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DOE OSTI2024

A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems

We present the first machine learning-based autonomous hyperspectral neutron computed tomography experiment performed at the Spallation Neutron Source. Hyperspectral neutron computed tomography allows the characterization of samples by enabling the reconstruction of crystallographic information and elemental/isotopic composition of objects relevant to materials science. High quality reconstructions using traditional algorithms such as the filtered back projection require a high signal-to-noise ratio across a wide wavelength range combined with a large number of projections. This results in scan times of several days to acquire hundreds of hyperspectral projections, during which end users have minimal feedback. To address these challenges, a golden ratio scanning protocol combined with model-based image reconstruction algorithms have been proposed. This novel approach enables high quality real-time reconstructions from streaming experimental data, thus providing feedback to users, while requiring fewer yet a fixed number of projections compared to the filtered back projection method. In this paper, we propose a novel machine learning criterion that can terminate a streaming neutron tomography scan once sufficient information is obtained based on the current set of measurements. Our decision criterion uses a quality score which combines a reference-free image quality metric computed using a pre-trained deep neural network with a metric that measures differences between consecutive reconstructions. The results show that our method can reduce the measurement time by approximately a factor of five compared to a baseline method based on filtered back projection for the samples we studied while automatically terminating the scans.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Vapor–liquid equilibrium of water with the MB-pol many-body potential

Among the many existing molecular models of water, the MB-pol many-body potential has emerged as a remarkably accurate model, capable of reproducing thermodynamic, structural, and dynamic properties across water's solid, liquid, and vapor phases. In this work, we assessed the performance of MB-pol with respect to an important set of properties related to vapor-liquid coexistence and interfacial behavior. Through direct coexistence classical molecular dynamics simulations at temperatures of 400 K < T < 600 K, we calculated properties such as equilibrium coexistence densities, vapor-liquid interfacial tension, vapor pressure, and enthalpy of vaporization and compared the MB-pol results to experimental data. We also compared rigid vs fully flexible variants of the MB-pol model and evaluated system size effects for the properties studied. We found that the MB-pol model predictions are in good agreement with experimental data, even for temperatures approaching the vapor-liquid critical point; this agreement was largely insensitive to system sizes or the rigid vs flexible treatment of the intramolecular degrees of freedom. Furthermore, these results attest to the chemical accuracy of MB-pol and its high degree of transferability, thus enabling MB-pol's application across a large swath of water's phase diagram.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Search for decays of the Higgs boson into pair-produced pseudoscalar particles decaying into τ+τ−τ+τ− using pp collisions at s=13 TeV with the ATLAS detector

A search for a pair of low-mass pseudoscalars a that promptly decay into τ-leptons is presented using 140 fb −1 of proton–proton collision data at 13 TeV centre-of-mass energy recorded with the ATLAS detector at the Large Hadron Collider. The result is used to place constraints on exotic decays of the Higgs boson into four τ-leptons, H→aa→τ+τ−τ+τ−. This search focuses on events with either one or two τ-leptons decaying into hadrons and neutrinos, and the remaining three or two τ-leptons decaying into either electron or muon and neutrinos. No significant excess is observed above the expected Standard Model background and upper limits at the 95% confidence level on B(H→aa→τ+τ−τ+τ−) are set ranging from 0.06 to 0.23, depending on the mass ma ranging from 15 to 60 GeV.

Source record↗

DOE OSTI2020

Search for physics beyond the standard model in events with jets and two same-sign or at least three charged leptons in proton-proton collisions at $\sqrt{s}=$ 13 TeV

A data sample of events from proton-proton collisions with at least two jets, and two isolated same-sign or three or more charged leptons, is studied in a search for signatures of new physics phenomena. The data correspond to an integrated luminosity of $137{\,{\text {fb}}^{-1}} $ at a center-of-mass energy of $13\,{\text {TeV}} $, collected in 2016–2018 by the CMS experiment at the LHC. The search is performed using a total of 168 signal regions defined using several kinematic variables. The properties of the events are found to be consistent with the expectations from standard model processes. Exclusion limits at 95% confidence level are set on cross sections for the pair production of gluinos or squarks for various decay scenarios in the context of supersymmetric models conserving or violating R parity. The observed lower mass limits are as large as $2.1\,{\text {TeV}} $ for gluinos and $0.9\,{\text {TeV}} $ for top and bottom squarks. To facilitate reinterpretations, model-independent limits are provided in a set of simplified signal regions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Enterprise Artificial Intelligence Strategy for Los Alamos National Laboratory

In the 1984 martial arts drama film, The Karate Kid, a young Daniel LaRusso is unexpectedly placed in an adversarial environment unable to eYectively adapt to a series of new threats and limitations. Fortunately for the main character, once placed under the tutelage of a Mr. Miyagi, he finds resiliency not through the adoption of new tools, but a re-focused set of fundamentals. Much in the same way that Daniel learns waxing on and buYing oY car wax by hand has rewards for Karate, LANL is choosing the harder path of self-hosting Large Language Models (LLMs) for enterprise use instead of only relying on buying access to a hosted AI service like Azure’s OpenAI Application Programming Interface (API). We also are not willing to wait for software-as-a-service (SAAS) AI services to meet us where we need to be from a FedRAMP accreditation standpoint. Our operations regularly depend on access at CUI, UCNI, ITAR and other FIPS-199 moderate-impact data levels and hosting our own services gives us the right security and compliance posture to be useful across the broad range of our work at LANL. With the rise in threats to critical infrastructure, cloud service providers (CSPs), and supply chain attacks from both state and non-state actors, we are not placing the bet that SAAS hosted AI services will be available when we need them. Should a major event occur, we do not want our staY and operations left without a pathway for us to fix the problem and resume the use of AI tools.

42 ENGINEERING↗

DOE OSTI2025

Proceedings for the Workshop on Applied Nuclear Data Activities 2025

The 2025 Workshop for Applied Nuclear Data Activities (WANDA) covered four topic areas in nuclear data: Nuclear Data and Deterrence, Nuclear Data Prioritization for Fusion, High-Assay Low-Enriched Uranium and Novel Moderators for Advanced Reactors, and Data Preservation and Data Workflows. The intention of this workshop is to connect different communities that are invested in nuclear data and have their own unique sets of needs for the purposes of sharing information, fostering collaboration in areas of shared interest, and leveraging synergistic capabilities. The attendance of federal program managers at these workshops is essential in creating awareness of the needs of their respective communities and in providing information to better guide funding investments. In each of these topical sessions, a general description of the nuclear data needs and/or capabilities was presented, along with discussions of existing capabilities that could be leveraged, potential synergistic needs or resources, and challenges that must be overcome for the application space to progress. There are many synergistic nuclear data needs among these application spaces. The discussion largely focused on increasing the accuracy of the nuclear data and better quantifying the data uncertainties that have the greatest impact on applications. The full-day session on data processing and data workflows was by nature intended to be synergistic and applicable to all technical sessions at WANDA. A notable common theme that was highlighted across all sessions was the need for accelerated delivery of nuclear data products across complex and time-consuming workflows.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

NLR's FleetDNA Speed Distributions for EPA MOVES

The U.S. Environmental Protection Agency (EPA) MOtor Vehicle Emission Simulator (MOVES) models mobile sources of air toxics at small and large scales across the Unites States. Vehicle speed distributions based on the type of vehicle, type of road traveled, and hour of the day are inputs into MOVES. In conjunction with vehicle miles traveled (VMT) inputs, they are used to estimate total operation time and select the relevant driving cycles from which running operating modes and emissions are estimated. In an ongoing research effort, the National Laboratory of the Rockies (NLR) partnered with the EPA to provide default average speed data for heavy-duty vehicles based on NLR's FleetDNA database and the Bourns College of Engineering - Center for Environmental Research and Technology (CE-CERT) data set from the University of California, Riverside.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Measuring Young Stars in Space and Time. II. The Pre-main-sequence Stellar Content of N44

The Hubble Space Telescope survey Measuring Young Stars in Space and Time (MYSST) entails some of the deepest photometric observations of extragalactic star formation, capturing even the lowest-mass stars of the active star-forming complex N44 in the Large Magellanic Cloud. We employ the new MYSST stellar catalog to identify and characterize the content of young pre-main-sequence (PMS) stars across N44 and analyze the PMS clustering structure. To distinguish PMS stars from more evolved line of sight contaminants, a non-trivial task due to several effects that alter photometry, we utilize a machine-learning classification approach. This consists of training a support vector machine (SVM) and a random forest (RF) on a carefully selected subset of the MYSST data and categorize all observed stars as PMS or non-PMS. Combining SVM and RF predictions to retrieve the most robust set of PMS sources, we find ∼26,700 candidates with a PMS probability above 95% across N44. Employing a clustering approach based on a nearest neighbor surface density estimate, we identify 16 prominent PMS structures at 1σ significance above the mean density with sub-clusters persisting up to and beyond 3σ significance. The most active star-forming center, located at the western edge of N44's bubble, is a subcluster with an effective radius of ∼5.6 pc entailing more than 1100 PMS candidates. Furthermore, we confirm that almost all identified clusters coincide with known H ii regions and are close to or harbor massive young O stars or YSOs previously discovered by MUSE and Spitzer observations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

CLASS: Coronal Line Activity Spectroscopic Survey

We conduct the first systematic survey of a comprehensive set of the twenty optical coronal lines in the spectra of nearly 1 million galaxies observed by the Sloan Digital Sky Survey (SDSS) Data Release 8 catalog. This includes often overlooked high ionization potential lines such as [Ar x] λ5533, [S XII ] λ7609, [Fe XI ] λ7892, and [Fe XIV ] λ5303. We find that, given the limited sensitivity of SDSS, strong coronal line emission is extremely rare, with only ~0.03% of the sample showing at least one coronal line, significantly lower than the fraction of galaxies showing optical narrow line ratios (~17%) or mid-infrared colors (~2%) indicative of nuclear activity. The coronal line luminosities exhibit a large dynamic range, with values ranging from ~10 34 to 10 42 erg s –1 . We find that a vast majority (~80%) of coronal line emitters in dwarf galaxies (M $\star$ < 9.6 × 10 9 M ⊙ ) do not display optical narrow line ratios indicative of nuclear activity, in contrast to higher mass galaxies (~17%). Moreover, we find that the highest ionization potential lines are preferentially found in lower mass galaxies. These findings are consistent with the theory that lower mass black holes found in lower mass galaxies produce a hotter accretion disk, which in turn enhances the higher ionization coronal line spectrum. Future coronal line searches with 30 m class telescopes or JWST may provide a pathway into uncovering the intermediate mass black hole population.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Search for supersymmetry in proton-proton collisions at $\sqrt{s} =$ 13 TeV in events with high-momentum Z bosons and missing transverse momentum

A search for new physics in events with two highly Lorentz-boosted Z bosons and large missing transverse momentum is presented. The analyzed proton-proton collision data, corresponding to an integrated luminosity of 137 fb$^{−1}$, were recorded at $ \sqrt{s} $ = 13 TeV by the CMS experiment at the CERN LHC. The search utilizes the substructure of jets with large radius to identify quark pairs from Z boson decays. Backgrounds from standard model processes are suppressed by requirements on the jet mass and the missing transverse momentum. No significant excess in the event yield is observed beyond the number of background events expected from the standard model. For a simplified supersymmetric model in which the Z bosons arise from the decay of gluinos, an exclusion limit of 1920 GeV on the gluino mass is set at 95% confidence level. This is the first search for beyond-standard-model production of pairs of boosted Z bosons plus large missing transverse momentum.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Predicting Sintering Window of Binder Jet Additively Manufactured Parts Using a Coupled Data Analytics and CALPHAD Approach

Batch-to-batch variation in powder compositions for binder jet additive manufacturing (BJAM) can significantly deter defining an “ideal” sintering window for a given alloy. One way to overcome the problem is by running sintering experiments at various temperatures for each batch of the powder. However, such an approach increases the time required to achieve large-scale production of parts. The predictive capabilities of computational thermodynamic tools like CALPHAD can be leveraged to overcome the challenge, especially for binder jet additive manufacturing, since the process occurs under near-equilibrium conditions. However, calculating the sintering window using CALPHAD can be computationally expensive, considering many possible feedstock compositions within “specification”. Here, we generate high throughput CALPHAD data for nickel-based superalloys to develop machine learning models to predict the sintering window rapidly. The predictive capability of the models has been validated using published results on BJAM of Inconel 718 and 625. Further, validated models are lightweight and can be deployed in an industrial setting to get sintering window in an accelerated manner.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Real-Time Monitoring of Gas-Phase and Dissolved CO 2 Using a Mixed-Matrix Composite Integrated Fiber Optic Sensor for Carbon Storage Application

Novel chemical sensors that improve detection and quantification of CO 2 are critical to ensuring safe and cost-effective monitoring of carbon storage sites. Fiber optic (FO) based chemical sensor systems are promising field-deployable systems for real-time monitoring of CO 2 in geological formations for long-range distributed sensing. Here, a mixed-matrix composite integrated FO sensor system was developed with a purely optical readout that reliably operates as a detector for gas-phase and dissolved CO 2 . A mixed-matrix composite sensor coating consisting of plasmonic nanocrystals and hydrophobic zeolite embedded in a polymer matrix was integrated on the FO sensor. The mixed-matrix composite FO sensor showed excellent reversibility/stability in a high humidity environment and sensitivity to gas-phase CO 2 over a large concentration range. This remarkable sensing performance was enabled by using plasmonic nanocrystals to significantly enhance the sensitivity and a hydrophobic zeolite to effectively mitigate interference from water vapor. The sensor exhibited the ability to sense CO 2 in the presence of other geologically relevant gases, which is of importance for applications in geological formations. A prototype FO sensor configuration which possesses a robust sensing capability for monitoring dissolved CO 2 in natural water was demonstrated. Reproducibility was confirmed over many cycles, both in a laboratory setting and in the field. More importantly, we demonstrated on-line monitoring capabilities with a wireless telemetry system, which transferred the data from the field to a website. The combination of outstanding CO 2 sensing properties and facile coating processability makes this mixed-matrix composite FO sensor a good candidate suitable for practical carbon storage applications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Building an Efficient Cluster Cosmology Software Package for Modeling Cluster Counts and Lensing

We introduce a software suite developed for galaxy cluster cosmological analysis with the Dark Energy Survey Data. Cosmological analyses based on galaxy cluster number counts and weak-lensing measurements need efficient software infrastructure to explore an increasingly large parameter space, and account for various cosmological and astrophysical effects. Our software package is designed to model the cluster observables in a wide-field optical survey, including galaxy cluster counts, their averaged weak-lensing masses, or the cluster's averaged weak-lensing radial signals. To ensure maximum efficiency, this software package is developed in C++ in the CosmoSIS software framework, making use of the CUBA integration library. We also implement a testing and validation scheme to ensure the quality of the package. We demonstrate the effectiveness of this development by applying the software to the Dark Energy Survey Year 1 galaxy cluster cosmological data sets, and acquired cosmological constraints that are consistent with the fiducial Dark Energy Survey analysis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Efficient Parallelization of Irregular Applications on GPU Architectures

With the enlarging computation capacity of general Graphics Processing Units (GPUs), leveraging GPUs to accelerate parallel applications has become a critical topic in academia and industry. However, a wide range of irregular applications with the computation-/memory-intensive nature cannot easily achieve high GPU utilization. The challenges mainly involve the following aspects: first, data dependence leads to coarse-grained kernel and inefficient parallelism; second, heavy GPU memory usage may cause frequent memory evictions and extra overhead of I/O; third, specific computation patterns produce memory redundancies; last, workload balance and data reusability conjunctly benefit the overall performance, but there may exist a dynamic trade-off between them. Targeting these challenges, this dissertation proposes multiple optimizations to accelerate two real-world applications: many-body correlation functions to simulate nuclear physics in a large-scale scientific system; the other is the eALS-based matrix factorization recommendation system. To accelerate the calculations of many-body correlation functions, this dissertation presents three frameworks in GPU memory management and multi-GPU scheduling. Firstly, an optimized systematic GPU memory management framework, MemHC, utilizes a series of new memory reduction designs in GPU memory allocation, CPU/GPU communications, and GPU memory oversubscription. Secondly, an enhanced multi-GPU scheduling framework, MICCO, particularly by taking both data dimension (e.g., data reuse and data eviction) and computation dimension into account. MICCO designs a heuristic scheduling algorithm and a machine learning-based regression model to generate the optimal settings of a proposed new concept to manage the trade-off. Thirdly, a locality-aware multi-GPU scheduling framework. This scheduler leverages pipeline batch generation with a looking-ahead strategy by building local dependency graphs for memory transfer reduction and better data reuse, achieving up to 79.92% memory cost reduction and 1.67x speedup. To parallelize the eALS-based recommendation system, this dissertation proposes an efficient CPU/GPU heterogeneous recommendation system, HEALS. HEALS employs newly designed architecture-adaptive data formats to achieve load balance and good data locality on CPU and GPU. To mitigate the data dependence, HEALS presents a CPU/GPU collaboration model for both task parallelism and data parallelism with multiple kernel computation optimizations. In summary, this dissertation efficiently accelerates two typical irregular applications on GPUs by building four frameworks, including CPU/GPU collaboration, GPU memory management, and multi-GPU scheduling.

Wang, Qihan↗

DOE OSTI2024

Large-Scale Visualization of 3D Unstructured Groundwater Model Using Cave Automated Virtual Environment

The immersive three-dimensional (3D) virtual reality (VR) visualization of groundwater models allows us to deepen our understanding of aquifer systems and provide better solutions to present groundwater-related problems, such as groundwater recharge, water quality, and sustainability. Visualization assists in accurately developing groundwater models and revealing important subsurface features, including faulting, folding, and unconformity. However, assessing model accuracy poses challenges due to the complexity of geology and groundwater systems. This research demonstrates a workflow to visualize and analyze raw 3D unstructured groundwater model data using an immersive Cave Automated Virtual Environment (CAVE). To visualize the unstructured groundwater model data, the raw dataset is converted into interactive CAVE-compatible formats utilizing a set of tools: ParaView, Blender, and Unity. This enables researchers to immerse themselves in the data, identifying influential patterns and relationships. e resulting insights can inform the development of sophisticated machine-learning models for groundwater level prediction. The CAVE’s immersive capabilities allow intuitive exploration from various perspectives, providing a more holistic understanding of the factors affecting groundwater levels. These insights are crucial to improve predictive models. The CAVE results also facilitate collaborative analysis and have potential applications in training and education. is research demonstrates the value of immersive VR tools such as the CAVE for unraveling intricacies within high-dimensional scientific data to drive real-world forecasting and modeling applications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Search for vector-like leptons coupling to first- and second-generation Standard Model leptons in pp collisions at s = 13 TeV with the ATLAS detector

A search for pair production of vector-like leptons coupling to first- and second-generation Standard Model leptons is presented. The search is based on a dataset of proton-proton collisions at s$$ \sqrt{s} $$ = 13 TeV recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 140 fb−1. Events are categorised depending on the flavour and multiplicity of leptons (electrons or muons), as well as on the scores of a deep neural network targeting particular signal topologies according to the decay modes of the vector-like leptons. In each of the signal regions, the scalar sum of the transverse momentum of the leptons and the missing transverse momentum is analysed. The main background processes are estimated using dedicated control regions in a simultaneous fit with the signal regions to data. No significant excess above the Standard Model background expectation is observed and limits are set at 95% confidence level on the production cross-sections of vector-like electrons and muons as a function of the vector-like lepton mass, separately for SU(2) doublet and singlet scenarios. The resulting mass lower limits are 1220 GeV (1270 GeV) and 320 GeV (400 GeV) for vector-like electrons (muons) in the doublet and singlet scenarios, respectively.

Aad, G↗

DOE OSTI2020

A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications

Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the number of reaction intermediates can be very large, and the cost of calculating the adsorption energies by DFT for all surface intermediates even for one active site model can become prohibitive. In this paper, we have identified appropriate descriptors and machine learning models that can be used to predict a significant part of these adsorption energies given data on the rest of them. Furthermore, our investigations also included the case when the species data used to train the predictive model are of different size relative to the species the model tries to predict—this is an extrapolation in the data space which is typically difficult with regular machine learning models. Due to the relative size of the available data sets, we have attempted to extrapolate from the larger species to the smaller ones in the current work. Here, we have developed a neural network based predictive model that combines an established additive atomic contribution based model with the concepts of a convolutional neural network that, when extrapolating, achieves a statistically significant improvement over the previous models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Electricity use in big area additive manufacturing of fiber-reinforced polymer composites

In recent years, additive manufacturing (AM), especially large-format additive manufacturing (LFAM), has gained momentum in the manufacturing industry. While LFAM offers benefits over conventional manufacturing processes, such as minimizing material waste and providing vast geometric freedom, assessing its sustainability remains challenging due to limited data, particularly on energy consumption. Most existing data pertain to small-scale or desktop AM and are not directly applicable to LFAM. In this study, we conducted real-time measurements of electricity usage for a type of LFAM known as big area additive manufacturing (BAAM), which typically uses fiber-reinforced polymer pellets as feedstock. We collected electricity usage data from fifteen printing jobs over two months in an industrial production setting. These data fill the existing gap and can be reused to enhance the community’s understanding of LFAM electricity usage, support further research, and promote sustainable development in advanced manufacturing technologies.

ecology↗