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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19

Demonstration of event position reconstruction based on diffusion in the NEXT-white detector

Noble element time projection chambers are a leading technology for rare event detection in physics, such as for dark matter and neutrinoless double beta decay searches. Time projection chambers typically assign event position in the drift direction using the relative timing of prompt scintillation and delayed charge collection signals, allowing for reconstruction of an absolute position in the drift direction. In this paper, alternate methods for assigning event drift dis tance via quantification of electron diffusion in a pure high pressure xenon gas time projection chamber are explored. Data from the NEXT-White detector demonstrate the ability to achieve good position assignment accuracy for both high and low-energy events. Using point-like energy deposits from 83m Kr calibration electron captures (E ~ 45 keV), the position of origin of low-energy events is determined to 2 cm precision with bias < 1 mm. A convolutional neural network approach is then used to quantify diffusion for longer tracks (E ≥ 1.5 MeV), from radiogenic electrons, yielding a precision of 3 cm on the event barycenter. The precision achieved with these methods indicates the feasibility energy calibrations of better than 1% FWHM at Q ββ in pure xenon, as well as the potential for event fiducialization in large future detectors using an alternate method that does not rely on primary scintillation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Function Preserving Projection

With function preserving projection (FPP), we aim to address high-dimensional data exploration challenges common in many scientific applications. Instead of preserving the global or local geometric structure of a dataset like traditional dimensionality reduction methods (e.g., principal component analysis, t-stochastic neighborhood embedding), the proposed approach finds a linear projection that reveal interpretable (yet potentially nonlinear) pattern of the properties of interests defined on a highdimensional domain (e.g., simulation yield defined on the input parameter space). The key insight driving FPP is the fact that while non-linear embeddings are difficult to interpret, humans are highly skilled in understanding complex visual patterns. FPP lets users select the complexity of a pattern they consider interpretable, allows to find a joint projection that explains multiple functions, and provides a confidence statistic (akin to a p-value) indicating how salient the resulting pattern is. We demonstrate the efficacy of the proposed method on several synthetic and scientific datasets for both continuous (regression) and discrete (classification) problems of interests and illustrate the scalability of FPP by finding an optimal projection for close to 40 gigabytes in around 10 minutes.

Liu, SHUSEN↗

ErrFilter

ErrFilter is a tool that allows an analyst to explore data by defining simple Boolean filters on data. The tool also allows the user to sort the results by any attribute. The user sees both the distribution of filtered data as well as selected examples. The tool was designed to be applied generally to multivariate data associated with machine learning models.

Arendt, Dustin↗

datasight [SWR-26-045]

This software is an AI-powered data exploration with natural language. datasight connects an AI agent to your database and provides a web UI where you can ask questions in natural language. The agent writes SQL, runs queries, and generates interactive Plotly visualizations. Supports DuckDB, PostgreSQL, SQLite, and Flight SQL databases. Also queries local CSV and Parquet files directly — no database setup required. Supports Anthropic Claude (default), GitHub Models (open source), and Ollama (local) as LLM backends.

Thom, Daniel [National Laboratory of the Rockies (↗

Quarterly Report - May through July 2012

The first quarter of my postgraduate internship has been an extremely varied one, and one which I have tackled several different aspects of the project. Because this is the beginning of a new investigation for the Research Library, I think it is appropriate that I explore data management at LANL from multiple perspectives.

59 BASIC BIOLOGICAL SCIENCES↗

Argonne Leadership Computing Facility 2011 annual report: Shaping future supercomputing

The ALCF's Early Science Program aims to prepare key applications for the architecture and scale of Mira and to solidify libraries and infrastructure that will pave the way for other future production applications. Two billion core-hours have been allocated to 16 Early Science projects on Mira. The projects, in addition to promising delivery of exciting new science, are all based on state-of-the-art, petascale, parallel applications. The project teams, in collaboration with ALCF staff and IBM, have undertaken intensive efforts to adapt their software to take advantage of Mira's Blue Gene/Q architecture, which, in a number of ways, is a precursor to future high-performance-computing architecture. The Argonne Leadership Computing Facility (ALCF) enables transformative science that solves some of the most difficult challenges in biology, chemistry, energy, climate, materials, physics, and other scientific realms. Users partnering with ALCF staff have reached research milestones previously unattainable, due to the ALCF's world-class supercomputing resources and expertise in computation science. In 2011, the ALCF's commitment to providing outstanding science and leadership-class resources was honored with several prestigious awards. Research on multiscale brain blood flow simulations was named a Gordon Bell Prize finalist. Intrepid, the ALCF's BG/P system, ranked No. 1 on the Graph 500 list for the second consecutive year. The next-generation BG/Q prototype again topped the Green500 list. Skilled experts at the ALCF enable researchers to conduct breakthrough science on the Blue Gene system in key ways. The Catalyst Team matches project PIs with experienced computational scientists to maximize and accelerate research in their specific scientific domains. The Performance Engineering Team facilitates the effective use of applications on the Blue Gene system by assessing and improving the algorithms used by applications and the techniques used to implement those algorithms. The Data Analytics and Visualization Team lends expertise in tools and methods for high-performance, post-processing of large datasets, interactive data exploration, batch visualization, and production visualization. The Operations Team ensures that system hardware and software work reliably and optimally; system tools are matched to the unique system architectures and scale of ALCF resources; the entire system software stack works smoothly together; and I/O performance issues, bug fixes, and requests for system software are addressed. The User Services and Outreach Team offers frontline services and support to existing and potential ALCF users. The team also provides marketing and outreach to users, DOE, and the broader community.

99 GENERAL AND MISCELLANEOUS↗

SISGR: Linking Ion Solvation and Lithium Battery Electrolyte Properties

The solvation and phase behavior of the model battery electrolyte salt lithium trifluoromethanesulfonate (LiCF 3 SO 3 ) in commonly used organic solvents; ethylene carbonate (EC), gamma-butyrolactone (GBL), and propylene carbonate (PC) was explored. Data from differential scanning calorimetry (DSC), Raman spectroscopy, and X-ray diffraction were correlated to provide insight into the solvation states present within a sample mixture. Data from DSC analyses allowed the construction of phase diagrams for each solvent system. Raman spectroscopy enabled the determination of specific solvation states present within a solvent-salt mixture, and X-ray diffraction data provided exact information concerning the structure of a solvates that could be isolated Thermal analysis of the various solvent-salt mixtures revealed the phase behavior of the model electrolytes was strongly dependent on solvent symmetry. The point groups of the solvents were (in order from high to low symmetry): C2V for EC, CS for GBL, and C1 for PC(R). The low symmetry solvents exhibited a crystallinity gap that increased as solvent symmetry decreased; no gap was observed for EC-LiTf, while a crystallinity gap was observed spanning 0.15 to 0.3 mole fraction for GBL-LiTf, and 0.1 to 0.33 mole fraction for PC(R)-LiTf mixtures. Raman analysis demonstrated the dominance of aggregated species in almost all solvent compositions. The AGG and CIP solvates represent the majority of the species in solutions for the more concentrated mixtures, and only in very dilute compositions does the SSIP solvate exist in significant amounts. Thus, the poor charge transport characteristics of CIP and AGG account for the low conductivity and transport properties of LiTf and explain why is a poor choice as a source of Li + ions in a Li-ion battery.

25 ENERGY STORAGE↗

Hedberg Research Conference on Fundamental Controls on Flow in Carbonates: Request for Travel Support for Post-Doctoral Fellows

Carbonate reservoirs pose a scientific and engineering challenge to geophysical prediction and monitoring of fluid flow in the subsurface. Difficulties in interpreting hydrological, reservoir and other exploration data arise because carbonates are composed of a hierarchy of geological structures, constituents and processes that span a wide spectrum of length and time scales. What makes this problem particularly challenging is that length scales associated with physical structure and processes are often not discrete, but overlap, preventing the definition of discrete elements at one scale to become the building blocks of the next scale. This is particularly true for carbonates where complicated depositional environments, subsequent post-deposition diagenesis and geochemical interactions result in pores that vary in scale from submicron to centimeters to fractures, variation in fabric composition with fossils, minerals and cement, as well as variations in structural features (e.g., oriented inter- and intra layered - interlaced bedding and/or discontinuous rock units). In addition, this complexity is altered by natural and anthropogenic processes such as changes in stress, fluid content, reactive fluid flow, etc. Thus an accurate geophysical assessment of the flow behavior of carbonate reservoirs requires a fundamental understanding of the interplay of textural and structural features subjected to physical processes that affect and occur on various length and time scales. To address this complexity related to carbonates, a Hedberg conference on “Fundamental Controls on Flow in Carbonates” was held July 8 to 13, 2012, to bring together industry and academic scientists to stimulate innovative ideas that can accelerate research advances related to flow prediction and recovery in carbonate reservoirs. Participants included scientist and engineers from multiple disciplines (such as hydrology, structural geology, geochemistry, reservoir engineering, geophysics, geomechanics, numerical modeling, physical experiments, sedimentology, well-testing, statistics, mathematics, visualization, etc.) who encompass experience as well as the latest advances in these multi-faceted fields. One of the goals was to include early career scientists and engineers (post-doctoral fellows, assistant professors, etc.). With this grant 10 early career scientists and engineers were supported to attend the conference. This reports contains a brief overview of the conference and the list of support participants supported by this grant. Full details of the outcomes of the conference are given in the publication found in the Attachment section of this report.

58 GEOSCIENCES↗

Building a Community-Focused Data Platform

The National Data Service (NDS) presents a broad-reaching vision of integrating discovery, publishing, and sharing of data. Metcalfe's Law states that the value of a network increases with the number of connections in the network, not the number of nodes. The NDS must develop and steward connections not only between data, as is in its vision statement, but between individuals as well. We propose that this be accomplished by explicitly developing it as a platform on which data exploration can be constructed by individual researchers.

97 MATHEMATICS AND COMPUTING↗

Characterization of Extremes and Compound Impacts: Applications of Machine Learning and Interpretable Neural Networks

Focal Area: This white paper responds to Focal area III by exploring data fusion, learning and explainable AI methods in characterizing hydrological extremes and interconnections. It also addresses Focal area II by using probabilistic AI and ensemble ML for predicting extremes and compound extremes. Science Challenge: A key question associated with the integrated water (or hydrological) cycle grand challenge in the Earth and Environmental Systems Sciences Division (EESSD) strategic plan, is how the frequency and intensity of hydrological events will change. Prediction of the tail behavior (extremes) of the hydrological cycle is especially challenging, because of their stochasticity and low probability. These extreme events and their compound impacts have significant societal and economic consequences. It is anticipated for the next-generation Earth System models (ESMs), that model predictability of the water cycle will improve with increased resolution (e.g., regionally refined E3SM), advanced software and computational architectures, and improved model physics based on the data from ARM measurements and high-fidelity models. However, the challenges for predictability of low-probability high-impact extreme events will unlikely be alleviated with conventional modeling and data-driven approaches, as ESMs are calibrated largely for capturing the high-frequency mean climate states. Recent AI and ML applications have shown great potential in quantifying well-defined climate extremes (e.g., supervised learning of tropical cyclones/atmospheric rivers by ClimateNet1) but few efforts are dedicated to compound events, extreme drivers and uncertainty estimation. We envision the opportunity to develop and apply ML and interpretable AI methods extended on the existing efforts, specifically, for: (1) identification of compound extremes, (2) diagnosing drivers of extremes, (3) bias correction in extreme predictions and (4) probabilistic modeling of extremes.

54 ENVIRONMENTAL SCIENCES↗

Tools for Water Ingress Testing

The Safety Storage and Engineering Team, as part of the Production Support Services division (PSS-2), is tasked with ensuring the safety of containers used for handling and storage of nuclear materials. As part of this work, water ingress tests are conducted to evaluate the water-tightness of containers intended for in-glovebox use. In collaboration, the statistics group of the Computer and Computational Sciences Division (CCS-6) provided support in developing a statistically defensible approach for determining appropriate sample sizes for water ingress testing. Water ingress testing involves multiple measurements on multiple containers. Our approach uses a simple random effects model to analyze a pilot data set, implementing prediction limits to evaluate the efficacy of collecting additional data. Although this study capitalizes on available data, our approach can be used with estimates of the ratio of between and within variability and average values, often available from past testing or expert knowledge. An interactive Shiny tool was developed as a final user-friendly product for future testing. The Shiny interface is an open-source package providing a framework for building web applications. Raw data exploration and prediction interval-based sample size assessments can quickly be conducted by the engineering team without needing to interact with the underlying code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Carbon Storage Site Mapping Inquiry Tool (MapIT)

The Carbon Storage Site Mapping Inquiry Tool (MapIT) is an online web mapping tool designed to help users discover available public-sourced data to facilitate data exploration in support of Underground Injection Control (UIC) Program Class VI Well Site permitting for the geologic sequestration of carbon dioxide.

Pantaleone, Scott↗

Identification of Preferential Recharge Zones in Karst Systems Based on the Correlation between the Spring Level and Precipitation: A Case Study from Jinan Spring Basin

The Jinan spring basin is located in the karst area of northern China, where springs serve as important sources of water supply. Several studies on spring protection and water supply have been carried out, and scholars have developed some laws on local groundwater flow dynamic and characteristics of aquifer structures. Unfortunately, there is a lack of detailed research on preferential recharge zones, which are the main recharge pathways of springs. Therefore, this research focuses on identifying preferential recharge zones based on the correlation between the spring level and precipitation. The results show that when precipitation is more intense or lasts longer, there is a stronger correlation between spring level and precipitation. It has been established that the precipitation at Donghongmiao station has the closest relationship with the dynamic of Baotu spring, which is found to be the most significant contribution to spring preservation. Two potential preferential recharge zones in the Jinan spring basin are detected through correlation analysis and geological exploration data. These findings support spring protection and water supply projects in karst regions.

58 GEOSCIENCES↗

STUDY OF A SUDDEN QPO TRANSITION EVENT IN THE BLACK HOLE SOURCE XTE J1550-564

The transition of type B/A quasi-periodic oscillations (QPOs) is often associated with a sudden flux change at the peak of the outburst in black hole X-ray binaries. Recent studies show that these variations are connected to the jet ejection phenomenon in the inner regions of the accretion disk. One such event of the black hole source XTE J1550-564 is investigated, where a type A QPO transforms to type B in the first transition. In the second transition, a notable increase in the flux was observed in the 13–60 keV band. The dynamical energy-dependent power density spectrum and broadband energy spectrum obtained using the Rossi X-ray Timing Explorer data of this event were inspected to understand the energetics of the inner accretion disk. During these transitions, both disk and corona parameters were found to be varying along with their corresponding fluxes. We discuss the plasma ejection model scenario in the context of the first transition and the condensation of the corona in the inner region for the second transition event. Spectral results were used to derive the critical mass accretion rate, the amount of condensation, and the critical luminosity needed for the condensation process. We found the results to be in well agreement with theoretical simulations.

79 ASTRONOMY AND ASTROPHYSICS↗

Keck/mosfire spectroscopy of z = 7-8 galaxies: Lyα emission from a galaxy at z = 7.66

We report the results from some of the deepest Keck/Multi-Object Spectrometer For Infra-Red Exploration data yet obtained for candidate z ≳ 7 galaxies. Our data show one significant line detection with 6.5σ significance in our combined 10 hr of integration which is independently detected on more than one night, thus ruling out the possibility that the detection is spurious. The asymmetric line profile and non-detection in the optical bands strongly imply that the detected line is Lyα emission from a galaxy at z(Lyα) = 7.6637 ± 0.0011, making it the fourth spectroscopically confirmed galaxy via Lyα at z > 7.5. This galaxy is bright in the rest-frame ultraviolet (UV; M {sub UV} ∼ −21.2) with a moderately blue UV slope (β=−2.2{sub −0.2}{sup +0.3}), and exhibits a rest-frame Lyα equivalent width of EW(Lyα) ∼ 15.6{sub −3.6}{sup +5.9} Å. The non-detection of the 11 other z ∼ 7–8 galaxies in our long 10 hr integration, reaching a median 5σ sensitivity of 28 Å in the rest-frame EW(Lyα), implies a 1.3σ deviation from the null hypothesis of a non-evolving distribution in the rest-frame EW(Lyα) between 3 < z < 6 and z = 7–8. Our results are consistent with previous studies finding a decline in Lyα emission at z > 6.5, which may signal the evolving neutral fraction in the intergalactic medium at the end of the reionization epoch, although our weak evidence suggests the need for a larger statistical sample to allow for a more robust conclusion.

79 ASTRONOMY AND ASTROPHYSICS↗

Banyan. VIII. New low-mass stars and brown dwarfs with candidate circumstellar disks

We present the results of a search for new circumstellar disks around low-mass stars and brown dwarfs with spectral types >K5 that are confirmed or candidate members of nearby young moving groups. Our search input sample was drawn from the BANYAN surveys of Malo et al. and Gagné et al. Two Micron All-Sky Survey and Wide-field Infrared Survey Explorer data were used to detect near- to mid-infrared excesses that would reveal the presence of circumstellar disks. A total of 13 targets with convincing excesses were identified: 4 are new and 9 were already known in the literature. The new candidates are 2MASS J05010082–4337102 (M4.5), J08561384–1342242 (M8γ), J12474428–3816464 (M9γ), and J02265658–5327032 (L0δ); they are candidate members of the TW Hya (∼10±3 Myr), Columba (∼42{sub −4}{sup +6} Myr), and Tucana-Horologium (∼45±4 Myr) associations, with masses of 120 and 13–18 M{sub Jup}. The M8–L0 objects in Columba and Tucana-Horologium are potentially among the first substellar disk systems aged ∼40 Myr. Estimates of the new candidates’ mean disk temperatures and fractional luminosities are in the ranges ∼135--520 K and 0.021--0.15, respectively. New optical spectroscopy of J0501–4337 reveals strong Hα emission, possibly indicating ongoing accretion, provides a detection of lithium absorption, and shows a radial velocity measurement that is consistent with a membership to Columba. We also present a near-infrared spectrum of J0226–5327 that reveals Paschen β emission and shows signs of low surface gravity, consistent with accretion from a disk and a young age.

79 ASTRONOMY AND ASTROPHYSICS↗

Spectral Properties of Globally Distributed ENA Fluxes across Diverse Regions of the Heliosphere

This study analyzes energetic neutral atom (ENA) spectral properties across distinct regions of globally distributed flux (GDF) sky maps, using Interstellar Boundary Explorer data from a full solar cycle, corrected for time dispersion. By time-shifting the data to the heliosheath using GDF source distances from D. B. Reisenfeld et al., we achieve a more accurate representation of heliosheath GDF energy spectra. We quantify ENA spectral characteristics, heliosheath line-of-sight-integrated proton pressure, and heliosheath proton temperature, comparing these to solar wind properties at 1 au and interplanetary scintillation-derived solar wind data. Our findings show that the spectral index is generally anticorrelated with heliosheath proton temperature and pressure, except in the central tail, where a partial positive correlation is observed. The lowest spectral index values occur when high-latitude heliosheath regions are dominated by fast solar wind from polar coronal holes. The south pole exhibits the flattest energy spectra due to plasma heating from both fast solar wind and a late-2014 pressure pulse. The central tail shows shorter variability (5–6 yr) for spectral index and heliosheath proton temperature, while proton pressure follows the 11 yr solar cycle. Most spectral shapes exhibit a “knee” distribution, peaking during solar maximum, with an “ankle” shape observed only at the south pole during solar cycle transitions. Asymmetry in proton pressure in the lobes is driven by the draping effect of the local interstellar magnetic field. This study provides insights into the energetic properties of GDF across the heliosphere, enhancing our understanding of the heliospheric environment.

79 ASTRONOMY AND ASTROPHYSICS↗

DELVE Milky Way Satellite Galaxy Census. I. Satellite Population and Survey Selection Function in DES, DELVE, and Pan-STARRS

The properties of Milky Way satellite galaxies have important implications for galaxy formation, reionization, and the fundamental physics of dark matter. However, the population of Milky Way satellites includes the faintest known galaxies, and current observations are incomplete. To understand the impact of observational selection effects on the known satellite population, we perform rigorous, quantitative estimates of the Milky Way satellite galaxy detection efficiency in three wide-field survey datasets: the Dark Energy Survey Year 6, the DECam Local Volume Exploration Data Release 3, and the Pan-STARRS1 Data Release 1. Together, these surveys cover ∼13,600 deg 2 to g ∼ 24.0 and ∼27,700 deg 2 to g ∼ 22.5, spanning ∼91% of the high-Galactic-latitude sky (∣b∣ ≥ 15°). We apply multiple detection algorithms over the combined footprint and recover 49 known satellites above a strict census detection threshold. To characterize the sensitivity of our census, we run our detection algorithms on a large set of simulated galaxies injected into the survey data, which allows us to develop models that predict the detectability of satellites as a function of their properties. We then fit an empirical model to our data and infer the luminosity function, radial distribution, and size–luminosity relation of Milky Way satellite galaxies. Our empirical model predicts a total of $265^{+79}_{-47}$ satellite galaxies with −20 ≤ M V ≤ 0, half-light radii of 15 ≤ r 1/2 , (pc) ≤ 3000, and galactocentric distances of 10 ≤ D GC (kpc) ≤ 300. We also identify a mild anisotropy in the angular distribution of the observed galaxies, at a significance of ∼2σ, which can be attributed to the clustering of satellites associated with the LMC.

Tan, Chin Yi [Univ. of Chicago, IL (United States)↗