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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Topography restoration in white-light interferometry using an instrument transfer function evaluated with binary pseudo-random arrays

Accurate knowledge of the instrument transfer function (ITF) is vital for topography measurements using white-light interferometry (WLI). To this end, we derive a complete set of analytical expressions for the power spectral density (PSD) of a discretely sampled binary pseudo-random array (BPRA) as a theoretical benchmark. We then determine the ITF by comparing this theoretical PSD with the measured PSD of the BPRA. For the Zygo ZeGage Pro HR with a objective, the determined ITF closely matches the nominal modulation transfer function (MTF). Accordingly, we integrate the nominal MTF into Fourier-domain restoration filters and apply them to practical WLI topography data, yielding restored topographies that show improved agreement with atomic force microscopy measurements. Overall, our integrated BPRA-based ITF measurement and filtering methodology offers a robust yet practical means of characterizing WLI performance and enhancing the accuracy of surface measurements.

Hirose, Shigenobu [Japan Agency for Marine Earth S↗
DOE OSTI2020

Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product Report

Objective analysis (OA) is a method of mapping unstructured data to a structured grid. In the context of scanning radar data, OA is used to interpolate data in antenna coordinates (range, azimuth, and elevation) onto a regularly spaced Cartesian grid (Trapp and Doswell 2000). The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product (VAP) uses the Python ARM Radar Toolkit (Py-ART), a data model-driven interactive architecture for working with weather radar data, to map the data to a Cartesian grid (Helmus and Collis 2016). MMCG, with Py-ART built in, has the ability to take radar data in antenna coordinates and map the gates to a Cartesian grid using inverse distance weight functions such as Cressman (square) and Barnes (exponential), but also can filter the data during the interpolation. MMCG also allows arbitrary formulations for the radius of influence, which are matched to particular radar scanning strategies. This creates a complex parameter space for optimizing the retention of storm structure detail while minimizing artifacts. MMCG takes data processed with ARM’s Corrected Precipitation Radar Moments in Antenna Coordinates (CMAC) VAP and maps it to a Cartesian grid as the output product. A variety of fields that have been mapped to the Cartesian grid are then saved to plots to complement each grid file.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Fabrication Development for SPT-SLIM, a Superconducting Spectrometer for Line Intensity Mapping

Line Intensity Mapping (LIM) is a new observational technique that uses low-resolution observations of line emission to efficiently trace the large-scale structure of the Universe out to high redshift. Common mm/sub-mm emission lines are accessible from ground-based observatories, and the requirements on the detectors for LIM at mm-wavelengths are well matched to the capabilities of large-format arrays of superconducting sensors. We describe the development of an R = lambda/Delta lambda = 300 on-chip superconducting filter-bank spectrometer covering the 120-180 GHz band for future mm-LIM experiments, focusing on SPT-SLIM, a pathfinder LIM instrument for the South Pole Telescope. Radiation is coupled from the telescope optical system to the spectrometer chip via an array of feedhorn-coupled orthomode transducers. Superconducting microstrip transmission lines then carry the signal to an array of channelizing half-wavelength resonators, and the output of each spectral channel is sensed by a lumped element kinetic inductance detector (leKID). Key areas of development include incorporating new low-loss dielectrics to improve both the achievable spectral resolution and optical efficiency and development of a robust fabrication process to create a galvanic connection between ultra-pure superconducting thin-films to realize multi-material (hybrid) leKIDs. We provide an overview of the spectrometer design, fabrication process, and prototype devices.

microstrip resonators↗
DOE OSTI2022

Blanco DECam Bulge Survey (BDBS)

The Blanco DECam Bulge Survey (BDBS) has imaged more than 200 square degrees of the southern Galactic bulge, providing photometry in the ugrizy filters for ~250 million unique stars. The presence of a strong foreground disk population, along with complex reddening and extreme image crowding, has made it difficult to constrain the presence of young and intermediate age stars in the bulge population. We employed an accurate cross-match of BDBS with the latest data release (EDR3) from the Gaia mission, matching more than 140 million sources with BDBS photometry and Gaia EDR3 photometry and astrometry. We relied on Gaia EDR3 astrometry, without any photometric selection, to produce clean BDBS bulge colour-magnitude diagrams (CMDs). Gaia parallaxes were used to filter out bright foreground sources, and a Gaussian mixture model fit to Galactic proper motions could identify stars kinematically consistent with bulge membership. We applied this method to 127 different bulge fields of 1 deg 2 each, with |$\ell$| ≤ 9.5 ° and –9.5 ° ≤ b ≤ –2.5 °. The astrometric cleaning procedure removes the majority of blue stars in each field, especially near the Galactic plane, where the ratio of blue to red stars is .10%, increasing to values ~20% at higher Galactic latitudes. We rule out the presence of a widespread population of stars younger than 2 Gyr. The vast majority of blue stars brighter than the turnoff belong to the foreground population, according to their measured astrometry. We introduce the distance between the observed red giant branch bump and the red clump as a simple age proxy for the dominant population in the field, and we confirm the picture of a predominantly old bulge. Further work is needed to apply the method to estimate ages to fields at higher latitudes, and to model the complex morphology of the Galactic bulge. We also produce transverse kinematic maps, recovering expected patterns related to the presence of the bar and of the X-shaped nature of the bulge.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Photometric redshifts for the S-PLUS Survey: Is machine learning up to the task?

The Southern Photometric Local Universe Survey (S-PLUS) is a novel project that aims to map the Southern Hemisphere using a twelve filter system, comprising five broad-band SDSS-like filters and seven narrow-band filters optimized for important stellar features in the local universe. In this paper we use the photometry and morphological information from the first S-PLUS data release (S-PLUS DR1) cross-matched to unWISE data and spectroscopic redshifts from Sloan Digital Sky Survey DR15. We explore three different machine learning methods (Gaussian Processes with GPz and two Deep Learning models made with TensorFlow) and compare them with the currently used template-fitting method in the S-PLUS DR1 to address whether machine learning methods can take advantage of the twelve filter system for photometric redshift prediction. Using tests for accuracy for both single-point estimates such as the calculation of the scatter, bias, and outlier fraction, and probability distribution functions (PDFs) such as the Probability Integral Transform (PIT), the Continuous Ranked Probability Score (CRPS) and the Odds distribution, we conclude that a deep-learning method using a combination of a Bayesian Neural Network and a Mixture Density Network offers the most accurate photometric redshifts for the current test sample. In conclusion, it achieves single-point photometric redshifts with scatter (σ NMAD ) of 0.023, normalized bias of -0.001, and outlier fraction of 0.64% for galaxies with r_auto magnitudes between 16 and 21.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Octave-spanning mid-infrared femtosecond OPA in a ZnGeP 2 pumped by a 2.4 μm Cr:ZnSe chirped-pulse amplifier

We report on the highly efficient, octave-spanning mid-infrared (mid-IR) optical parametric amplification (OPA) in a ZnGeP 2 (ZGP) crystal, pumped by a 1 kHz, 2.4 μm, 250 fs Cr:ZnSe chirped-pulse amplifier. The full spectral coverage of 3−10 μm with the amplified signal and idler beams is demonstrated. The signal beam in the range of ∼3 − 5 μm is produced by either white light generation (WLG) in YAG or optical parametric generation (OPG) in ZGP using the common 2.4 μm pump laser. We demonstrate the pump to signal and idler combined conversion efficiency of 23% and the pulse energy of up to 130 μJ with ∼2 μJ OPG seeding, while we obtain the efficiency of 10% and the pulse energy of 55 μJ with ∼0.2 μJ WLG seeding. The OPA output energy is limited by the available pump pulse energy (0.55 mJ at ZGP crystal) and therefore further energy scaling is feasible with multi-stage OPA and higher pump pulse energy. The autocorrelation measurements based on random quasi-phase matching show that the signal pulse durations are ∼318 fs and ∼330 fs with WLG and OPG seeding, respectively. In addition, we show the spectrally filtered 30 μJ OPA output at 4.15 μm suitable for seeding a Fe:ZnSe amplifier. Our ultrabroadband femtosecond mid-IR source is attractive for various applications, such as strong-field interactions, dielectric laser electron acceleration, molecular spectroscopy, and medical surgery.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Filter-Banks for Ultra-Wideband Communications, Sensing, and Localization

Recently, filterbank multi-carrier spread spectrum (FBMC-SS) has been proposed as a candidate waveform for ultrawideband (UWB) communications. It has been noted that FBMCSS is a perfect match to this application, leading to a trivial method of matching to the required spectral mask at different regions of the world. FBMC-SS also allows easy rejection of high-power interfering signals that may appear over different parts of the UWB spectral band. In this paper, we concentrate on the use of staggered multitone spread spectrum (SMT-SS) for UWB communications. SMT makes use of offset QAM modulation to transmit data symbols over narrowband, overlapping subcarrier bands. This form of FBMC-SS is well-suited to UWB communications because it has good spectral efficiency and a flat power spectral density (PSD), resulting in good utilization of the UWB spectral mask. Additionally, we explore new methods for multi-coding that result in higher bit rates than previous FBMCSS systems. Moreover, we study methods for equalizing the UWB multipath channel and cancelling narrowband interference. Excellent performance of the proposed methods are substantiated by presenting simulation results.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Decentralized Filtering Adaptive Neural Network Control for Uncertain Switched Interconnected Nonlinear Systems

This article presents a novel decentralized filtering adaptive neural network control framework for uncertain switched interconnected nonlinear systems. Each subsystem has its own decentralized controller based on the established decentralized state predictor. For each subsystem, the nonlinear uncertainties are approximated by a Gaussian radial basis function (GRBF) neural network incorporated with a piecewise constant adaptive law, where the adaptive law will update adaptive parameters from the error dynamics between the host system and the decentralized state predictor by discarding the unknowns, whereas a decentralized filtering control law is derived to cancel both local and mismatched uncertainties from other subsystems, as well as achieve the local objective tracking of the host system. The achievement of global objective depends on the achievement of local objective for each subsystem. The matched uncertainties are canceled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is required to eliminate the effect of the mismatched uncertainties on the output. By exploiting the average dwell time principle, the error bounds between the real system and the virtual reference system, which defines the best performance that can be achieved by the closed-loop system, are derived. A numerical example is given to illustrate the effectiveness of the decentralized filtering adaptive neural network control architecture by comparing against the model reference adaptive control (MRAC).

Average dwell time, decentralized, filtering adapt↗
DOE OSTI2023

A single oscillating proto-hypothalamic neuron gates taxis behavior in the primitive chordate Ciona

Ciona larvae display a number of behaviors, including negative phototaxis. In negative phototaxis, the larvae first perform short spontaneous rhythmic casting swims. As larvae cast in a light field, their photoreceptors are directionally shaded by an associated pigment cell, providing a phototactic cue. This then evokes an extended negative taxis swim. We report here that the larval forebrain of Ciona has a previously uncharacterized single slow-oscillating inhibitory neuron (neuron cor-assBVIN78) that projects to the midbrain, where it targets key interneurons of the phototaxis circuit known as the photoreceptor relay neurons. The anatomical location, gene expression and oscillation of cor-assBVIN78 suggest homology to oscillating neurons of the vertebrate hypothalamus. Ablation of cor-assBVIN78 results in larvae showing extended phototaxis-like swims, but which occur in the absence of phototactic cues. These results indicate that cor-assBVIN78 has a gating activity on phototaxis by projecting temporally-oscillating inhibition to the photoreceptor relay neurons. However, in intact larvae the frequency of cor-assBVIN78 oscillation does not match that of the rhythmic spontaneous swims, indicating that the troughs in oscillations do not themselves initiate swims, but rather that cor-assBVIN78 may modulate the phototaxis circuit by filtering out low level inputs while restricting them temporally to the troughs in inhibition.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Details of the neutral energy distribution and ionization source using spectrally resolved Balmer-alpha measurements on DIII-D

Spectrally resolved passive Balmer- α (D- α, H- α) measurements from the DIII-D 16 channel edge main-ion charge exchange recombination system confirm the presence of higher energy neutrals (“thermal” neutrals) in addition to the cold neutrals that recycle off the walls in the edge region of DIII-D plasmas. Charge exchange between thermal ions and edge neutrals transfers energy and momentum between the populations giving rise to thermal neutrals with energies approximating the ions in the pedestal region. Multiple charge exchange events in succession allow an electron to effectively take a random walk, transferring from ion to ion, providing a pathway of increasing energy and velocity, permitting a neutral to get deeper into the plasma before a final ionization event that contributes to the ion and electron particle fueling. Spectrally resolved measurements provide information about the density and velocity distribution of these neutrals, which has been historically valuable for validating Monte Carlo neutral models, which include the multi stage charge exchange dynamics. Here, in this study, a multi-channel set of such measurements is used to specifically isolate the details of the thermal neutrals that are responsible for fueling inside the pedestal top. Being able to separate the thermal from the cold emission overcomes several challenges associated with optical filter-based neutral density measurements. The neutral dynamics, deeper fueling by the thermal neutrals, and spectral measurement are modeled with the FIDASIM Monte Carlo collisional radiative code, which also produces synthetic spectra with a shape that is in close agreement with the measurements. By scaling the number of neutrals in the simulation to match the intensity of the thermal emission, we show it is possible to obtain local neutral densities and ionization source rates.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Predicting measures of soil health using the microbiome and supervised machine learning

Soil health encompasses a range of biological, chemical, and physical soil properties that sustain the commercial and ecological value of agroecosystems. Monitoring soil health requires a comprehensive set of diagnostics that can be cost-prohibitive for routine analyses. The soil microbiome provides a rich source of information about soil properties, which can be assayed in a high-throughput, cost-effective way. We evaluated the accuracy of random forest (RF) and support vector machine (SVM) regression and classification models in predicting 12 measures of soil health, tillage status, and soil texture from 16S rRNA gene amplicon data with an operationally relevant sample set. We validated the efficacy of the best performing models against independent datasets and also tested best practices for processing microbiome data for use in machine learning. Soil health metrics could be predicted from microbiome data with the best models achieving a Kappa value of ~0.65, for categorical assessments, and a R2 value of ~0.8, for numerical scores. Biological health ratings were better predicted than chemical or physical ratings. Validation with independent datasets revealed that models had general predictive value for soil properties, including yield. The ecological profiles of several taxa important for model accuracy matched the observed relationships with soil health, including Pyrinomonadaceae, Nitrososphaeraceae, and Candidatus Udeaobacter. Models trained at the highest taxonomic resolution proved most accurate, with losses in accuracy resulting from rarefying, sparsity filtering, and aggregating at higher taxonomic ranks. Furthermore, our study provides the groundwork for developing scalable technology to use microbiome-based diagnostics for the assessment of soil health.

16S rRNA gene↗
DOE OSTI2023

TopPICR: A Companion R Package for Top-Down Proteomics Data Analysis

Top-down proteomics is the analysis of proteins in their intact form without proteolysis, thus preserving valuable information about post-translational modifications, isoforms, and proteolytic processing. However, it is still a developing field due to limitations in the instrumentation, difficulties with interpretation of complex mass spectra, and a lack of well-established quantification approaches. TopPIC is one of the popular tools for proteoform identification. Here we extended its capabilities into label-free proteoform quantification by developing a companion R package (TopPICR). Key steps in the TopPICR pipeline include filtering identifications, inferring a minimal set of protein accessions explaining the observed sequences, aligning retention times, recalibrating measured masses, clustering features across datasets, and finally compiling feature intensities using the match-between-runs approach. The output of the pipeline is an MSnSet object which makes downstream data analysis seamlessly compatible with packages from the Bioconductor project. It also provides the capability for visualizing proteoforms within the context of the parent protein sequence. The functionality of TopPICR is demonstrated on top-down LC-MS/MS datasets of 10 human-in-mouse xenografts of luminal and basal breast tumor samples.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The gMOSS: the galaxy survey and galaxy populations of the large homogeneous field

ABSTRACT We present the gMOSS (Galaxies of Medium-band One-meter Schmidt telescope Survey) catalogue of ∼19 000 galaxies in 20 filters (4 broad-band SDSS and 16 medium-band filters). We observed 2.386 deg2 on the central part of the HS47.5-22 field with the 1-m Schmidt telescope of the Byurakan Astrophysical Observatory. The gMOSS is a complete flux-limited sample of galaxies with a threshold magnitude of r SDSS ≤ 22.5 AB. From photometric measurements with 16 medium-band filters and u SDSS, we get spectral energy distributions for each object in the field, which are used for further analysis. Galaxy classification and photometric redshift estimation based on spectral template matching with zebra software. The obtained redshift accuracy is σNMAD < 0.0043. Using the SED-fitting cigale code, we obtained the main properties of the stellar population of galaxies, such as rest-frame (u − r)res colour, stellar mass, extinction, and mass-weighted age with a precision of 0.16 ± 0.07 mag, 0.14 ± 0.04 dex, 0.27 ± 0.1 mag, and 0.08 ± 0.04 dex, respectively. Using a dust-corrected colour–mass diagram, we divided the full sample into populations of red and blue galaxies and considered the dependencies between stellar mass and age. Throughout cosmic time, red sequence galaxies remain older and more massive than blue cloud galaxies. The star formation history of a complete subsample of galaxies selected in the redshift range 0.05 ≤ z ≤ 0.015 with <$\mathrm{log} M \mathrm{\gt }_\mathrm{[M_\odot ]}$>8.3 shows an increase in the SFRD up to z ∼ 3, under the results obtained in earlier studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Validation of Actuator Line and Actuator Disk Models with Filtered Lifting Line Corrections Implemented in Nalu-Wind Large Eddy Simulations of the Atmospheric Boundary Layer

Turbine generator power from simulations using Actuator Line Models and Actuator Disk Models with a Filtered Lifting Line Correction are compared to field data of a V27 turbine. Preliminary results of the wake characteristics are also presented. Turbine quantities of interest from traditional ALM and ADM with the Gaussian kernel (..epsilon..) set at the optimum value for matching power production and that resolve the kernel at all mesh sizes are also presented. The atmospheric boundary layer is simulated using Nalu-Wind, a Large Eddy Simulation code which is part of the ExaWind code suite. The effect of mesh resolution on quantities of interest is also examined.

Actuator Disk Models↗
DOE OSTI2022

The miniJPAS survey quasar selection – I. Mock catalogues for classification

In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper, we develop a pipeline to compute synthetic photometry of quasars, galaxies, and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey . To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range 17.5 ≤ r < 24, we augment our sample of available spectra by shifting the original r-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modelling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Ultrasensitive single-cell proteomics workflow identifies >1000 protein groups per mammalian cell

Here, we report on the combination of nanodroplet sample preparation, ultra-low-flow nanoLC, high-field asymmetric ion mobility spectrometry (FAIMS), and the latest-generation Orbitrap Eclipse Tribrid mass spectrometer for greatly improved single-cell proteome profiling. FAIMS effectively filtered out singly charged ions for more effective MS analysis of multiply charged peptides, resulting in an average of 1056 protein groups identified from single HeLa cells without MS1-level feature matching. This is 2.3 times more identifications than without FAIMS and a far greater level of proteome coverage for single mammalian cells than has been previously reported for a label-free study. Differential analysis of single microdissected motor neurons and interneurons from human spinal tissue indicated a similar level of proteome coverage, and the two subpopulations of cells were readily differentiated based on single-cell label-free quantification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Benchmark Tracking System for Performance Monitoring

Benchmarking is essential for high-performance software development, particularly for monitoring performance across code iterations. This project focused on enhancing the benchmarking process for Lamellar, an asynchronous runtime for High-Performance Computing (HPC) systems developed at Pacific Northwest National Laboratory. Prior to this work, benchmark results were difficult to track and compare across code versions, presenting significant challenges in identifying performance regressions and long-term trends. The primary objective was to establish a systematic, reproducible approach for measuring performance and detecting regressions following code commits. Our methodology involved three key components: standardizing benchmark outputs, implementing data versioning, and developing analysis tools. We standardized the benchmark output format to JSON Line records containing specific fields (execution time, hardware specifications, and environmental variables). To address data management challenges, we evaluated several options and eventually chose a git repository dedicated to benchmark data. We developed a suite of Python tools that processed benchmark results, enriched them with metadata, and facilitated search in the repository. The resulting system enables more efficient filtering and comparison of performance metrics across commit histories, hardware configurations, and benchmark variants through a unified query interface. Our implementation reduces computational overhead by first checking for existing results through configuration matching before initiating new benchmark runs, thereby conserving resources. The system has been validated by Lamellar developers. It organizes results by benchmark type and build configurations for efficient retrieval. Future developments include a planned Large Language Model interface for predicting benchmark performance, incorporating the criterion package for statistical analysis, which will enable automated detection of statistically significant performance changes, and integration with continuous integration pipelines. Despite these enhancements being reserved for future work, this project has successfully provided the Lamellar development team with a framework for maintaining consistent performance standards and identifying optimization opportunities across workloads and hardware environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

SEED: Semantic Energy Exploration and Discovery

The Bioenergy Knowledge Discovery Framework (KDF) hosts a vast repository of specialized data, yet traditional keyword-based search methods often struggle to provide direct answers, requiring significant domain expertise and manual effort to filter through raw documents. To overcome these barriers, this software introduces a semantic search engine that enables both specialists and non-specialists to query the KDF using natural language. By shifting from rigid keyword matching to intent-based retrieval, the tool automatically identifies and ranks the most relevant sources within the database. The system functions by processing natural language queries to extract the most pertinent information, delivering an AI-generated plain-language summary alongside exact supporting quotes from retrieved documents. This integrated approach provides users with immediate, evidence-based answers while eliminating the need for exhaustive manual review. By surfacing direct insights and contextual evidence, the software enhances the usability of existing KDF resources and democratizes access to complex bioenergy data. Ultimately, this semantic search solution accelerates the discovery process and supports faster, more informed decision-making across the bioenergy sector.

Pan, Meiyu (Melrose) [Oak Ridge National Laborator↗