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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 OSTI2026

Legacy Survey of Space and Time Data Preview 2: deep_coadd_input_summary dataset type

We present Rubin Data Preview 2 (DP2), the second data preview from the NDF-DOE Vera C. Rubin Observatory. Data Preview 2 (DP2) comprises coadds, detection catalogs, and ancillary data products; and when fully released will also include single-epoch images and difference images. DP2 is derived from observations acquired by the LSST Science Camera (LSSTCam) on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile, primarily during the on-sky commissioning campaign between 2025-04-16 and 2025-09-21, supplemented by observations taken between 2025-10-25 and 2026-01-06 that overlap the commissioning footprint. The DP2 footprint comprises the Science Validation wide-area survey, five Deep Drilling Fields, and a number of targeted small-field regions, including Trifid and Lagoon, Prawn, M49, and New Horizons, all observed as part of the Rubin First Look campaign. Each field was imaged in up to six broad photometric bands, ugrizy, and coadded to produce deep imaging covering an estimated 3,000 deg2. The addition of single-visit-only areas expands the total DP2 footprint to an estimated 15,000 deg2, with coverage in at least one filter. The median per-visit PSF FWHM across the wide-area survey ranges from 1.17 arcsec in the z band to 1.26 arcsec in g and r bands. The deepest field, reaches estimated coadded 5σ depths of u=26 mag, g=26.8 mag, r=26.3 mag, i=26.1 mag, z=25.3 mag, y=23.9 mag. Based on a roughly five-month primary observing baseline and covering only part of the eventual LSST footprint, DP2's area, depth, and multiband coverage nonetheless support a broad range of early science investigations ahead of LSST Data Release This dataset is a subset of the full data release consisting of the deep_coadd_input_summary dataset type. These are summary of the visit images that contribute to each deep coadd patch. This release contains 1 dataset of this type.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

UIR-Net: Object Detection in Infrared Imaging of Thermomechanical Processes in Automotive Manufacturing

Thermomechanical processes (TMPs) such as resistance spot welding (RSW) and hot stamping are widely used in automotive manufacturing. Recent advancement in sensing technology has led to an increasing adoption of thermographic cameras to capture the infrared (IR) radiation of a metal part (or component of a part) during its thermomechanical processing or immediately after the process when the part is still hot. Detecting the object(s) of interest from raw IR images is an essential step in analyzing these data. Deep learning (DL) has been a recent success for object detection (OD), but the application of DL-based OD for industrial IR images in manufacturing is largely lagging behind. The major contribution of this work, which is also the distinction from previous OD studies, is the capability of building the OD model with unlabeled IR images, i.e., imaging data without accurate information indicating the object position. Here, the architecture of Unsupervised IR Image Net (UIR-Net) is designed to accommodate the unique characteristics of IR images from TMPs in manufacturing. This study presents a novel method for OD in unlabeled IR images from TMPs. The proposed method, called UIR-Net, consists of two components: label generation and DL model construction. Two case studies from automotive manufacturing, RSW and hot stamping, are reported to demonstrate the feasibility and effectiveness of the proposed method.

42 ENGINEERING↗
DOE OSTI2015

A Bridge for Accelerating Materials by Design

Recent technical advances in the area of nanoscale imaging, spectroscopy, and scattering/diffraction have led to unprecedented capabilities for investigating materials structural, dynamical and functional characteristics. In addition, recent advances in computational algorithms and computer capacities that are orders of magnitude larger/faster have enabled large-scale simulations of materials properties starting with nothing but the identity of the atomic species and the basic principles of quantum- and statistical-mechanics and thermodynamics. Along with these advances, an explosion of high-resolution data has emerged. This confluence of capabilities and rise of big data offer grand opportunities for advancing materials sciences but also introduce several challenges. In this editorial we identify challenges impeding progress towards advancing materials by design (e.g., the design/discovery of materials with improved properties/performance), possible solutions, and provide examples of scientific issues that can be addressed by using a tightly integrated approach where theory and experiments are linked through big-deep data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Constraining Data Mining with Physical Models: Voltage- and Oxygen Pressure-Dependent Transport in Multiferroic Nanostructures

Development of new generation electronic devices requires understanding and controlling the electronic transport in ferroic, magnetic, and optical materials, which is hampered by two factors. First, the complications of working at the nanoscale, where interfaces, grain boundaries, defects, and so forth, dictate the macroscopic characteristics. Second, the convolution of the response signals stemming from the fact that several physical processes may be activated simultaneously. Here, we present a method of solving these challenges via a combination of atomic force microscopy and data mining analysis techniques. Rational selection of the latter allows application of physical constraints and enables direct interpretation of the statistically significant behaviors in the framework of the chosen physical model, thus distilling physical meaning out of raw data. We demonstrate our approach with an example of deconvolution of complex transport behavior in a bismuth ferrite–cobalt ferrite nanocomposite in ambient and ultrahigh vacuum environments. Measured signal is apportioned into four electronic transport patterns, showing different dependence on partial oxygen and water vapor pressure. These patterns are described in terms of Ohmic conductance and Schottky emission models in the light of surface electrochemistry. Finally and furthermore, deep data analysis allows extraction of local dopant concentrations and barrier heights empowering our understanding of the underlying dynamic mechanisms of resistive switching.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Legacy Survey of Space and Time Data Preview 1: deep_coadd dataset type

The Legacy Survey of Space and Time Data Preview 1 (DP1) is the first release of data from the NSF-DOE Vera C. Rubin Observatory. It consists of raw and calibrated single-epoch images, co-adds, difference images, detection catalogs, and other derived data products. DP1 is based on 1792 science-grade optical/near-infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera, LSSTComCam, on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile during the first on-sky commissioning campaign in late 2024. DP1 covers a total of approximately 15 sq. deg. over seven roughly equally-sized non-contiguous fields, each independently observed in six broad photometric bands, ugrizy, spanning a range of stellar densities and latitudes and overlapping with external reference datasets. This dataset is a subset of the full data release consisting of the deep_coadd dataset type. These are the combination of multiple processed, calibrated, and background- subtracted images, for a patch of sky, for each of the six filters. This release contains 2,644 datasets of this type.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine-learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns. We overcome the scarce-data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data. As a test case, 115 thin-film metal-halides spanning 3 dimensionalities and 7 space-groups are synthesized and classified. After testing various algorithms, we develop and implement an all convolutional neural network, with cross-validated accuracies for dimensionality and space-group classification of 93% and 89%, respectively. We propose average class activation maps, computed from a global average pooling layer, to allow high model interpretability by human experimentalists, elucidating the root-causes of misclassification. Finally, we systematically evaluate the maximum XRD pattern step size (data acquisition rate) before loss of predictive accuracy occurs, and determine it to be 0.16° 2θ, which enables an XRD pattern to be obtained and classified in 5.5 minutes or less.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Running of the charm-quark mass from HERA deep-inelastic scattering data

Combined HERA data on charm production in deep-inelastic scattering have previously been used to determine the charm-quark running mass m c (m c ) in the MS¯ renormalisation scheme. Here, the same data are used as a function of the photon virtuality Q 2 to evaluate the charm-quark running mass at different scales to one-loop order, in the context of a next-to-leading order QCD analysis. Lastly, the scale dependence of the mass is found to be consistent with QCD expectations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Job Management with mpi_jm

About this book: This book constitutes the refereed post-conference proceedings of 13 workshops held at the 33rd International ISC High Performance 2018 Conference, in Frankfurt, Germany, in June 2018: HPC I/O in the Data Center, HPC-IODC 2018; Workshop on Performance and Scalability of Storage Systems, WOPSSS 2018; 13th Workshop on Virtualization in High­-Performance Cloud Computing, VHPC 2018; Third International Workshop on In Situ Visualization, WOIV 2018; 4th International Workshop on Communication Architectures for HPC, Big Data, Deep Learning and Clouds at Extreme Scale, ExaComm 2018; International Workshop on OpenPOWER for HPC, IWOPH 2018; IXPUG Workshop: Many-Core Computing on Intel Processors; Workshop on Sustainable Ultrascale Computing Systems; Approximate and Transprecision Computing on Emerging Technologies, ATCET 2018; First Workshop on the Convergence of Large-Scale Simulation and Artificial Intelligence; Third Workshop for Open Source Supercomputing, OpenSuCo 2018; First Workshop on Interactive High-Performance Computing; Workshop on Performance Portable Programming Models for Accelerators, P^3MA 2018. The 53 full papers included in this volume were carefully reviewed and selected from 80 submissions. They cover all aspects of research, development, and application of large-scale, high performance experimental and commercial systems. Topics include HPC computer architecture and hardware; programming models, system software, and applications; solutions for heterogeneity, reliability, power efficiency of systems; virtualization and containerized environments; big data and cloud computing; and artificial intelligence.

Berkowitz, Evan↗
DOE OSTI2023

Machine learning for detection of 3D features using sparse x-ray tomographic reconstruction

In many inertial confinement fusion (ICF) experiments, the neutron yield and other parameters cannot be completely accounted for with one and two dimensional models. This discrepancy suggests that there are three dimensional effects that may be significant. Sources of these effects include defects in the shells and defects in shell interfaces, the fill tube of the capsule, and the joint feature in double shell targets. Due to their ability to penetrate materials, x rays are used to capture the internal structure of objects. Methods such as computational tomography use x-ray radiographs from hundreds of projections, in order to reconstruct a three dimensional model of the object. In experimental environments, such as the National Ignition Facility and Omega-60, the availability of these views is scarce, and in many cases only consists of a single line of sight. Mathematical reconstruction of a 3D object from sparse views is an ill-posed inverse problem. These types of problems are typically solved by utilizing prior information. Neural networks have been used for the task of 3D reconstruction as they are capable of encoding and leveraging this prior information. We utilize half a dozen, different convolutional neural networks to produce different 3D representations of ICF implosions from the experimental data. Deep supervision is utilized to train a neural network to produce high-resolution reconstructions. These representations are used to track 3D features of the capsules, such as the ablator, inner shell, and the joint between shell hemispheres. Machine learning, supplemented by different priors, is a promising method for 3D reconstructions in ICF and x-ray radiography, in general.

Wolfe, Bradley T. (ORCID:0000000268301614)↗
DOE OSTI2017

The Stellar Initial Mass Function in Early-type Galaxies from Absorption Line Spectroscopy. III. Radial Gradients

There is good evidence that the centers of massive early-type galaxies have a bottom-heavy stellar initial mass function (IMF) compared to that of the Milky Way. Here we study the radial variation of the IMF within such galaxies, using a combination of high-quality Keck spectroscopy and a new suite of stellar population synthesis models that cover a wide range in metallicity. As in the previous studies in this series, the models are fitted directly to the spectra and treat all elemental abundance ratios as free parameters. Using newly obtained spectroscopy for six galaxies, including deep data extending to ∼1R{sub e} for the galaxies NGC 1407, NGC 1600, and NGC 2695, we find that the IMF varies strongly with galactocentric radius. For all six galaxies the IMF is bottom-heavy in the central regions, with average mass-to-light ratio “mismatch” parameter α≡(M/L)/(M/L){sub MW}≈2.5 at R = 0. The IMF rapidly becomes more bottom-light with increasing radius, flattening off near the Milky Way value (α≈1.1) at R>0.4R{sub e}. A consequence is that the luminosity-weighted average IMF depends on the measurement aperture: within R=R{sub e} we find 〈α〉{sub L}=1.3--1.5, consistent with recent lensing and dynamical results from SLACS and ATLAS{sup 3D}. Our results are also consistent with several earlier studies that were based on analyses of radial gradients of line indices. The observed IMF gradients support galaxy formation models in which the central regions of massive galaxies had a different formation history than their outer parts. Finally, we make use of the high signal-to-noise central spectra of NGC 1407 and NGC 2695 to demonstrate how we can disentangle IMF effects and abundance effects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

A Detailed Study of the Most Relaxed SPT-selected Galaxy Clusters: Properties of the Cool Core and Central Galaxy

We present a multi-wavelength analysis of the four most relaxed clusters in the South Pole Telescope 2500 deg 2 survey, which lie at 0.55 < z < 0.75. This study, which utilizes new, deep data from Chandra and Hubble, along with ground-based spectroscopy from Gemini and Magellan, improves significantly on previous studies in both depth and angular resolution, allowing us to directly compare to clusters at z~0. We find that the temperature, density, and entropy profiles of the intracluster medium (ICM) are very similar among the four clusters, and share similar shapes to clusters at z~0. Specifically, we find no evidence for deviations from self similarity in the temperature profile over the radial range 10kpc < r < 1Mpc, implying that the processes responsible for preventing runaway cooling over the past >6 Gyr are, at least roughly, preserving self similarity. We find typical metallicities of ~0.3 Zsun in the bulk of the ICM, rising to ~0.5 Zsun in the inner ~100 kpc, and reaching ~1 Zsun at r < 10kpc. This central excess is similar in magnitude to what is observed in the most relaxed clusters at z~0, suggesting that both the global metallicity and the central excess that we see in cool core clusters at z~0 were in place very early in the cluster lifetime and, specifically, that the central excess is not due to late-time enrichment by the central galaxy. Consistent with observations at z~0, we measure a diversity of stellar populations in the central brightest cluster galaxies of these four clusters, with star formation rates spanning a factor of ~500, despite the similarity in cooling time, cooling rate, and central entropy. These data suggest that, while the details vary dramatically from system to system, runaway cooling has been broadly regulated in relaxed clusters over the past 6 Gyr.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

OPER: Optimality-Guided Embedding Table Parallelization for Large-scale Recommendation Model

With the sharp increasing volume of user data, Deep Learning Recommendation Model (DLRM) becomes an indispensable infrastructure in large technology companies. However, large-scale DLRM on the multi-GPU platform is still inefficient due to unbalanced workload partitioning and intensive inter-GPU communication. To this end, we propose OPER, an OPtimality guided Embedding table placement for large-scale Recommendation model training and inference. OPER explores the potential of mitigating remote memory access latency in DLRM through fine-grained embedding table placement. Specifically, OPER proposes a theoretical modeling that builds up the relationship between EMT placement and the embedding communication latency in both training and inference. OPER proves the NP hardness of finding the optimal embedding table placement and proposes a heuristic algorithm that yields near optimal placement. OPER implements a SHMEM-based embedding table training system and a unified embedding index mapping to support fine-grained embedding table sharding and placement. Comprehensive experiments reveal that OPER achieves on average 3.4× and 5.1× speedup on training and inference respectively over state-of-the-art DLRM frameworks.

Wang, Zheng↗
DOE OSTI2020

Multimodal Data Representation with Deep Learning for Extracting Cancer Characteristics from Clinical Text

This paper presents a multimodal data representation to improve the performance of deep learning models for extracting cancer key characteristics from unstructured text in pathology reports. Specifically, in addition to using the text as the input to deep learning models, we use concept unique identifiers (CUIs) as another source of information to the models. We analyze the performance of different text and CUI data representations, including word embeddings and bag of embeddings (BOE), with a convolutional neural network (CNN) and a fully connected multilayer perceptron neural network (MLP-NN). The high level document embeddings from text and CUI inputs are combined by concatenating them and then applying a classifier. The model is used for extracting cancer subsite and histology from pathology reports. These two classification tasks have a large number of labels, i.e. 317 for subsite and 556 for histology, with extreme class imbalance. We compare the performance of the developed DL models across the two tasks based on micro- and macro-F1 scores. The evaluation shows that a multi-channel DL model that utilizes text represented by word embeddings and CUIs represented by BOE outperforms other DL models. Also, this approach significantly improves the model performance on low prevalence classes.

Alawad, Mohammed↗
DOE OSTI2014

White dwarfs in the UKIRT infrared deep sky survey data release

We have identified 8 to 10 new cool white dwarfs from the Large Area Survey (LAS) Data Release 9 of the United Kingdom InfraRed Telescope (UKIRT) Infrared Deep Sky Survey (UKIDSS). The data set was paired with the Sloan Digital Sky Survey to obtain proper motions and a broad ugrizYJHK wavelength coverage. Optical spectroscopic observations were secured at Gemini Observatory and confirm the degenerate status for eight of our targets. The final sample includes two additional white dwarf candidates with no spectroscopic observations. We rely on improved one-dimensional model atmospheres and new multi-dimensional simulations with CO5BOLD to review the stellar parameters of the published LAS white dwarf sample along with our additional discoveries. Most of the new objects possess very cool atmospheres with effective temperatures below 5000 K, including two pure-hydrogen remnants with a cooling age between 8.5 and 9.0 Gyr, and tangential velocities in the range 40 km s{sup –1} ≤v {sub tan} ≤ 60 km s{sup –1}. They are likely thick disk 10-11 Gyr old objects. In addition, we find a resolved double degenerate system with v {sub tan} ∼ 155 km s{sup –1} and a cooling age between 3.0 and 5.0 Gyr. These white dwarfs could be disk remnants with a very high velocity or former halo G stars. We also compare the LAS sample with earlier studies of very cool degenerates and observe a similar deficit of helium-dominated atmospheres in the range 5000 < T {sub eff} (K) < 6000. We review the possible explanations for the spectral evolution from helium-dominated toward hydrogen-rich atmospheres at low temperatures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

The Morphology and Structure of Stellar Populations in the Fornax Dwarf Spheroidal Galaxy from Dark Energy Survey Data

Using deep wide-field photometry 3 yr data (Y3) from the Dark Energy Survey (DES), we introduce a panoramic study of the Fornax dwarf spheroidal galaxy. The data discussed here—a small subset of the full survey—uniformly cover a region of 25 deg 2 centered on the galaxy to a depth of g ~ 23.5. We use these data to study the structural properties of Fornax, overall stellar population, and its member stars in different evolutionary phases. We also search for possible signs of tidal disturbance. Fornax is found to be significantly more spatially extended than what early studies suggested. No statistically significant distortions or signs of tidal disturbances were found down to a surface brightness limit of ~32.1 mag arcsec -2 . Yet, there are hints of shell-like features located ~20'–40' from the center of Fornax that may be stellar debris from past merger events. We also find that intermediate-age and young main-sequence populations show different orientation at the galaxy center and have many substructures. The deep DES Y3 data allow us to characterize the age of those young stellar substructures with great accuracy, both those previously known and those newly identified as possible overdensities in this work, on the basis of their color–magnitude diagram morphology. We find that the youngest overdensities are all found on the eastern side of Fornax, where the Fornax field population itself is slightly younger than in the west. In summary, the high-quality DES Y3 data reveal that Fornax has many rich structures and provide insights into its complex formation history.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

DeepClone: Lightweight State Replication of Deep Learning Models for Data Parallel Training

Training modern deep neural network (DNN) models involves complex workflows triggered by model exploration, sensitivity analysis, explainability, etc. A key primitive in this context is the ability to clone a model training instance, i.e. “fork” the training process in a potentially different direction, which enables comparisons of different evolution paths using variations of training data and model parameters. However, in a quest improve the training throughput, a mix of data parallel, model parallel, pipeline parallel and layer-wise parallel approaches are making the problem of cloning highly complex. In this paper, we explore the problem of efficient cloning under such circumstances. To this end, we leverage several properties of data-parallel training and layer-wise parallelism to design DeepClone, a cloning approach based on augmenting the execution graph to gain direct access to tensors, which are then sharded and reconstructed asynchronously in order to minimize runtime overhead, standby duration, readiness duration. Compared with state-of-art approaches, DeepClone shows orders of magnitude improvement for several classes of DNN models.

data-parallel training↗
DOE OSTI2022

Unpolarized transverse momentum distributions from a global fit of Drell-Yan and semi-inclusive deep-inelastic scattering data

We present an extraction of unpolarized transverse-momentum-dependent parton distribution and fragmentation functions based on more than two thousand data points from several experiments for two different processes: semi-inclusive deep-inelastic scattering and Drell-Yan production. The baseline analysis is performed using the Monte Carlo replica method and resumming large logarithms at N 3 LL accuracy. The resulting description of the data is very good (χ 2 /N dat = 1.06). For semi-inclusive deep-inelastic scattering, predictions for multiplicities are normalized by factors that cure the discrepancy with data introduced by higher-order perturbative corrections.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗