Results for “network acceleration” — Page 67

DOE OSTI2026

Track Matching in the DUNE Near Detectors

The Deep Underground Neutrino Experiment (DUNE) is an international particle physics experiment looking answer some of the largest unanswered questions in neutrino physics. DUNE uses a high power neutrino beam produced at Fermi National Accelerator Laboratory (Fermilab), and consists of a near detector (ND) also located at Fermilab and a far detector (FD) 1300 km away at the Sanford Underground Research Facility (SURF) in South Dakota. In the first phase of the experiment, the ND complex will contain a modular liquid argon TPC (ND-LAr) and a solid scintillator-based muon spectrometer (TMS), in addition to a beam monitoring detector (SAND) and systems for moving ND-LAr and TMS away from the neutrino beam axis (PRISM). A prototype of ND-LAr, the 2x2 demonstrator, alongside a solid scintillator muon tagger provided by repurposed MINERvA planes, has been built and taken data at Fermilab. For analyses with the ND, connecting particle tracks (such as muons) that exit the liquid argon active volume into the solid scintillator muon detector can improve particle identification and energy reconstruction, and alleviate pileup due to the intense beam. To match tracks between detectors during reconstruction, we have explored using Graph Neural Networks (GNNs) to connect tracks segments between the liquid argon detector region and the solid scintillator detector planes. We have trained a GNN on reconstructed simulated data from the 2×2 demonstrator and repurposed MINERvA planes. We will evaluate its performance and then train a similar network on reconstructed ND-LAr and TMS simulations.

Xing, Daniel [U. Colorado, Boulder]↗

NASA NTRS2012

NASA Tech Briefs, August 2012

Topics covered include: Mars Science Laboratory Drill; Ultra-Compact Motor Controller; A Reversible Thermally Driven Pump for Use in a Sub-Kelvin Magnetic Refrigerator; Shape Memory Composite Hybrid Hinge; Binding Causes of Printed Wiring Assemblies with Card-Loks; Coring Sample Acquisition Tool; Joining and Assembly of Bulk Metallic Glass Composites Through Capacitive Discharge; 670-GHz Schottky Diode-Based Subharmonic Mixer with CPW Circuits and 70-GHz IF; Self-Nulling Lock-in Detection Electronics for Capacitance Probe Electrometer; Discontinuous Mode Power Supply; Optimal Dynamic Sub-Threshold Technique for Extreme Low Power Consumption for VLSI; Hardware for Accelerating N-Modular Redundant Systems for High-Reliability Computing; Blocking Filters with Enhanced Throughput for X-Ray Microcalorimetry; High-Thermal-Conductivity Fabrics; Imidazolium-Based Polymeric Materials as Alkaline Anion-Exchange Fuel Cell Membranes; Electrospun Nanofiber Coating of Fiber Materials: A Composite Toughening Approach; Experimental Modeling of Sterilization Effects for Atmospheric Entry Heating on Microorganisms; Saliva Preservative for Diagnostic Purposes; Hands-Free Transcranial Color Doppler Probe; Aerosol and Surface Parameter Retrievals for a Multi-Angle, Multiband Spectrometer LogScope; TraceContract; AIRS Maps from Space Processing Software; POSTMAN: Point of Sail Tacking for Maritime Autonomous Navigation; Space Operations Learning Center; OVERSMART Reporting Tool for Flow Computations Over Large Grid Systems; Large Eddy Simulation (LES) of Particle-Laden Temporal Mixing Layers; Projection of Stabilized Aerial Imagery Onto Digital Elevation Maps for Geo-Rectified and Jitter-Free Viewing; Iterative Transform Phase Diversity: An Image-Based Object and Wavefront Recovery; 3D Drop Size Distribution Extrapolation Algorithm Using a Single Disdrometer; Social Networking Adapted for Distributed Scientific Collaboration; General Methodology for Designing Spacecraft Trajectories; Hemispherical Field-of-View Above-Water Surface Imager for Submarines; and Quantum-Well Infrared Photodetector (QWIP) Focal Plane Assembly.

Source record↗

DOE OSTI2023

High-temperature active oxidation of nanocrystalline silicon-carbide: A reactive force-field molecular dynamics study

Flexible woven SiC ceramics are prone to accelerated fiber embrittlement under high temperature oxidation in dynamic oxygen environments. The nanocrystalline structure of the constituent fibers impacts the reaction kinetics and phase transformations during active oxidation. However, fundamental understanding and quantification of grain boundary effects on oxidation behavior in nanocrystalline SiC remain elusive when temperatures exceed 1500 K. This study deploys large-scale molecular dynamics simulations with a reactive force-field to elucidate the complex roles of atomic oxygen reservoir conditions and grain size on oxidation kinetics and the nature of oxides produced in both monocrystalline and nanocrystalline 3C-SiC between 1100 K and 2000 K. The simulations with dynamically replenished oxygen provide good agreement with oxidation kinetics and activation energies for the monocrystalline Si(100) and C(100) orientations published in the available literature. This study reveals that, by contrast, nanocrystalline SiC samples exhibit two distinct oxidation kinetics with a transition point at 1500 K due to surface melting, which is supported by experimental evidence. The introduction of a grain-boundary network produces a two-fold decrease in oxidation activation energies compared to monocrystalline SiC below 1500 K. Above 1500 K, however, the activation energies rise substantially due to the formation of a liquid Si phase at the SiC/Si oxide interface. Further, it is shown that the stability of the interfacial liquid phase is promoted by incoherent grain boundaries in the crystalline SiC. These findings are important for the deployment of nanocrystalline SiC fibers in advanced thermal protection systems for high-temperature applications.

36 MATERIALS SCIENCE↗

NASA NTRS2014

Carbon-Temperature-Water Change Analysis for Peanut Production Under Climate Change: A Prototype for the AgMIP Coordinated Climate-Crop Modeling Project (C3MP)

Climate change is projected to push the limits of cropping systems and has the potential to disrupt the agricultural sector from local to global scales. This article introduces the Coordinated Climate-Crop Modeling Project (C3MP), an initiative of the Agricultural Model Intercomparison and Improvement Project (AgMIP) to engage a global network of crop modelers to explore the impacts of climate change via an investigation of crop responses to changes in carbon dioxide concentration ([CO2]), temperature, and water. As a demonstration of the C3MP protocols and enabled analyses, we apply the Decision Support System for Agrotechnology Transfer (DSSAT) CROPGRO-Peanut crop model for Henry County, Alabama, to evaluate responses to the range of plausible [CO2], temperature changes, and precipitation changes projected by climate models out to the end of the 21st century. These sensitivity tests are used to derive crop model emulators that estimate changes in mean yield and the coefficient of variation for seasonal yields across a broad range of climate conditions, reproducing mean yields from sensitivity test simulations with deviations of ca. 2% for rain-fed conditions. We apply these statistical emulators to investigate how peanuts respond to projections from various global climate models, time periods, and emissions scenarios, finding a robust projection of modest (<10%) median yield losses in the middle of the 21st century accelerating to more severe (>20%) losses and larger uncertainty at the end of the century under the more severe representative concentration pathway (RCP8.5). This projection is not substantially altered by the selection of the AgMERRA global gridded climate dataset rather than the local historical observations, differences between the Third and Fifth Coupled Model Intercomparison Project (CMIP3 and CMIP5), or the use of the delta method of climate impacts analysis rather than the C3MP impacts response surface and emulator approach.

climate change↗

DOE OSTI2024

Towards Precision Photometric Type ia Supernova Cosmology With Machine Learning

The revolutionary discovery of dark energy and accelerating cosmic expansion was made with just 42 type Ia supernovae (SNe Ia) in 1999. Since then, large synoptic surveys, e.g., Dark Energy Survey (DES), have observed thousands more SNe Ia and the upcoming Rubin Legacy Survey of Space and Time (LSST) and Roman Space Telescope promise to deliver millions in the next decade. This unprecedented data volume can produce the required precision to unambiguously test concordance cosmology which could represent a monumental shift in our understanding of dark energy and its role in cosmic history. However, extracting a pure SN Ia sample with accurate redshifts for such a large dataset will be a challenge. Specifically, spectroscopic classification will not be possible for the vast majority of discovered objects, and only ~25% will have spectroscopic redshifts. This thesis presents a series of observational and methodological studies designed to address the questions associated with this new era of photometric SN Ia cosmology. First, we present a machine learning method for photometric classification of SNe, Supernova Classification with a COnvolutional Neural Network (SCONE). Photometric classification enables SNe with no spectroscopic information to be confidently categorized, and is a critical component of current and future analysis pipelines. SCONE achieves >99% accuracy distinguishing simulated SNe Ia from non-Ia SNe, and has been integrated into DES, LSST, and Roman analysis pipelines. We also demonstrate the efficacy of SCONE on early-time photometric classification, which will be vital for optimal allocation of spectroscopic resources. We show that SCONE can distinguish between 6 SN types with 75% accuracy on the night of initial discovery, comparable to results in the literature for full-phase SNe. Next, we study current methods for estimating SN Ia redshifts and propose a machine learning alternative that uses SN photometry alone to extract redshift information. Most SNe Ia inherit redshift information from their host galaxy, but the process of matching SNe to the correct host galaxy can be challenging. We systematically analyze the impact of incorrect redshifts from host galaxy mismatch on 5 years of DES SN data, and conclude that improved host matching or redshift estimation methods can reduce our systematic errors by ~10%. In response to this finding, we present a SN photometry-only method for estimating redshifts independent of host galaxy information, Photo-zSNthesis. We show that Photo-zSNthesis redshift estimates are accurate to within 2% across the full redshift range of LSST, a first in the literature. Finally, we focus on the robustness of machine learning (ML) algorithms for real-world and scientific applications. ML models generalize poorly beyond their training set and often experience severe performance degradation when deployed on new data. We demonstrate a general method for improving robustness that achieves new state-of-the-art results on astronomical object classification, wildlife identification, and tumor detection.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

EMT data generation

The integration of inverter-based resources (IBRs) in power systems is accelerating, bringing with it significant benefits such as reduced greenhouse gas emissions, improved grid resilience, and increased energy independence. Despite these advantages, the widespread adoption of IBRs introduces several challenges, including issues related to grid stability, increased operational complexity, and the need for updated regulatory frameworks. To address these challenges, IEEE released Standard 2800 in 2022, which sets forth the necessary interconnection capabilities and performance criteria for IBRs connected to transmission and sub-transmission systems. This standard outlines the performance requirements to ensure the reliable integration of IBRs into the bulk power system. Furthermore, in 2023, the North American Electric Reliability Corporation (NERC) published a reliability guideline for electromagnetic transient (EMT) modeling of BPS-connected IBRs. This guideline provides recommendations for developing EMT model requirements, performing model quality checks, and implementing verification practices specifically for EMT models representing BPS-connected inverter-based resources in reliability studies conducted by transmission planners and planning coordinators. These standards and guidelines have a profound impact on EMT studies for transmission networks, influencing system stability analyses, grid recovery and resynchronization processes, fault ride-through evaluations, protection and coordination strategies, advanced control methodologies, and the inclusion of IBRs in transient models of transmission networks. As a result, the generation of EMT data is crucial for conducting various transient-based studies to understand the impact of IBRs. EMT data generation use cases serve as the basis for scenarios in event detection and identification use cases, providing comprehensive details about EMT data generation for transmission grids with inverter-based resources. These use cases supply sufficient training and validation datasets for subsequent EMT analysis algorithms.

Xia, Qianxue↗

DOE OSTI2025

Neural architecture codesign for fast physics applications

We develop a pipeline to streamline neural architecture codesign for physics applications to reduce the need for ML expertise when designing models for novel tasks. Our method employs neural architecture search and network compression in a two-stage approach to discover hardware efficient models. This approach consists of a global search stage that explores a wide range of architectures while considering hardware constraints, followed by a local search stage that fine-tunes and compresses the most promising candidates. We exceed performance on various tasks and show further speedup through model compression techniques such as quantization-aware-training and neural network pruning. We synthesize the optimal models to high level synthesis code for FPGA deployment with the hls4ml library. Additionally, our hierarchical search space provides greater flexibility in optimization, which can easily extend to other tasks and domains. We demonstrate this with two case studies: Bragg peak finding in materials science and jet classification in high energy physics, achieving models with improved accuracy, smaller latencies, or reduced resource utilization relative to the baseline models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Cell Cycle-Dependent Recruitment of FtsN to the Divisome in Escherichia coli

Cell division in Escherichia coli starts with the formation of an FtsZ protofilament network at midcell, the Z ring. However, only after a considerable lag period does the cell start to form a midcell constriction. The onset of constriction depends upon the arrival of so-called late divisome proteins, among which, FtsN is the last essential one. The timing and dependency of FtsN arrival to the divisome, along with genetic evidence, suggests it triggers cell division. In this study, we used high-throughput fluorescence microscopy to determine the arrival of FtsN and the early divisome protein ZapA to midcell at a single-cell level during the cell cycle. Our data show while the recruitment of ZapA/FtsZ is gradual in the cell cycle, recruitment of FtsN is rapid and begins at about the onset of constriction. At this time, the fraction of ZapA/FtsZ in the Z ring approaches its peak value. We also find a second increase in FtsN recruitment to the divisome, which begins once the amount of ZapA/FtsZ at midcell starts decreasing. Increasing hypermorphic FtsA* (FtsA R286W), but not FtsA, accelerates FtsN recruitment but not constriction. This finding is consistent with FtsA* recruiting FtsN with some other divisome component being rate-limiting for constriction under these conditions. Finally, our data support the recently proposed idea that ZapA/FtsZ and FtsN are part of physically separate complexes in midcell throughout the whole septation process.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRSDate not supplied

Small Spacecraft Sample Return to Support Gateway and Lunar Science

The Lunar Gateway is a planned orbital outpost to support Lunar surface, Cislunar, and deep space exploration activities. NASA, together with international and commercial partners, are providing various capabilities, infrastructure, and services to build the Lunar economy. As Gateway capabilities and transportation logistics evolve, utilization is expected to increase, providing ample science, technology demonstration, and commercial development opportunities. Elements of the transportation network supporting Lunar activities are primarily focused on the outbound segment, which include Commercial Lunar Payload Services (CLPS), Human Landing System (HLS), Deep Space Logistics (DSL), and the SLS/Orion crew transportation system. Initially, the only Earth return segment will be provided via Orion. However, infrequent mission cadence (once every 12 months), limited payload return mass (100 kg), and operational constraints suggest that additional sample return logistics capability will be needed. Sustaining long-term presence at the Moon will likely require innovative approaches for frequent and affordable payload return. NASA Ames Research Center and the DSL team at Kennedy Space Center (which provides the Gateway Logistics Services missions) have investigated the development of a small spacecraft-based sample return capability to complement Orion. The goal of the first mission is to demonstrate the capability as a part of an early DSL mission, and provide up to 10 kg of scientific sample return from the Gateway. The mission concept envisions the progressive addition of sample return capabilities, including returning temperature- and acceleration-sensitive payloads, and evolution into a commercially provided service, similar to existing ISS payload return logistics. An overview of payload science and technology use cases and small spacecraft mission concepts will be presented to engage scientists, payload developers and mission planners who are considering Lunar exploration activities that will require the return of high-value samples from the Gateway and/or the lunar surface.

Alan Cassell↗

NASA NTRSDate not supplied

Small Spacecraft Sample Return Mission Concept to Support Gateway and Lunar Science

The Lunar Gateway is a planned orbital outpost to support Lunar surface, Cislunar, and deep space exploration activities. NASA, together with international and commercial partners, are providing various capabilities, infrastructure, and services to build the Lunar economy. As Gateway capabilities and transportation logistics evolve, utilization is expected to increase, providing ample science, technology demonstration, and commercial development opportunities. Elements of the transportation network supporting Lunar activities are primarily focused on the outbound segment, which include Commercial Lunar Payload Services (CLPS), Human Landing System (HLS), Deep Space Logistics (DSL), and the SLS/Orion crew transportation system. Initially, the only Earth return segment will be provided via Orion. However, infrequent mission cadence (once every 12 months), limited payload return mass (100 kg), and operational constraints suggest that additional sample return logistics capability will be needed. Sustaining long-term presence at the Moon will likely require innovative approaches for frequent and affordable payload return. NASA Ames Research Center and the DSL team at Kennedy Space Center (which provides the Gateway Logistics Services missions) have investigated the development of a small spacecraft-based sample return capability to complement Orion. The goal of the first mission is to demonstrate the capability as a part of an early DSL mission, and provide up to 10 kg of scientific sample return from the Gateway. The mission concept envisions the progressive addition of sample return capabilities, including returning temperature- and acceleration-sensitive payloads, and evolution into a commercially provided service, similar to existing ISS payload return logistics. An overview of payload science and technology use cases and small spacecraft mission concepts will be presented to engage scientists, payload developers and mission planners who are considering Lunar exploration activities that will require the return of high-value samples from the Gateway and/or the lunar surface.

Small Spacecraft↗

NASA NTRSDate not supplied

Small Spacecraft Sample Return Mission Concept to Support Gateway and Lunar Science

The Lunar Gateway is a planned orbital outpost to support Lunar surface, Cislunar, and deep space exploration activities. NASA, together with international and commercial partners, are providing various capabilities, infrastructure, and services to build the Lunar economy. As Gateway capabilities and transportation logistics evolve, utilization is expected to increase, providing ample science, technology demonstration, and commercial development opportunities. Elements of the transportation network supporting Lunar activities are primarily focused on the outbound segment, which include Commercial Lunar Payload Services (CLPS), Human Landing System (HLS), Deep Space Logistics (DSL), and the SLS/Orion crew transportation system. Initially, the only Earth return segment will be provided via Orion. However, infrequent mission cadence (once every 12 months), limited payload return mass (100 kg), and operational constraints suggest that additional sample return logistics capability will be needed. Sustaining long-term presence at the Moon will likely require innovative approaches for frequent and affordable payload return. NASA Ames Research Center and the DSL team at Kennedy Space Center (which provides the Gateway Logistics Services missions) have investigated the development of a small spacecraft-based sample return capability to complement Orion. The goal of the first mission is to demonstrate the capability as a part of an early DSL mission, and provide up to 10 kg of scientific sample return from the Gateway. The mission concept envisions the progressive addition of sample return capabilities, including returning temperature- and acceleration-sensitive payloads, and evolution into a commercially provided service, similar to existing ISS payload return logistics. An overview of payload science and technology use cases and small spacecraft mission concepts will be presented to engage scientists, payload developers and mission planners who are considering Lunar exploration activities that will require the return of high-value samples from the Gateway and/or the lunar surface.

Alan Cassell↗

DOE OSTI2023

Three-dimensional nanoscale reduced-angle ptycho-tomographic imaging with deep learning (RAPID)

X-ray ptychographic tomography is a nondestructive method for three dimensional (3D) imaging with nanometer-sized resolvable features. The size of the volume that can be imaged is almost arbitrary, limited only by the penetration depth and the available scanning time. Here we present a method that rapidly accelerates the imaging operation over a given volume through acquiring a limited set of data via large angular reduction and compensating for the resulting ill-posedness through deeply learned priors. The proposed 3D reconstruction method “RAPID” relies initially on a subset of the object measured with the nominal number of required illumination angles and treats the reconstructions from the conventional two-step approach as ground truth. It is then trained to reproduce equal fidelity from much fewer angles. After training, it performs with similar fidelity on the hitherto unexamined portions of the object, previously not shown during training, with a limited set of acquisitions. In our experimental demonstration, the nominal number of angles was 349 and the reduced number of angles was 21, resulting in a x140 aggregate speedup over a volume of 4.48 x 93.18 x 3.92 μm 3 and with (14nm) 3 feature size, i.e. ~ 10 8 voxels. RAPID’s key distinguishing feature over earlier attempts is the incorporation of atrous spatial pyramid pooling modules into the deep neural network framework in an anisotropic way. We found that adjusting the atrous rate improves reconstruction fidelity because it expands the convolutional kernels’ range to match the physics of multi-slice ptychography without significantly increasing the number of parameters.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Parallel sorting algorithm classification: is manual instrumentation necessary?

Understanding parallel algorithms is crucial for accelerating scientific simulations on complex, distributed memory, high-performance computers. Modern algorithm classification approaches learn semantics directly from source code to differentiate between algorithms, however, accessing source code is not always possible. We can learn about parallel algorithms from observing their performance, as programs running the same algorithms and using the same hardware should exhibit similar performance characteristics. We present an approach to learn algorithm classes from parallel performance data directly in order to classify algorithms without access to the source code. We extend previous work to enable classifying parallel sorting algorithms using automatic instrumentation instead of requiring manual region annotations in the source code. In this work, we design and demonstrate a study for classification of parallel sorting algorithms using parallel performance data collected from automatic instrumentation, and evaluate the performance of our new methodology on classification. We leverage Caliper to collect the performance data, Thicket for our exploratory data analysis (EDA), and PyTorch and Scikit-learn to evaluate the effectiveness of random forests, support vector machines (SVMs), decision trees, neural networks, and logistic regressions on parallel performance data. Additionally, we study noise in parallel performance data, whether the removal of noise and pre-processing of the data is necessary to accurately classify parallel sorting algorithms, and determine the effectiveness of features created from performance data. In conclusion, we demonstrate classification accuracy for these five different models of up to 97.7% across four different parallel algorithm classes.

Algorithm Classification↗

DOE OSTI2020

Advanced data science toolkit for non-data scientists – A user guide

Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Here in this paper, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS1999

Work on Planetary Atmospheres and Planetary Atmosphere Probes

A summary final report of work accomplished is presented. Work was performed in the following areas: (1) Galileo Probe science analysis, (2) Galileo probe Atmosphere Structure Instrument, (3) Mars Pathfinder Atmosphere Structure/Meteorology instrument, (4) Mars Pathfinder data analysis, (5) Science Definition for future Mars missions, (6) Viking Lander data analysis, (7) winds in Mars atmosphere Venus atmospheric dynamics, (8) Pioneer Venus Probe data analysis, (9) Pioneer Venus anomaly analysis, (10) Discovery Venus Probe Titan probe instrument design, and (11) laboratory studies of Titan probe impact phenomena. The work has resulted in more than 10 articles published in archive journals, 2 encyclopedia articles, and many working papers. This final report is organized around the four planets on which there was activity, Jupiter, Mars, Venus, and Titan, with a closing section on Miscellaneous Activities. A major objective was to complete the fabrication, test, and evaluation of the atmosphere structure experiment on the Galileo probe, and to receive, analyze and interpret data received from the spacecraft. The instrument was launched on April 14, 1989. Calibration data were taken for all experiment sensors. The data were analyzed, fitted with algorithms, and summarized in a calibration report for use in analyzing and interpreting data returned from Jupiter's atmosphere. The sensors included were the primary science pressure, temperature and acceleration sensors, and the supporting engineering temperature sensors. Computer programs were written to decode the Experiment Data Record and convert the digital numbers to physical quantities, i.e., temperatures, pressures, and accelerations. The project office agreed to obtain telemetry of checkout data from the probe. Work to extend programs written for use on the Pioneer Venus project included: (1) massive heat shield ablation leading to important mass loss during entry; and (2) rapid planet rotation, which introduced terms of motion not needed on Venus. When the Galileo Probe encountered Jupiter, analysis and interpretation of data commenced. The early contributions of the experiment were to define (1) the basic structure of the deep atmosphere, (2) the stability of the atmosphere, (3) the upper atmospheric profiles of density, pressure, and temperature. The next major task in the Galileo Probe project was to refine, verify and extend the analysis of the data. It was the verified, and corrected data, which indicated a dry abiabatic atmosphere within measurement accuracy. Temperature in the thermosphere was measured at 900 K. Participation in the Mars atmospheric research included: (1) work as a team member of the Mars Atmosphere Working Group, (2) contribution to the Mars Exobiology Instrument workshop, (3) asssistance in planning the Mars global network and (4) assitance in planning the Soviet-French Mars mission in 1994. This included a return to the Viking Lander parachute data to refine and improve the definition of winds between 1.5 and 4 kilometer altitude at the two entry sites. The variability of the structure of Mars atmosphere was addressed, which is known to vary with season, latitude, hemisphere and dust loading of the atmosphere. This led to work on the Pathfinder project. The probe had a deployable meteorology mast that had three temperature sensors, and a wind sensor at the tip of the mast. Work on the Titan atmospheric probe was also accomplished. This included developing an experiment proposal to the European Space Agency (ESA), which was not selected. However, as an advisor in the design and preparation of the selected experiment the researcher interacted with scientist on the Huygens Probe Atmosphere Structure Experiment. The researcher also participated in the planning for the Venus Chemical Probe. The science objectives of the probe were to resolve unanswered questions concerning the minor species chemistry of Venus' atmosphere that control cloud formation, greenhouse effectiveness, and the thermal structure. The researcher also reviewed problems with the Pioneer Venus Probe, that caused anomalies which occurred on the Probes at and below 12.5 km level of the Venus' atmosphere. He convened and participated in a workshop that concluded the most likely hardware cause was insulation failure in the electrical harness outside the Probes' pressure vessels. It was discovered that the shrink tubing material failed at 600K. This failure could explain the anomalies experienced by the probes. The descent data of the Pioneer probes, and the Soviet Vega Lander was analyzed to evaluate the presence of small scale gravity waves in and below the Venus cloud layer.

Lester, Peter↗

DOE OSTI2025

Shock propagation in aerogel and TPP foams for inertial fusion energy target design

Achieving practical inertial fusion energy (IFE) requires the development of target designs with well-characterized microstructure and compression response. We measured shock dynamics in low-density (17.5–500 mg/cm 3 ) aerogel and two-photon polymerization (TPP) foams using x-ray phase contrast imaging (XPCI) methods and the Velocity Interferometer System for Any Reflector. By analyzing shock front evolution, we examined how target type and density influence shock propagation and energy dissipation. Talbot-XPCI shows that aerogels support a smooth, bowed shock front due to their homogeneous nanometer-scale pore network. In contrast, TPP foams exhibit irregular, stepwise propagation driven by interactions with their periodic micrometer-scale lattice. Shock velocity follows a power-law relation: aerogels deviate from classical ρ −1/2 scaling due to pore-collapse dissipation, while TPP foams follow the trend with larger uncertainties from density variations. Comparisons with xRAGE simulations reveal systematic underestimation of shock speeds. These results provide the first experimental constraints on shock propagation in TPP foams over a wide density range and highlight the influence of internal structure on anisotropic shock behavior. Our findings support improved benchmarking of EOS and hydrodynamic models and inform the design of foam architectures that promote implosion symmetry in IFE capsules.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Investigating parameter trainability in the SNAP-displacement protocol of a qudit system

Here, in this study, we explore the universality of Selective Number-dependent Arbitrary Phase (SNAP) and Displacement gates for quantum control in qudit-based systems. However, optimizing the parameters of these gates poses a challenging task. Our main focus is to investigate the sensitivity of training any of the SNAP parameters in the SNAP-Displacement protocol. We analyze conditions that could potentially lead to the Barren Plateau problem in a qudit system and draw comparisons with multi-qubit systems. The parameterized ansatz we consider consists of blocks, where each block is composed of hardware operations, namely SNAP and Displacement gates [Fösel et al 2020 Efficient cavity control with snap gates arXiv:2004.14256]. Applying Variational Quantum algorithm (VQA) with observable and gate cost functions, we utilize techniques similar to those in [McClean et al 2018 Barren plateaus in quantum neural network training landscapes Nat. Commun.9 1–6] and [Cerezo et al 2021 Cost function dependent barren plateaus in shallow parametrized quantum circuits. Nat. Commun.12 1–12] along with the concept of t − design. Through this analysis, we make the following key observations: (a) The trainability of a SNAP-parameter does not exhibit a preference for any particular direction within our cost function landscape, (b) By leveraging the first and second moments properties of Haar measures, we establish new lemmas concerning the expectation of certain polynomial functions, and (c) utilizing these new lemmas, we identify a general condition that indicates an expected trainability advantage in a qudit system when compared to multi-qubit systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗