SEARCH · Search NASA

Results for “Correlation Analysis”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Cosmological Constraints from Combining Photometric Galaxy Surveys and Gravitational Wave Observatories

Spatial variations in survey properties due to selection effects generate substantial systematic errors in large-scale structure measurements in optical galaxy surveys on very large scales. On such scales, the statistical sensitivity of optical surveys is also limited by their finite sky coverage. By contrast, gravitational wave (GW) sources appear to be relatively free of these issues, provided the angular sensitivity of GW experiments can be accurately characterized. We quantify the expected cosmological information gain from combining the forecast LSST 3$\times$2pt analysis (combination of three 2-point correlations of galaxy density and weak lensing shear fields) with the large-scale auto-correlation of GW sources from proposed next-generation GW experiments. We find that in $\Lambda$CDM and $w$CDM models, there is no significant improvement in cosmological constraints from combining GW with LSST 3$\times$2pt over LSST alone, due to the large shot noise for the former; however, this combination does enable a $\sim6\%$ constraint on the linear galaxy bias of GW sources. More interestingly, the optical-GW data combination provides tight constraints on models with primordial non-Gaussianity (PNG), due to the predicted scale-dependent bias in PNG models on large scales. Assuming that the largest angular scales that LSST will probe are comparable to those in Stage III surveys ($\ell_{\rm min}\sim50$), the inclusion of next-generation GW measurements could improve constraints on the PNG parameter $f_{\rm NL}$ by up to a factor of $\simeq6.6$ compared to LSST alone, yielding $\sigma(f_{\rm NL})=8.5$. These results assume the expected capability of a network of Einstein Telescope-like GW observatories, with a detection rate of $10^6$ events/year. We investigate the sensitivity of our results to different assumptions about future GW detectors as well as different LSST analysis choices.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Review of High‐Speed Digital Image Correlation: Advancements and Good Practices

This paper reviews the current state of the art in high‐speed (HS) and ultrahigh‐speed (UHS) digital image correlation (DIC) techniques, emphasizing their critical role in experimental research across various scientific domains. HS and UHS DIC have evolved significantly, driven by advancements in camera systems, image processing algorithms and experimental methodologies. These developments have opened new avenues for capturing and analysing dynamic events with unprecedented temporal and spatial detail, but not without introducing challenges such as optical distortions, motion blur and lighting issues that can affect measurement quality. This review advocates for standardized reporting practices in HS/UHS DIC methodologies to improve reproducibility and reliability across studies, drawing on guidelines from the International Digital Image Correlation Society (iDICs). Through a comprehensive analysis of over 150 articles, this review identifies key advancements in imaging technology and their application in six research domains: material characterization, test development, fracture mechanics, model validation, ballistic and explosive phenomena assessment and measurement uncertainties. Distinctions between two‐dimensional (2D) and stereo‐DIC applications are explored, offering insights into their practical implementation and trade‐offs. Good practices for HS/UHS DIC applications are proposed along with suggestions for future directions for this evolving field, highlighting the indispensable role of technological innovation in expanding the capabilities of optical metrology.

experimental mechanics↗

DOE OSTI2025

Sample screening of uranium ore concentrates using portable spectrophotometers: investigating the correlation between visible colors and chemical signatures

A research collaboration between the Japan Atomic Energy Agency and the Department of Energy’s National Nuclear Security Administration examined nuclear forensic signatures and analytical methods for tracing the origins of uranium ore concentrates (UOCs). Here, this study focuses on utilizing portable spectrophotometers capable of reflectance measurements in the visible light spectrum as a potential rapid screening tool for nuclear forensics analysis. Unlike laboratory-based near-infrared spectroscopy or digital image analysis, this research investigated the potential to correlate visible color measurements with key nuclear forensics signatures using seven types of UOC samples with known origins and three UOC certified reference materials. Results demonstrated that distinct color groups, quantified using CIELAB values, correlated with major uranium compounds. Furthermore, the findings indicated that trace elements can influence the UOC colors, providing additional insights into material characteristics. Although this approach requires further validation across a broader range of UOC species, this study demonstrated that simple colorimetric analysis using visible spectrophotometry, which does not require complex sample preparation or data processing, can serve as a practical and novel rapid tool for preliminary screening and attribution in nuclear forensics investigations.

and nuclear chemistry↗

DOE OSTI2026

Affine Transformations to Enable Machine Learning for Semi-Quantitative EDS Analysis

Energy Dispersive X-ray Spectroscopy (EDS) is an essential technique for determining elemental concentrations and distributions within microstructures, critical for materials discovery, optimization, and qualification. However, most published EDS data is qualitative because current quantitative EDS analysis methods require extensive calibration and post-processing, limiting their practicality and widespread adoption. This work seeks to establish a framework for accelerated EDS characterization and spectrum analysis that can leverage ML to analyze correlations between various elemental compositions and resulting EDS spectra. The complex physics and data result in a high-dimensional problem that grows exponentially with the number of elements in the system and the complexity of the spectrum analysis. ML provides a way to compute and optimize the results of this highly dimensional problem in a flexible way to tailor it to the user’s specific needs and material system. However, the framework emphasizes transparency through a strictly mathematical affine transformation, so the analysis remains understandable and reviewable to facilitate adoption by the scientific community. While currently implemented methods are simplistic and unvalidated, further development and demonstration of this framework could enable high-throughput, accurate, and accessible EDS characterization.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Lightfall v0.0.1

Lightfall is a desktop application for synchrotron beamline instrument control, data acquisition, and live analysis at the Advanced Light Source (ALS). Built on Python and Qt, it provides a native graphical interface for operating beamline hardware, configuring and executing experimental scans, and visualizing results in real time. Key features include direct integration with EPICS control systems, a built-in electronic logbook, remote beamline access over secure tunnels, and an interprocess communication (IPC) architecture that coordinates with external analysis applications via ZMQ and EPICS process variables. This IPC approach allows Lightfall to orchestrate specialized analysis tools—including GPU-accelerated streaming correlators—without embedding them, avoiding the dependency conflicts common in monolithic scientific software platforms. Compared to prior approaches such as Xi-CAM's plugin-based architecture, Lightfall's design cleanly separates instrument control from domain-specific analysis, enabling feedback-driven acquisition where live analysis results can adjust scan parameters during an experiment. Its native Qt interface provides responsive performance for real-time data visualization that web-based alternatives struggle to match. Lightfall is designed for use by beamline scientists and staff operating synchrotron instruments at national user facilities.

Pandolfi, Ronald [Lawrence Berkeley National Labor↗

DOE OSTI2024

Robustness of quantum correlation in quantum energy teleportation

In this article, we explore a feedback-control protocol in different quantum field theories (QFTs) to study the quantum correlation in nonunitary evolution of quantum systems. Traditional studies on QFTs focus on quantum entanglement of pure states under the unitary evolution, however, we examine quantum correlation in mixed states using quantum energy teleportation (QET), which is an energy transfer protocol by utilizing ground state entanglement, and introduce quantum discord as a measure. QET involves a midcircuit measurement, which disrupts pure state entanglement. Despite this, our analysis demonstrates that quantum discord maintains the correlation throughout the QET process. We conducted numerical analyses with benchmark models including the Nambu-Jona-Lasinio (NJL) model, revealing that quantum discord consistently acts as an order parameter for phase transitions. The model is extended in a way that it has both the chiral chemical potential and the chemical potential, which are useful to study the phase structures mimicking the chiral imbalance between left- and right- quarks coupled to the chirality density operator. In all cases we studied, the quantum discord behaved as an order parameter of the phase transition. Published by the American Physical Society 2024

Ikeda, Kazuki (ORCID:0000000338212669)↗

DOE OSTI2025

Multi-channel, multi-template event reconstruction for SuperCDMS data using machine learning

SuperCDMS SNOLAB uses kilogram-scale germanium and silicon detectors to search for dark matter. Each detector has Transition Edge Sensors (TESs) patterned on the top and bottom faces of a large crystal substrate, with the TESs electrically grouped into six phonon readout channels per face. Noise correlations are expected among a detector's readout channels, in part because the channels and their readout electronics are located in close proximity to one another. Moreover, owing to the large size of the detectors, energy deposits can produce vastly different phonon propagation patterns depending on their location in the substrate, resulting in a strong position dependence in the readout-channel pulse shapes. Both of these effects can degrade the energy resolution and consequently diminish the dark matter search sensitivity of the experiment if not accounted for properly. We present a new algorithm for pulse reconstruction, mathematically formulated to take into account correlated noise and pulse shape variations. This new algorithm fits N readout channels with a superposition of M pulse templates simultaneously - hence termed the N$\times$M filter. We describe a method to derive the pulse templates using principal component analysis (PCA) and to extract energy and position information using a gradient boosted decision tree (GBDT). We show that these new N$\times$M and GBDT analysis tools can reduce the impact from correlated noise sources while improving the reconstructed energy resolution for simulated mono-energetic events by more than a factor of three and for the 71Ge K-shell electron-capture peak recoils measured in a previous version of SuperCDMS called CDMSlite to $<$ 50 eV from the previously published value of $\sim$100 eV. These results lay the groundwork for position reconstruction in SuperCDMS with the N$\times$M outputs.

Albakry, M. F. [British Columbia U.; TRIUMF]↗

DOE OSTI2026

Taming nuclear mass models with Gaussian processes

We propose a new set of nuclear mass predictions based on multiple theoretical mass models. By employing Gaussian process regression with the Matérn kernel, we achieved root-mean-square (rms) deviations below 100 keV for the training dataset. The best-performing mass models achieved rms deviations below 150 keV for the new precise mass data from AME2020, whereas the ensemble average showed robust performance across the nuclear chart. Our approach uniquely combines: (1) systematic refinement of eight mass models through their residuals, (2) physics-informed features, including magic numbers, nucleon parity numbers, neutron excess, and nuclear collectivity, and (3) theory-to-theory validation demonstrating robust extrapolation capability. We find that the Matérn kernel provides superior uncertainty quantification compared to the RBF kernel, with a length-scale analysis revealing enhanced inter-nuclei correlations. We provide complete mass predictions for all unknown nuclides in AME2020, offering valuable constraints for nuclear structure studies and astrophysical modeling when used with proper uncertainty propagation.

Gaussian processes↗

DOE OSTI2025

Energy storage in combined gas-electric energy transitions models: The case of California

California’s vision for a net-zero future by 2045 relies heavily on variable renewable energy systems. Thus, energy storage - particularly long-duration storage - could play a fundamental role in reliably supplying low-carbon electricity. We study energy storage using the BRIDGES model, a combined gas-electric capacity expansion model for California across multiple investment periods (2025-2045), modeled with progressively decreasing carbon emission targets to a zero emissions by 2045. This least-cost optimization model includes renewable gas production via power-to-gas, long-term storage of energy in gaseous form, electric energy storage such as through batteries and hydrogen storage, and renewable energy generation, all with capacity tracking and investment. Multiple scenarios are evaluated to examine the sensitivity of the optimal storage portfolio to system-level and sector-level parameters. The scenario results show that all electric energy storage systems - which vary in storage duration - are deployed and required in a net-zero California in 2045, amounting to around 75 GW of storage capacity. Lithium ion systems make up approximately 80% of this power capacity and supply most short-run storage needs. Hydrogen storage - in the form of a power-to-gas-to-power system - emerges as a replacement to conventional natural gas storage, comprising most of the total energy storage capacity (~ 4 TWh). This capacity is less than 5% of the current natural gas storage capacity (94 TWh), indicating sufficient room for repurposing part of the gas infrastructure. A demand-side sensitivity analysis proves that higher electricity demand correlates with more builds of Li-ion batteries, while higher industrial heat demand leads to more builds of long-duration storage systems in a net-zero economy. Furthermore, power-to-gas systems satisfy part of the industrial heat demand by locally supplying renewable gas, which overtakes the traditional centralized gas storage and transfers through pipelines, casting significant doubts on the future of the large-scale gas infrastructure.

03 NATURAL GAS↗

DOE OSTI2024

Machine learning models of intermittent operation of RO wellhead water treatment for salinity reduction and nitrate removal

Machine learning models were developed for intermittent multi-mode operation of a wellhead reverse osmosis water purification and desalination system to predict salt passage, nitrate passage, and permeate flux. The models, based on long short-term memory (LSTM) recurrent neural network (RNN) architecture, included an attention mechanism to increase model performance in proximity of the regulatory limit for nitrate. Training and testing of the models for the Startup, Production, Shutdown and Flushing operational modes were based on operational data (consisting of 22 process variables per data sample) acquired every 2–5 s over a six-month period. The significant sets of model input attributes for the different operational modes were assessed via Spearman ranking correlation, Self-Organizing Map (SOM) analysis and feed forward feature selection (FFFS). Although the variability of nitrate passage, salt passage and permeate flux was significant over the four operational modes, prediction performance for the three outcomes were with R2 and Average Absolute Relative Error (AARE) of 0.78–0.95 and 2.96–6.16 %, respectively. Model updates post membrane elements replacement demonstrated similar levels of prediction accuracy. The study results suggest that there is merit in exploring the utility of multi-mode models for sensor fault detection, data imputation, and for potential use in model-predictive control.

Intermittent RO operation↗

DOE OSTI2025

In situ probing of structure and deagglomeration of SnO 2 colloids via small-angle X-ray scattering

Transforming dry nanopowders into stable colloidal dispersions remains challenging due to the cohesive forces between the nanoparticles (NPs) which promote agglomeration. Effective dispersion and deagglomeration of these agglomerates is a critical process in the formulation and preparation of nanoparticle-based functional materials via the colloidal route. Understanding the deagglomeration dynamics provides information to improve microstructural quality in various applications by enabling engineering of agglomerate size, structure and morphology. However, the deagglomeration process dynamics with respect to the evolution of the fractal agglomerate structures, particularly for very small NPs, is still poorly understood and requires further investigation. This study employs in situ small-angle X-ray scattering (SAXS) to investigate the sonication-induced deagglomeration of SnO 2 NPs. Electrostatically stabilized SnO 2 colloids with varying primary particle size (6–21 nm) are investigated in a specifically designed in situ cell using synchrotron-based SAXS to study the influence of sonication time and intensity on the nanoscaled agglomerates. Complete structural analysis via SAXS reveals a direct correlation between changes in agglomerate size and structure, size-dependent deagglomeration behavior and a dependence on the overall energy introduced during sonication into the dispersion regardless of the actual power as well as ultrasonic process parameters in case of SnO 2 NPs. The results suggest that control of the dispersion process during ultrasonic deagglomeration results in tailoring agglomerates with respect to size and structure.

Deagglomeration↗

DOE OSTI2025

Delineating the Effects of Counterions on the Structural and Vibrational Properties of U(IV) Lindqvist Polyoxometalate Complexes

Herein we conducted a full investigation into the fundamental structural and vibrational properties of uranium(IV) Peacock−Weakley-type lacunary Lindqvist (W 10 ) polyoxometalate (POM) complexes. We recently demonstrated the importance of the secondary lattice elements in tuning the distortion of the D 4d symmetry in W 10 POM complexes, and here, we synthesized eight UW 10 complexes with different alkali metal counterions and evaluated how the composition and packing of counterion species affected complex structural and vibrational properties. Single-crystal X-ray diffraction analysis on complexes 1−8 revealed changes in structural distortion parameters as a function of differences in counterion configurations, while far-infrared and Raman spectra for 1−8 also demonstrated that vibrational mode frequencies were sensitive to changes in counterion composition and packing. To more effectively compare different counterion configurations, we developed counterion effective ionic radius (eIR) as a new structural parameter, and comparisons between structural distortion parameters and eIR values strongly suggested that modulation by the secondary lattice elements can affect structural and vibrational manifolds within POM complexes. Partial least squares (PLS) analysis was used to quantitatively evaluate correlations observed within this investigation, and PLS statistical models showed a strong correlation between counterion eIR and both structural distortion parameters and vibrational mode frequencies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

A road map to cosmological parameter analysis with third-order shear statistics: III. Efficient estimation of third-order shear correlation functions and an application to the KiDS-1000 data

Context. Third-order lensing statistics contain a wealth of cosmological information that is not captured by second-order statistics. However, the computational effort it takes to estimate such statistics in forthcoming stage IV surveys is prohibitively expensive. Aims. We derive and validate an efficient estimation procedure for the three-point correlation function (3PCF) of polar fields such as weak lensing shear. We then use our approach to measure the shear 3PCF and the third-order aperture mass statistics on the KiDS-1000 survey. Methods We constructed an efficient estimator for third-order shear statistics that builds on the multipole decomposition of the 3PCF. We then validated our estimator on mock ellipticity catalogs obtained from N -body simulations. Finally, we applied our estimator to the KiDS-1000 data and presented a measurement of the third-order aperture statistics in a tomographic setup. Results. Our estimator provides a speedup of a factor of ∼100–1000 compared to the state-of-the-art estimation procedures. It is also able to provide accurate measurements for squeezed and folded triangle configurations without additional computational effort. We report a significant detection of tomographic third-order aperture mass statistics in the KiDS-1000 data (S/N = 6.69). Conclusions. Our estimator will make it computationally feasible to measure third-order shear statistics in forthcoming stage IV surveys. Furthermore, it can be used to construct empirical covariance matrices for such statistics.

Astronomy & Astrophysics↗

DOE OSTI2025

Cosmology from Planck CMB lensing and DESI DR1 quasar tomography

We present a measurement of the amplitude of matter fluctuations over the redshift range 0.8 ≤ z ≤ 3.5 from the cross correlation of over 1.2 million spectroscopic quasars selected by the Dark Energy Spectroscopic Instrument (DESI) across 7,200 deg 2 (∼ 170 quasars/deg 2 ) and Planck PR4 (NPIPE) cosmic microwave background (CMB) lensing maps. We perform a tomographic measurement in three bins centered at effective redshiftsz=1.44, 2.27 and 2.75, which have ample overlap with the CMB lensing kernel. From a joint fit using the angular clustering of all three redshift bins (auto and cross-spectra), and including an Q m prior from DESI DR1 baryon acoustic oscillations to break the $Ω_{m}-σ_{8}$ degeneracy, we constrain the amplitude of matter fluctuations in the matter-dominated regime to be $σ_{8}=0.929^{+0.059}_{-0.074}$ and $S_{8}≡σ_{8}(Ω_m/0.3)^{0.5} = 0.922^{+0.059}_{-0.073}$. We provide a growth of structure measurement with the largest spectroscopic quasar sample to date at high redshift, which is ∼ 1.5σ higher than predictions from ΛCDM fits to measurements of the primary CMB from Planck PR4. The cross-correlation between PR4 lensing maps and DESI DR1 quasars is detected with a signal-to-noise ratio of 21.7 and the quasar auto-correlation at 27.2 for the joint analysis of all redshift bins. We combine our measurement with the CMB lensing auto-spectrum from the ground-based Atacama Cosmology Telescope (ACT DR6) and Planck PR4 to perform a sound-horizon-free measurement of the Hubble constant, yielding $H_{0}=69.1^{+2.2}_{-2.6} {\rm \ km\ s}^{-1}{\rm Mpc}^{-1}$ through its sensitivity to the matter-radiation equality scale.

baryon acoustic oscillations↗

DOE OSTI2025

Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge

Understanding the interactions and regulatory relationships among biomolecules is essential for deciphering complex biological systems and elucidating the mechanisms behind diverse biological functions. Traditionally, the collection of such molecular interaction data has relied on expert curation, a process that is both time-consuming and labor-intensive. To address these limitations, this study explores the use of large language models (LLMs) to automate the genome-scale extraction of molecular interaction knowledge. Here, we evaluate the performance of various LLMs on key biological tasks, including the identification of protein-protein interactions, detection of genes associated with pathways influenced by low-dose radiation, and inference of gene regulatory relationships. Our findings demonstrate that larger LLMs tend to perform better, particularly in extracting intricate gene and protein interactions. Despite their strengths, these models face challenges in recognizing functionally diverse gene groups and highly correlated regulatory relationships. Through a comprehensive analysis using established molecular interaction and pathway databases, we show that LLMs possess the potential to identify relevant biomolecules and predict their interactions, offering valuable insights and marking a significant step toward AI-driven biological knowledge discovery.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2025

Antiprotons and Elementary Particles over a Solar Cycle: Results from the Alpha Magnetic Spectrometer

We present results over an 11-year Solar cycle of cosmic antiprotons based on 1.1 × 10 6 events in the rigidity range from 1.00 to 41.9 GV. The $\bar{𝑝}$ fluxes exhibit distinct properties. The magnitude of the $\bar{𝑝}$ flux temporal variation is significantly smaller than those of 𝑝, 𝑒 − , and 𝑒 + . A hysteresis between the $\bar{𝑝}$ fluxes and the 𝑝 fluxes is observed, whereas the $\bar{𝑝}$ and 𝑒 − fluxes show a linear correlation. With a model-independent analysis, we found a universal relation between the shape of the rigidity spectrum and the magnitude of flux temporal variation over an 11-year Solar cycle for both positively and negatively charged particles. The simultaneous results on $\bar{𝑝}$ and 𝑝, 𝑒 − , and 𝑒 + provide unique information for understanding particle transport in the Solar System as a function of mass, charge, and spectral shape.

cosmic ray composition & spectra↗

DOE OSTI2024

Anode Upcycling via Tailored Solvent Treatment

To achieve a truly closed-loop direct recycling process for lithium-ion batteries, all component materials must be recovered. To date, direct recycling method development has primarily focused on the high-value transition-metal cathode materials, while the inherently lower-value graphite has been challenging to recover in a cost-effective manner. However, end-of-life graphite contains a unique engineered value due to the presence of the solid electrolyte interphase (SEI). Growth of the SEI during the cell's active lifetime stabilizes the electronically reactive graphite surface through an irreversible consumption of Li, and thus necessitates both excess lithiation of the cathode and a costly and time-intensive formation procedure during manufacturing. An optimized pre-formed SEI that capitalizes on existing SEI components from end-of-life batteries has the potential to significantly reduce cathode lithiation requirements and eliminate the critical bottleneck of formation cycling during cell remanufacturing. Further, retaining Li at the anode obviates the need for a separate Li leaching and recovery step, improving the overall efficiency of the direct recycling line. In this work, we present a novel approach to "upcycling" spent graphite through use of tailored chemical treatment to remove adverse (i.e., highly resistive and/or poorly passivating) SEI species while retaining beneficially passivating components. We have explored a rational set of solvents spanning a range of polarity, proticity, and molecular size to evaluate structure-property-performance relationships between applied solvent(s), removed and remaining SEI species, and electrochemical response of the resulting graphite product. Further, we have developed and optimized a robust and holistic analysis procedure that couples symmetric-cell electrochemical testing, multi-modal materials characterization, and advanced electrochemical modeling. These analysis results inform a set of correlative metrics for graphite performance relative to both solvent properties and upcycled SEI composition. We demonstrate effective tunability in the residual SEI composition by varying solvent identity and concentration, and report on several promising solvent systems that achieve comparable or performance to pristine graphite.

anode recycling↗

DOE OSTI2025

Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing

In this paper, we propose a method to perform empirical analysis of the loss landscape of machine learning (ML) models. The method is applied to two ML models for scientific sensing, which necessitates quantization to be deployed and are subject to noise and perturbations due to experimental conditions. Our method allows assessing the robustness of ML models to such effects as a function of quantization precision and under different regularization techniques -- two crucial concerns that remained underexplored so far. By investigating the interplay between performance, efficiency, and robustness by means of loss landscape analysis, we both established a strong correlation between gently-shaped landscapes and robustness to input and weight perturbations and observed other intriguing and non-obvious phenomena. Our method allows a systematic exploration of such trade-offs a priori, i.e., without training and testing multiple models, leading to more efficient development workflows. This work also highlights the importance of incorporating robustness into the Pareto optimization of ML models, enabling more reliable and adaptive scientific sensing systems.

Baldi, Tommaso [Pisa, Scuola Normale Superiore]↗