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At least 811 records · Page 45

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DOE OSTI2020

Reliable photometric membership (RPM) of galaxies in clusters – I. A machine learning method and its performance in the local universe

ABSTRACT We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive a membership classification. After testing several machine learning techniques (such as stochastic gradient boosting, model averaged neural network and k-nearest neighbours), we found the support vector machine algorithm to perform better when applied to our data. Our training and validation data are from the Sloan Digital Sky Survey main sample. Hence, to be complete to $M_r^* + 3$, we limit our work to 30 clusters with $z$phot-cl ≤ 0.045. Masses (M200) are larger than $\sim 0.6\times 10^{14} \, \mathrm{M}_{\odot }$ (most above $3\times 10^{14} \, \mathrm{M}_{\odot }$). Our results are derived taking in account all galaxies in the line of sight of each cluster, with no photometric redshift cuts or background corrections. Our method is non-parametric, making no assumptions on the number density or luminosity profiles of galaxies in clusters. Our approach delivers extremely accurate results (completeness, C $\sim 92{\rm{ per\ cent}}$ and purity, P $\sim 87{\rm{ per\ cent}}$) within R200, so that we named our code reliable photometric membership. We discuss possible dependencies on magnitude, colour, and cluster mass. Finally, we present some applications of our method, stressing its impact to galaxy evolution and cosmological studies based on future large-scale surveys, such as eROSITA, EUCLID, and LSST.

Lopes, Paulo A. A.↗

DOE OSTI2022

Different Formation Scenarios for Counterrotating Stellar Disks in Nearby Galaxies

Using the integral field unit data from the Mapping Nearby Galaxies at Apache Point Observatory survey, we select a sample of 101 galaxies with counterrotating stellar disks and regularly rotating ionized gas disks. We classify the 101 galaxies into four types based on the features of their stellar velocity fields. The relative fractions and stellar population age radial gradients of the four types are different in the blue cloud, green valley, and red sequence populations. We suggest different formation scenarios for counterrotating stellar disks; the key factors in the formation of counterrotating stellar disks include (1) the abundance of preexisting gas in the progenitor and (2) the efficiency in angular momentum consumption.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1983

Edge effects in composites by moire interferometry

The very high sensitivity of moire interferometry has permitted the present edge effect experiments to be conducted at a low average stress and strain level, assuring linear and elastic behavior in the composite material samples tested. Sensitivity corresponding to 2450 line/mm moire was achieved with a 0.408 micron/fringe. Simultaneous observations of the specimen face and edge displacement fields showed good fringe definition despite the 1-mm thickness of the specimens and the high gradients, and it is noted that the use of a carrier pattern and optical filtering was effective in even these conditions. Edge effects and dramatic displacement gradients were confirmed in angle-ply composite laminates.

Czarnek, R.↗

NASA NTRS1990

A new class of gradient estimators for queueing systems with real-time constraints

Queuing systems are considered where the waiting or sojourn times of customers are constrained by specified deadlines. A performance measure of interest in these systems is the probability that a customer exceeds its assigned deadline. A new class of gradient estimators is presented for such performance measures with respect to various system parameters. The approach is applicable to a general class of problems and is based on efficiently capturing sensitivity information for probabilities of event occurrences from observed sample paths.

Gong, Wei-Bo↗

NASA NTRS2015

Observation Platform for Dynamic Biomedical and Biotechnology Experiments Using the International Space Station (ISS) Light Microscopy Module (LMM)

Techshot, Inc., has developed an observation platform for the LMM on the ISS that will enable biomedical and biotechnology experiments. The LMM Dynamic Stage consists of an electronics module and the first two of a planned suite of experiment modules. Specimens and reagent solutions can be injected into a small, hollow microscope slide-the heart of the innovation-via a combination of small reservoirs, pumps, and valves. A life science experiment module allows investigators to load up to two different fluids for on-orbit, real-time image cytometry. Fluids can be changed to initiate a process, fix biological samples, or retrieve suspended cells. A colloid science experiment module conducts microparticle and nanoparticle tests for investigation of colloid self-assembly phenomena. This module includes a hollow glass slide and heating elements for the creation of a thermal gradient from one end of the slide to the other. The electronics module supports both experiment modules and contains a unique illuminator/condenser for bright and dark field and phase contrast illumination, power supplies for two piezoelectric pumps, and controller boards for pumps and valves. This observation platform safely contains internal fluids and will greatly accelerate the research and development (R&D) cycle of numerous experiments, products, and services aboard the ISS.

Kurk, Michael A. (Andy)↗

NASA NTRS2015

Observation Platform for Dynamic Biomedical and Biotechnology Experiments Using the International Space Station (ISS) Light Microscopy Module (LMM)

Kurk, Michael A. (Andy)↗

NASA NTRS2020

Assessment of NO2 Observations During DISCOVER-AQ and KORUS-AQ Field Campaigns

NASA’s Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ, conducted in 2011–2014) campaign in the United States and the joint NASA and National Institute of Environmental Research (NIER) Korea–United States Air Quality Study (KORUS-AQ, conducted in 2016) in South Korea were two field study programs that provided comprehensive, integrated data sets of airborne and surface observations of atmospheric constituents, including nitrogen dioxide (NO2), with the goal of improving the interpretation of spaceborne remote sensing data. Various types of NO2 measurements were made, including in situ concentrations and column amounts of NO2 using ground- and aircraft-based instruments, while NO2 column amounts were being derived from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This study takes advantage of these unique datasets by first evaluating in situ data taken from two different instruments on the same aircraft platform, comparing coincidently sampled profile-integrated columns from aircraft spirals with remotely sensed column observations from ground-based Pandora spectrometers, intercomparing column observations from the ground (Pandora), aircraft (in situ vertical spirals), and space (OMI), and evaluating NO2 simulations from coarse Global Modeling Initiative (GMI) and high-resolution regional models. We then use these data to interpret observed discrepancies due to differences in sampling and deficiencies in the data reduction process. Finally, we assess satellite retrieval sensitivity to observed and modeled a priori NO2 profiles. Contemporaneous measurements from two aircraft instruments that likely sample similar air masses generally agree very well but are also found to differ in integrated columns by up to 31.9 %. These show even larger differences with Pandora, reaching up to 53.9 %, potentially due to a combination of strong gradients in NO2 fields that could be missed by aircraft spirals and errors in the Pandora retrievals. OMI NO2 values are about a actor of 2 lower in these highly polluted environments due in part to inaccurate retrieval assumptions (e.g., a priori pro-files) but mostly to OMI’s large footprint (>312 km2).

Nitrogen dioxide↗

DOE OSTI2021

Compositional Control on Shale Pore Structure Characteristics across a Maturation Gradient: Insights from the Devonian New Albany Shale and Marcellus Shale in the Eastern United States

The pore structure characteristics of shales are controlled by their mineralogical and organic matter (OM) composition. However, the contributions by different components in shales at varying thermal maturities remain poorly understood. In this study, Devonian New Albany Shale and Marcellus Shale samples spanning a thermal maturity from marginally mature (vitrinite reflectance R o 0.55%) to post-mature (R o 2.41 %) were selected to study the control of composition on the pore structure properties of shales at different stages of thermal maturation. Scanning electron microscope (SEM) imaging was used to examine pore types in shales, and low-pressure N 2 and CO 2 adsorption analyses were used to quantitatively characterize the mesopore and micropore characteristics of bulk shales and major components in shales. The results show that matrix-associated pores including interparticle pores between silt-sized mineral grains, phyllosilicate framework pores, and intraparticle pores within mineral grains exist in all samples but become less common with increasing maturity, which is likely caused by elevated compaction, cementation, and occlusion with bitumen. Secondary organic pores were not observed under SEM at marginal maturity but were detected in the condensate–wet gas and dry gas windows, with more organic pores in the dry gas window. At marginal maturity, OM has large amounts of mesopores and micropores as demonstrated by low-pressure N 2 and CO 2 adsorption analyses of OM isolated from shales, even though no OM-hosted pores were observed under SEM. With increasing thermal maturity, the mesopore and micropore specific surface area of OM increase and make greater contributions to the pore structure properties of bulk shales. The mesopore and micropore properties of shales are controlled by OM content and maturity as well as by clay mineral type and content, and they can be estimated from the contribution of each component at different stages of thermal maturation. In conclusion, accurate evaluation of the pore volume and SSA of shales will have important implications for assessing gas adsorption and transport in shales.

03 NATURAL GAS↗

NASA NTRS2016

Thermal Cycling for Development of Hybrid Fuel for a Notional Mars Ascent Vehicle

A Mars Ascent Vehicle (MAV) would be a critical part of a potential Mars sample return campaign. As currently envisioned, the MAV would launch samples from Mars into Mars orbit. Challenging requirements would be placed on a MAV as it would need to be able to survive in the harsh Martian environment which can get as cold as -120°C in the winter. At these cold temperatures, survival heating could be necessary to keep the MAV within the allowable flight temperature range. Estimates for survival heating power show that a fuel capable of operating at low temperature would significantly reduce the total required energy of the MAV. This has led to consideration of a hybrid propulsion system due to its anticipated low temperature capability. A new wax-based fuel, SP7, has been developed for a notional MAV mission. Thermal modeling of the fuel in the Mars environment was completed in order to estimate the temperature response of the fuel during Mars diurnal temperature cycling. Preliminary thermal testing was conducted to determine the max temperature range, radial gradient, and ramp rate the fuel could tolerate without cracking. The modeling and preliminary testing was used to develop a comprehensive, 100-day thermal cycling campaign. A total of 200 cycles are planned that are representative of 200 Martian diurnal cycles, roughly one-third of a Martian year. After 150 cycles, the fuel samples show no signs of cracking that would jeopardize the rocket’s performance. Though not yet complete, this testing is showing that SP7 has the potential to successfully be used on a MAV mission.

Farias, Edgardo↗

DOE OSTI2026

Electronic transport, thermal transport, thermal expansion, and magnetization in the strongly correlated metal LaNi⁢O 3

Perovskite structured LaNiO 3 is a strongly correlated metal with intriguing thermal and magnetic properties. The volume dependence of calculated and measured physical properties can add additional critical information to develop a more in-depth understanding of this strongly correlated phenomenon. Taking advantage of recent single crystal LaNiO 3 growth using the floating-zone method, we have measured the thermal expansion, the magnetostriction, and the pressure dependence of the magnetic susceptibility, which then allows derivation of the Grüneisen parameters γ e = $\frac{dlnN(E_F)}{d lnV}$, γ χ = $\frac{dlnχ}{d lnV}$, as well as of electric and thermal transport properties. We simulate the volume dependence of structural and magnetic properties using Density Functional Theory calculations at the Generalized Gradient Approximation level. A large discrepancy between experimental values and calculated ones suggests that strong correlations are likely to be dynamic in nature. This study also provides a side-by-side comparison of measurements in single crystal and polycrystalline samples of LaNiO 3 to elucidate intrinsic materials properties. A broad hump at high temperatures in the temperature dependence of magnetization found in the single crystal sample of LaNiO 3 has been rationalized by a model that includes the influence of electron correlations on the Landau diamagnetism.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Predicting variable gene content in Escherichia coli using conserved genes

Having the ability to predict the protein-encoding gene content of an incomplete genome or metagenome-assembled genome is important for a variety of bioinformatic tasks. In this study, as a proof of concept, we built machine learning classifiers for predicting variable gene content in Escherichia coli genomes using only the nucleotide k-mers from a set of 100 conserved genes as features. Protein families were used to define orthologs, and a single classifier was built for predicting the presence or absence of each protein family occurring in 10%–90% of all E. coli genomes. The resulting set of 3,259 extreme gradient boosting classifiers had a per-genome average macro F1 score of 0.944 [0.943–0.945, 95% CI]. We show that the F1 scores are stable across multi-locus sequence types and that the trend can be recapitulated by sampling a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including “hypothetical proteins” was accurately predicted (F1 = 0.902 [0.898–0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions had slightly lower F1 scores but were still accurate (F1s = 0.895, 0.872, 0.824, and 0.841 for transposon, phage, plasmid, and antimicrobial resistance-related functions, respectively). Finally, using a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources, we observed an average per-genome F1 score of 0.880 [0.876–0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1988

Rarefied-flow pitching moment coefficient measurements of the Shuttle Orbiter

An overview of the process for obtaining the Shuttle Orbiter rarefied-flow pitching moment from flight gyro data is presented. The extraction technique involves differentiation of the output of the pitch gyro after accounting for nonaerodynamic torques, such as those produced by gravity gradient and the Orbiter's auxiliary power unit and adjusting for drift biases. The overview of the extraction technique includes examples of results from each of the steps involved in the process, using the STS-32 mission as a typical sample case. The total pitching moment and moment coefficient (Cm) for that flight are calculated and compared with preflight predictions. The flight results show the anticipated decrease in Cm with increasing altitude. However, the total moment coefficient is less than predicted using preflight estimates.

Blanchard, R. C.↗

NASA NTRS1991

Comparison of four stable numerical methods for Abel's integral equation

The 3-D image reconstruction from cone-beam projections in computerized tomography leads naturally, in the case of radial symmetry, to the study of Abel-type integral equations. If the experimental information is obtained from measured data, on a discrete set of points, special methods are needed in order to restore continuity with respect to the data. A new combined Regularized-Adjoint-Conjugate Gradient algorithm, together with two different implementations of the Mollification Method (one based on a data filtering technique and the other on the mollification of the kernal function) and a regularization by truncation method (initially proposed for 2-D ray sample schemes and more recently extended to 3-D cone-beam image reconstruction) are extensively tested and compared for accuracy and numerical stability as functions of the level of noise in the data.

Murio, Diego A.↗

NASA NTRSDate not supplied

Application of Regolith Polymer Composite Fused Granular Fabrication Construction in Simulated Lunar Conditions

NASA’s Artemis program has the goal of creating a sustained lunar presence to provide unprecedented opportunities for scientific discovery and to ensure industry’s access to the unlimited resources and commercial potential in space. To achieve this goal, NASA must incrementally develop and expand its capabilities beyond the short lunar stays of the Apollo program to a robust continued presence with infrastructure and equipment to reduce mission risk. Kennedy Space Center’s Granular Mechanics and Regolith Operations laboratory (a.k.a. Swamp Works) has partnered with SpaceFactory and LERA Consulting Structural Engineers to develop the architectural and structural design of a robotically constructable unpressurized shelter. The shelter, called Lunar Infrastructure Asset (LINA), is designed to protect astronauts and surface assets from radiation, meteoroid impact, thermal gradients, and to withstand moonquakes. A Fused Granular Fabrication (FGF) construction process using regolith polymer composites was developed. The construction system and associated print parameters are discussed along with the environmental simulation equipment and a summary of test conditions. Test samples were printed in dirty thermal vacuum conditions (~10-3 torr, ~-200 °C,) and subscale versions of LINA were printed on a regolith simulant substrate in vacuum (~10-4 torr). Full scale LINA design optimization, simulation, and construction concept of operations are discussed.

lunar infrastructure↗

NASA NTRSDate not supplied

Selection, Production and Properties of Regolith Polymer Composite for Lunar Construction

lunar infrastructure↗

DOE OSTI2026

A variational framework for residual-based adaptivity in neural PDE solvers and operator learning

Residual-based adaptive strategies are widely used in scientific machine learning yet remain largely heuristic. We introduce a variational framework that formalizes these methods through convex transformations of the residual, where different transformations correspond to distinct objective functionals. For instance, exponential weights target uniform error minimization, while linear weights recover quadratic error minimization. This perspective reveals adaptive weighting as a means of selecting sampling distributions that optimize a primal objective, directly linking discretization choices to error metrics. This principled approach yields three key benefits: it enables systematic design of adaptive schemes, reduces discretization error by lowering estimator variance, and enhances learning dynamics by improving gradient signal-to-noise ratio. Extending the framework to operator learning, we demonstrate substantial performance gains across diverse optimizers and architectures. Our results provide a theoretical perspective for residual-based adaptivity and establish a foundation for principled discretization and training.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Weighted active space protocol for multireference machine-learned potentials

Multireference methods such as multiconfiguration pair-density functional theory accurately capture electronic correlation in systems with strong multiconfigurational character, but their cost precludes direct use in molecular dynamics. Combining these methods with machine-learned interatomic potentials (MLPs) can extend their reach. However, the sensitivity of multireference calculations to the choice of the active space complicates the consistent evaluation of energies and gradients across structurally diverse nuclear configurations. To overcome this limitation, we introduce the weighted active space protocol (WASP), a systematic approach to assign a consistent active space for a given system across uncorrelated configurations. By integrating WASP with MLPs and enhanced sampling techniques, we propose a data-efficient active learning cycle that enables the training of an MLP on multireference data. We demonstrated the approach on the TiC + -catalyzed C–H activation of methane, a reaction that poses challenges for Kohn–Sham density functional theory due to its significant multireference character. This framework enables accurate and efficient modeling of catalytic dynamics, establishing a paradigm for simulating complex reactive processes beyond the limits of conventional electronic-structure methods.

enhanced sampling↗

DOE OSTI2024

Reanalysis of NOAA H 2 observations: implications for the H 2 budget

Abstract. Hydrogen (H2) is a promising low-carbon alternative to fossil fuels for many applications. However, significant gaps in our understanding of the atmospheric H2 budget limit our ability to predict the impacts of greater H2 usage. Here we use NOAA H2 dry air mole fraction observations from air samples collected from ground-based and ship platforms during 2010–2019 to evaluate the representation of H2 in the NOAA GFDL-AM4.1 atmospheric chemistry-climate model. We find that the base model configuration captures the observed interhemispheric gradient well but underestimates the surface concentration of H2 by about 10 ppb. Additionally, the model fails to reproduce the 1–2 ppb yr−1 mean increase in surface H2 observed at background stations. We show that the cause is most likely an underestimation of current anthropogenic emissions, including potential leakages from H2-producing facilities. We also show that changes in soil moisture, soil temperature, and snow cover have most likely caused an increase in the magnitude of the soil sink, the most important removal mechanism for atmospheric H2, especially in the Northern Hemisphere. However, there remains uncertainty due to fundamental gaps in our understanding of H2 soil removal, such as the minimum moisture required for H2 soil uptake, for which we performed extensive sensitivity analyses. Finally, we show that the observed meridional gradient of the H2 mixing ratio and its seasonality can provide important constraints to test and refine parameterizations of the H2 soil sink.

Paulot, Fabien (ORCID:0000000175344922)↗