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At least 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The HST See Change Program. I. Survey Design, Pipeline, and Supernova Discoveries*

The See Change survey was designed to make z > 1 cosmological measurements by efficiently discovering high-redshift Type Ia supernovae (SNe Ia) and improving cluster mass measurements through weak lensing. This survey observed twelve galaxy clusters with the Hubble Space Telescope (HST) spanning the redshift range z = 1.13–1.75, discovering 57 likely transients and 27 likely SNe Ia at z ~ 0.8–2.3. As in similar previous surveys, this proved to be a highly efficient use of HST for supernova observations; the See Change survey additionally tested the feasibility of maintaining, or further increasing, the efficiency at yet higher redshifts, where we have less detailed information on the expected cluster masses and star formation rates. Furthermore, we find that the resulting number of SNe Ia per orbit is a factor of ~8 higher than for a field search, and 45% of our orbits contained an active SN Ia within 22 rest-frame days of peak, with one of the clusters by itself yielding 6 of the SNe Ia. We present the survey design, pipeline, and supernova discoveries. Novel features include fully blinded supernova searches, the first random forest candidate classifier for undersampled IR data (with a 50% detection threshold within 0.05 mag of human searchers), real-time forward-modeling photometry of candidates, and semi-automated photometric classifications and follow-up forecasts. We also describe the spectroscopic follow-up, instrumental in measuring host galaxy redshifts. The cosmology analysis of our sample will be presented in a companion paper.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2005

Dynamic Harris current sheet thickness from Cluster current density and plasma measurements

We use the first accurate measurements of current densities in the plasma sheet to calculate the half-thickness and position of the current sheet as a function of time. Our technique assumes a Harris current sheet model, which is parameterized by lobe magnetic field B(o), current sheet half-thickness h, and current sheet position z(sub o). Cluster measurements of magnetic field, current density, and plasma pressure are used to infer the three parameters as a function of time. We find that most long timescale (6-12 hours) current sheet crossings observed by Cluster cannot be described by a static Harris current sheet with a single set of parameters B(sub o), h, and z(sub o). Noting the presence of high-frequency fluctuations that appear to be superimposed on lower frequency variations, we average over running 6-min intervals and use the smoothed data to infer the parameters h(t) and z(sub o)(t), constrained by the pressure balance lobe magnetic field B(sub o)(t). Whereas this approach has been used in previous studies, the spatial gnuhen& now provided by the Cluster magnetometers were unavailable or not well constrained in earlier studies. We place the calculated hdf&cknessa in a magnetospheric context by examining the change in thickness with substorm phase for three case study events and 21 events in a superposed epoch analysis. We find that the inferred half-thickness in many cases reflects the nominal changes experienced by the plasma sheet during substorms (i.e., thinning during growth phase, thickening following substorm onset). We conclude with an analysis of the relative contribution of (Delta)B(sub z)/(Delta)X to the cross-tail current density during substorms. We find that (Delta)B(sub z)/(Delta)X can contribute a significant portion of the cross-tail c m n t around substorm onset.

Thompson, S. M.↗
DOE OSTI2023

Leveraging the digital thread for physics-based prediction of microstructure heterogeneity in additively manufactured parts

A major limitation of additive manufacturing (AM) processes is that local conditions of material deposition frequently lead to unintentional heterogeneities in microstructure and properties within a single component, despite nominally uniform process conditions. Up to now, there has been no way to a priori determine the distribution of these heterogeneities, requiring expensive trial-and-error approaches to fabrication, testing, and characterization. Here, a physics-based framework for creating a digital representation of the laser powder bed fusion (PBF) process is proposed to predict the variation in solidification behavior that leads to heterogeneous microstructures in an as-built part. By leveraging in situ process data stored in the part’s digital thread, the scan path and process parameters were input into a heat transfer model which predicted solidification data at the melt pool scale. A two-step unsupervised clustering algorithm was used to first cluster the local solidification conditions (12.5µm 3 voxels) and then to cluster the regional behavior on the scale of multiple scan passes and print layers (250µm 3 super-voxels). This process was used to identify regions with similar solidification characteristics for multiple locations in a Stainless Steel 316-L component. The corresponding as-built part was sectioned and characterized using electron backscatter diffraction (EBSD). Quantitative analysis of the pole figures confirmed that the predicted regions of heterogeneity in the solidification conditions corresponded with differences in the observed microstructure. In conclusion, this work shows a viable path for estimating the microstructural heterogeneity for additively manufactured parts to either limit microstructural variation throughout a part or to enable functionality-based variation of the microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Chespa: Streamlining Expansive Chemical Space Evaluation of Molecular Sets

Thousands of chemical properties can be calculated for small molecules, which can be used to place the molecules within the context of a broader “chemical space.” These definitions vary based on compounds of interest and the goals for the given chemical space definition. Here, we introduce a customizable (i.e., modular) Python module, chespa, built to easily assess different chemical space definitions through cluster-ing of compounds in these spaces and visualize trends of these clusters. To demonstrate this, chespa currently streamlines prediction of vari-ous molecule descriptors (predicted chemical properties, molecular substructures, AI-based chemical space, and chemical class ontology) in order to test 6 different chemical space definitions. Furthermore, we investigated how these varying definitions trend with mass spectrometry (MS)-based observability, i.e., the ability of a molecule to be observed with MS (e.g., as a function of the molecule ionizability), using an example data set from the U.S. EPA's Non-Targeted Analysis Collaborative Trial (ENTACT), where blinded samples had been analyzed previously, providing 1,398 data points. Improved understanding of observability would offer many advantages in small molecule identifica-tion, such as (i) a priori selection of experimental conditions based on suspected sample composition, (ii) the ability to reduce the number of candidate structures during compound identification by removing those less likely to ionize, and, in turn, (iii) a reduced false discovery rate and increased confidence in identifications. Factors controlling observability are not fully understood, making prediction of this property non-trivial and a prime candidate for chemical space analysis. Chespa is available at github.com/pnnl/chespa.

Nunez, Jamie↗
NASA NTRS1971

Detailed Skylab ECS consumables analysis for the interim revision flight plan (November, 1972, SL-1 launch)

The consumables analysis was performed for the Skylab 2, 3, and 4 Preliminary Reference Interim Revision Flight Plan. The analysis and the results are based on the mission requirements as specified in the flight plan and on other available data. The results indicate that the consumables requirements for the Skylab missions allow for remaining margins (percent) of oxygen, nitrogen, and water nominal as follows: 83.5, 90.8, and 88.7 for mission SL-2; 57.1, 64.1, and 67.3 for SL-3; and 30.8, 44.3, and 46.5 for SL-4. Performance of experiment M509 as scheduled in the flight plan results in venting overboard the cluster atmosphere. This is due to the addition of nitrogen for propulsion and to the additional oxygen introduced into the cabin when the experiment is performed with the crewman suited.

Wells, C.↗
NASA NTRS1995

A long-term space astrophysics research program: An x-ray perspective of the components and structure of galaxies

X-ray studies of galaxies by the Smithsonian Astrophysical Observatory (SAO) and MIT are described. Activities at SAO include ROSAT PSPC x-ray data reduction and analysis pipeline; x-ray sources in nearby Sc galaxies; optical, x-ray, and radio study of ongoing galactic merger; a radio, far infrared, optical, and x-ray study of the Sc galaxy NGC247; and a multiparametric analysis of the Einstein sample of early-type galaxies. Activities at MIT included continued analysis of observations with ROSAT and ASCA, and continued development of new approaches to spectral analysis with ASCA and AXAF. Also, a new method for characterizing structure in galactic clusters was developed and applied to ROSAT images of a large sample of clusters. An appendix contains preprints generated by the research.

Fabbiano, G.↗
DOE OSTI2021

ResNet and CycleGAN for pulse shape discrimination of He-4 detector pulses: Recovering pulses conventional algorithms fail to label unanimously

Pulse shape discrimination (PSD) capable detectors, such as He-4, that respond to neutron and gamma-ray 7 interactions have a threshold deposited energy value below which n/γ discrimination vanishes when using 8 conventional PSD algorithms. Recent attempts in applying supervised learning based artificial neural 9 networks for PSD use the pulses in the separated regions to train the networks so they can be used to classify 10 another set of separated pulses. In doing so, pulses previously indistinguishable are not recovered for 11 classification, which would have increased the number of neutron and gamma-ray pulses that could be used 12 for further analysis. Assuming the reason why conventional PSD algorithms have unseparated regions is 13 because the parameter space of the algorithms fail to capture the intrinsic (but subtle) distinguishing 14 behavior of some of the neutron and gamma-ray pulses, a cycle-consistent generative adversarial network 15 (CycleGAN) was trained to amplify those differences and extract well separated neutron and gamma-ray 16 clusters. Results show that, once the network is trained with pulses from separated and unseparated regions, 17 it was able to transform the pulses in the unseparated region to improve the PSD. Subsequent n/γ 18 classification was performed using deep residual network (ResNet) that takes pulses with 512 data points 19 as an input. Two different ResNets were explored – simple ResNet and modified ResNet which takes 20 segmented pulse inputs in the first layer and the corresponding time axis values in the last hidden layer. 21 The later approach enables the network to extract time correlated pulse features to enhance its ability to 22 capture the pulse behaviors relevant for PSD. Although it achieves slightly lower accuracy, 99.41% versus 23 99.89%, based on simply counting the number of correct n/γ labels assigned, compared to the simple 24 ResNet, the modified ResNets architecture was able to decreases the cross-entropy loss function by half, 25 which implies that the correct n/γ labels assigned are less likely to be accidental. PSD parameter 26 distributions based on n/γ classification by ResNet before and after transforming unseparated pulses using 27 CycleGAN show that by enhancing the separation between neutrons and gamma-rays, the transformation 28 helps improve the performance of classifier networks that are trained using labeled dataset. The 29 enhancement of neutron and gamma-ray separation by the CycleGAN increased the PSD figure of merit 30 (FOM) by up to 70% in some regions. Here, the results show that, if a given detector achieves clear separation 31 between neutron and gamma-ray pulses in any energy region, such neural network approaches can help 32 lower the energy threshold for the separation and increasing the number of neutron and gamma-ray pulses 33 that can be used for further analysis.

4He↗
DOE OSTI2024

Real‐time XFEL data analysis at SLAC and NERSC: A trial run of nascent exascale experimental data analysis

X‐ray scattering experiments using free electron lasers (XFELs) are a powerful tool to determine the molecular structure and function of unknown samples (such as COVID‐19 viral proteins). XFEL experiments are a challenge to computing in two ways: (i) due to the high cost of running XFELs, a fast turnaround time from data acquisition to data analysis is essential to make informed decisions on experimental protocols; (ii) data‐collection rates are growing exponentially, requiring new scalable algorithms. Here we report our experiences analyzing data from two experiments at the Linac Coherent Light Source (LCLS) during September 2020. Raw data were analyzed on NERSC's Cori XC40 system, using the Superfacility paradigm: our workflow automatically moves raw data between LCLS and NERSC, where it is analyzed using the software package CCTBX. We achieved real time data analysis with a turnaround time from data acquisition to full molecular reconstruction in as little as 10 min—sufficient time for the experiment's operators to make informed decisions. By hosting the data analysis on Cori, and by automating LCLS‐NERSC interoperability, we achieved a data analysis rate which matches the data acquisition rate. Completing data analysis within 10 min is a first for XFEL experiments and an important milestone if we are to keep up with data‐collection trends.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Electroluminescence Analysis and Grading of Hail Damaged Solar Panels

We analyzed more than 4000 electroluminescence images of hail damaged solar panels from a cluster of houses in Texas. We enhanced the images for ease of analysis and classified the defects within each solar cell into categories of glass breakage, installer damage, inactive substrings, crack severity, interconnect wire problems, and whether the damage was likely caused by hail. From these statistics, we quantified each panel into five levels of hail damage for insurance claims, and four levels of overall quality for potential resale pricing. Here, we share here some statistics regarding the defects with the hope that the data is useful to others attempting to predict the invisible damage to systems based just on the easily observable glass breakage statistics.

14 SOLAR ENERGY↗
DOE OSTI2025

Constraining primordial non-Gaussianity with DESI 2024 LRG and QSO samples

We analyse the large-scale clustering of the Luminous Red Galaxy (LRG) and Quasar (QSO) sample from the first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI). In particular, we constrain the primordial non-Gaussianity (PNG) parameter f NL loc via the large-scale scale-dependent bias in the power spectrum using 1,631,716 LRGs (0.6 < z < 1.1) and 1,189,129 QSOs (0.8 < z < 3.1). This new measurement takes advantage of the enormous statistical power at large scales of DESI DR1 data, surpassing the latest data release (DR16) of the extended Baryon Oscillation Spectroscopic Survey (eBOSS). For the first time in this kind of analysis, we use a blinding procedure to mitigate the risk of confirmation bias in our results. We improve the model of the radial integral constraint proposing an innovative technique allowing the correction through the window matrix convolution. We also carefully test the mitigation of the dependence of the target selection on the photometry qualities by incorporating an angular integral constraint contribution to the window function, and validate our methodology with the blinded data. Finally, combining the two samples, we measure f NL loc = -3.6 -9.1 +9.0 at 68% confidence, where we assume the universality relation for the LRG sample and a recent merger model for the QSO sample about the response of bias to primordial non-Gaussianity. Adopting the universality relation for the PNG bias in the QSO analysis leads to f NL loc = 3.5 -7.4 +10.7 at 68% confidence. Due to restricted selection in the LRG sample, the inclusion of the LRGs allows for 10% improvement. This measurement is the most precise determination of primordial non-Gaussianity using large-scale structure to date, surpassing the latest result from eBOSS by a factor of 2.3.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Enhancing Cluster Identification in Atom Probe Tomography Data Using Transfer Learning

Atom probe tomography (APT) has enabled the direct visualization of solute clusters, providing valuable insights into material structures. This clustering is crucial for understanding the nanoscale composition and behavior of materials, which can significantly influence their mechanical and physical properties. However, the widely used clustering methods in the APT community face challenges such as subjective parametric selection and limited applicability, particularly in dealing with overlapping clusters, nested clusters, and artifacts across different scales, such as precipitates and dislocations. To address these challenges, we present a framework based on density-based cluster analysis that aims to be less dependent on user input, reproducible, and robust.

Density-based clustering↗
DOE OSTI2025

Low-lying states and total internal partition sums of CH

The electronic structure and spin-orbit states of the CH radical have been systematically investigated using multi-reference configuration interaction (MRCI) and single-reference coupled-cluster (CC) methods. These calculations were performed in conjunction with large correlation-consistent basis sets of quadruple-, quintuple-, and sextuple-ζ quality. To achieve high accuracy, electronic energies for all states were extrapolated to the complete basis set (CBS) limit, enabling the detailed construction of potential energy curves and determination of reliable spectroscopic constants. Spin-orbit coupling effects were explicitly incorporated, and vibrational energy levels were computed via Numerov analysis. Furthermore, the resulting values exhibit good to excellent agreement with available experimental data. Dipole moment and transition dipole moment curves were evaluated to assess the opacity characteristics of CH, revealing that transitions such as Χ 2 Π (u′′ = 0) → Α 2 Δ (u′ = 0), Χ 2 Π (u′′ = 0) → Β 2 Σ − (u′ = 0), Χ 2 Π (u′′ = 0) → C 2 Σ + (u′ = 0), and Χ 2 Π (u′′ = 0) → D 2 Σ + (u′ = 3) are particularly probable. Finally, the total internal partition function sum (TIPS) of CH was computed over a broad temperature range (10–30,000 K) based on our high-accuracy ab initio results.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Predicting Mechanical Properties from Microstructure Images in Fiber-Reinforced Polymers Using Convolutional Neural Networks

Evaluating the mechanical response of fiber-reinforced composites can be extremely time-consuming and expensive. Machine learning (ML) techniques offer a means for faster predictions via models trained on existing input–output pairs and have exhibited success in composite research. This paper explores a fully convolutional neural network modified from StressNet, which was originally used for linear elastic materials, and extended here for a non-linear finite element (FE) simulation to predict the stress field in 2D slices of segmented tomography images of a fiber-reinforced polymer specimen. The network was trained and evaluated on data generated from the FE simulations of the exact microstructure. The testing results show that the trained network accurately captures the characteristics of the stress distribution, especially on fibers, solely from the segmented microstructure images. The trained model can make predictions within seconds in a single forward pass on an ordinary laptop, given the input microstructure, compared to 92.5 h to run the full FE simulation on a high-performance computing cluster. These results show promise in using ML techniques to conduct fast structural analysis for fiber-reinforced composites and suggest a corollary that the trained model can be used to identify the location of potential damage sites in fiber-reinforced polymers.

Sun, Yixuan (ORCID:0000000311093380)↗
DOE OSTI2026

Automated Classification of Vehicle Movements at Signalized Intersections Using Vehicle Trajectories

Accurate vehicle movement classification through signalized intersections is of paramount importance to the analysis of intersection performance and the optimization of traffic control strategies. Conventional techniques for tracking vehicle turning movements depend on infrastructure-based strategies like human counts, loop detectors, and video analytics, all of which are costly, prone to errors, and spatially constrained. High-frequency trajectory data can be utilized to determine vehicle movement patterns in a scalable and infrastructure-independent method due to the adoption of connected vehicles (CVs). In recent years, several studies have utilized connected vehicle data to generate performance measures. Most of the trajectory-based performance measures approaches, however, require map matching-i.e., extracting geospatial references from maps to identify the movements that individual vehicles make at a signalized intersection. These approaches are often time-consuming and hinder scalability since geographic features need to be provided for an analysis to be conducted. Map matching methods are prone to errors as different map versions change these geographic features. This research presents a novel automatic classification pipeline that uses CV trajectory data to classify vehicle movements at signalized crossings, specifically pass-through left-turn and right-turn maneuvers. The process starts by filtering trips that cross a spatial bounding box that has been defined at the target intersection. Approach and departure headings for each trajectory crossing the boundary are computed and are clustered together to identify dominant movements. The proposed algorithm is used to classify the movement of vehicles at 10 intersections in the state of California, and the results indicate that the algorithm can classify movements at these intersections with varying traffic volumes and road network configurations, all in a map-less framework with no need for conflation of vehicle trajectories to a digital base map.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2001

From the Cluster Temperature Function to the Mass Function at Low Z

The project consists of three X-ray Multimirror Mission (XMM) observations of the hot, nearby galaxy cluster Triangulum Australis (one pointing to the cluster center and two offsets). The major goal of the project is to obtain an accurate radial gas temperature profile out to large radii and derive the total gravitating mass within the radius of over density 500. A secondary goal is to obtain a detailed two-dimensional gas temperature map of the central region. We have recently received the central pointing (of the two CDROMs received, one contains the central pointing and the other, a zero-exposure dummy observation). The analysis of the data at hand is underway with the aim of deriving the two-dimensional temperature map. At present, we have to limit quantitative analysis to the EPIC-MOS data because of the absence of the necessary calibration products for the Extended Full Frame mode used for EPIC-pn in these observations. We anticipate the arrival of the data from the remaining two offset pointings in the near future, after which we will be able to undertake the total mass derivation. Money from this year's installment of the grant was partially spent on an upgrade of the computer used for the above data analysis, and on travel to a conference that extensively discussed the XMM calibration issues.

Markevitch, Maxim↗
DOE OSTI2021

Inclusive breakup cross sections in reactions induced by the nuclides 6 He and 6,7 Li in the two-body cluster model

Calculations of inclusive α particle production are performed to interpret reaction experimental data induced by the two-neutron halo 6He and the isotopes 6,7 Li on different target nuclei. In this work, we have implemented the zero-range post-form DWBA to compute the elastic and nonelastic breakup cross sections. Integrated cross sections, angular distributions and spectra are presented. Projectiles are approximated as two-body clusters, namely 6 He as α+dineutron and 6 Li ( 7 Li) are interpreted as α+deuteron(triton). The São Paulo optical potential is employed in the calculation of the distorted wave functions for the incident channels, while standard phenomenological interactions are taken for the fragment-target interactions. Calculations for 6 Li and 7 Li furnish a good description of the data, while the reaction involving 6 He is found to be more complicated, where interpretation of the data must still be considered incomplete. The present analysis identifies an overall large contribution from inclusive breakup emission for all the cases studied.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1979

HEAO 1 observations of 4U1232+07

An analysis of the NRL HEAO 1 observations of the Virgo cluster and 3C 273 has revealed X-ray emission from a region between these two objects. The most satisfactory explanation is the presence of two weak X-ray sources, for which error boxes have been obtained. One source is likely to be the peculiar galaxy IC 3576, suggested by Margon et al. (1972) as the source 2U 1231+07=4U 1232+07. The error box resulting from combining the present observations with the 4U data strengthen the X-ray identification with this extremely interesting galaxy. There are numerous possibilities for the other X-ray source, including the interacting pair of galaxies NGC 4410a and NGC 4410b (NGC 4410a/b).

Bhavsar, S. P.↗