under consideration at the time of reporting the information for this article.

Song, Y. Tony↗
DOE OSTI2018

Detecting Weak Spectral Lines in Interferometric Data through Matched Filtering

Modern radio interferometers enable observations of spectral lines with unprecedented spatial resolution and sensitivity. In spite of these technical advances, many lines of interest are still at best weakly detected and therefore necessitate detection and analysis techniques specialized for the low signal-to-noise ratio (S/N) regime. Matched filters can leverage knowledge of the source structure and kinematics to increase sensitivity of spectral line observations. Application of the filter in the native Fourier domain improves S/N while simultaneously avoiding the computational cost and ambiguities associated with imaging, making matched filtering a fast and robust method for weak spectral line detection. We demonstrate how an approximate matched filter can be constructed from a previously observed line or from a model of the source, and we show how this filter can be used to robustly infer a detection significance for weak spectral lines. When applied to ALMA Cycle 2 observations of CH{sub 3}OH in the protoplanetary disk around TW Hya, the technique yields a ≈53% S/N boost over aperture-based spectral extraction methods, and we show that an even higher boost will be achieved for observations at higher spatial resolution. A Python-based open-source implementation of this technique is available under the MIT license at http://github.com/AstroChem/VISIBLE.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1985

Wind distances for planetary nebulae

A new method for the determination of distances to planetary nebulae is presented. If a star has a strong wind that produces a P Cygni profile in its spectrum, an escape velocity may be inferred from the terminal velocity, which provides a relation involving stellar temperature, luminosity, and mass. The theory of stellar evolution provides a second relation among these three variables, so that once the star's temperature is known, it is possible to simultaneously derive its mass and luminosity, and hence its distance. The method is applied to several test cases that illustrate its viability and current limitations.

Kaler, J. B.↗