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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Increasing aggregate size reduces single-cell organic carbon incorporation by hydrogel-embedded wetland microbes

Abstract Microbial degradation of organic carbon in sediments is impacted by the availability of oxygen and substrates for growth. To better understand how particle size and redox zonation impact microbial organic carbon incorporation, techniques that maintain spatial information are necessary to quantify elemental cycling at the microscale. In this study, we produced hydrogel microspheres of various diameters (100, 250, and 500 μm) and inoculated them with an aerobic heterotrophic bacterium isolated from a freshwater wetland (Flavobacterium sp.), and in a second experiment with a microbial community from an urban lacustrine wetland. The hydrogel-embedded microbial populations were incubated with 13C-labeled substrates to quantify organic carbon incorporation into biomass via nanoSIMS. Additionally, luminescent nanosensors enabled spatially explicit measurements of oxygen concentrations inside the microspheres. The experimental data were then incorporated into a reactive-transport model to project long-term steady-state conditions. Smaller (100 μm) particles exhibited the highest microbial cell-specific growth per volume, but also showed higher absolute activity near the surface compared to the larger particles (250 and 500 μm). The experimental results and computational models demonstrate that organic carbon availability was not high enough to allow steep oxygen gradients and as a result, all particle sizes remained well-oxygenated. Our study provides a foundational framework for future studies investigating spatially dependent microbial activity in aggregates using isotopically labeled substrates to quantify growth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Structural and Morphological Characteristics of Rare Earth Element-based MAX Phase and MXene

There has been a tremendous effort for the synthesis of crystalline MAX phases—a family of transition metal carbides and nitrides with a chemical formula of Mn+1AXn, where M is an early transition metal, A belongs to groups XIII – XVI in the periodic table, X is either C or N, and n can be in between 1 and 3. Furthermore, these materials have shown thermal and electrical conductance and have low density and high stiffness which open broad aspects of practical applications. Recently double transition metal MAX phases (M’ 2/3 M’’ 1/3 ) 2 AX - out-of-plane ordered, labeled as o-MAX and in-plane ordered, labeled as i-MAX, were reported. However, there are few reports on the synthesis and characterization of i-MAX and the corresponding MXenes. In this work, we synthesized Molybdenum-based rare earth containing i-MAX phase using arc melt technique. We then removed “A” element of i-MAX phase by acid treatment and sonication which resulted in two-dimensional (2D) transition metal carbides called MXenes. These MXenes offer high conductivity, hydrophilicity, magnetism, good dispersion ability in solvents, mechanical stability, and structural diversity with at least 100 stoichiometric MXene compositions and are recognized as multifunctional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Deep transfer learning for star cluster classification: I. application to the PHANGS– HST survey

ABSTRACT We present the results of a proof-of-concept experiment that demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in Hubble Space Telescope(HST) ultraviolet-optical imaging of nearby spiral galaxies ($D\lesssim 20\, \textrm{Mpc}$) in the Physics at High Angular Resolution in Nearby GalaxieS (PHANGS)–HST survey. Given the relatively small nature of existing, human-labelled star cluster samples, we transfer the knowledge of state-of-the-art neural network models for real-object recognition to classify star clusters candidates into four morphological classes. We perform a series of experiments to determine the dependence of classification performance on neural network architecture (ResNet18 and VGG19-BN), training data sets curated by either a single expert or three astronomers, and the size of the images used for training. We find that the overall classification accuracies are not significantly affected by these choices. The networks are used to classify star cluster candidates in the PHANGS–HST galaxy NGC 1559, which was not included in the training samples. The resulting prediction accuracies are 70 per cent, 40 per cent, 40–50 per cent, and 50–70 per cent for class 1, 2, 3 star clusters, and class 4 non-clusters, respectively. This performance is competitive with consistency achieved in previously published human and automated quantitative classification of star cluster candidate samples (70–80 per cent, 40–50 per cent, 40–50 per cent, and 60–70 per cent). The methods introduced herein lay the foundations to automate classification for star clusters at scale, and exhibit the need to prepare a standardized data set of human-labelled star cluster classifications, agreed upon by a full range of experts in the field, to further improve the performance of the networks introduced in this study.

Wei, Wei↗
DOE OSTI2020

Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ~2800 galaxies with visual classification from GZ1, we reach an accuracy of ~0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ~2.5 percent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Quantifying the poor purity and completeness of morphological samples selected by galaxy colour

The galaxy population is strongly bimodal in both colour and morphology, and the two measures correlate strongly, with most blue galaxies being late-types (spirals) and most early-types, typically ellipticals, being red. This observation has led to the use of colour as a convenient selection criterion to make samples that are then labelled by morphology. Such use of colour as a proxy for morphology results in necessarily impure and incomplete samples. In this paper, we make use of the morphological labels produced by Galaxy Zoo to measure how incomplete and impure such samples are, considering optical (ugriz), near-ultraviolet (NUV), and near-infrared (NIR; JHK) bands. The best single colour optical selection is found using a threshold of g - r = 0.742, but this still results in a sample where only 56 per cent of red galaxies are smooth and 56 per cent of smooth galaxies are red. Use of the NUV gives some improvement over purely optical bands, particularly for late-types, but still results in low purity/completeness for early-types. No significant improvement is found by adding NIR bands. With any two bands, including NUV, a sample of early-types with greater than two-thirds purity cannot be constructed. In conclusion, advances in quantitative galaxy morphologies have made colour–morphology proxy selections largely unnecessary going forward; where such assumptions are still required, we recommend studies carefully consider the implications of sample incompleteness/impurity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The stellar parameters and elemental abundances from low-resolution spectra – I. 1.2 million giants from LAMOST DR8

As a typical data-driven method, deep learning becomes a natural choice for analysing astronomical data. In this study, we built a deep convolutional neural network (NN) to estimate basic stellar parameters $T\rm {_{eff}}$, log g , metallicity ([M/H] and [Fe/H]) and [α/M] along with nine individual elemental abundances ([C/Fe], [N/Fe], [O/Fe], [Mg/Fe], [Al/Fe], [Si/Fe], [Ca/Fe], [Mn/Fe], and [Ni/Fe]). The NN is trained using common stars between the APOGEE survey and the LAMOST survey. We used low-resolution spectra from LAMOST survey as input, and measurements from APOGEE as labels. For stellar spectra with the signal-to-noise ratio in g band larger than 10 in the test set, the mean absolute error (MAE) is 29 K for $T\rm {_{eff}}$, 0.07 dex for log g , 0.03 dex for both [Fe/H] and [M/H], and 0.02 dex for [α/M]. The MAE of most elements is between 0.02 and 0.04 dex. The trained NN was applied to 1210 145 giants, including sub-giants, from LAMOST DR8 within the range of stellar parameters 3500 K < $T\rm {_{eff}}$ < 5500 K, 0.0 dex < log g < 4.0 dex, −2.5 dex < [Fe/H] < 0.5 dex. The distribution of our results in the chemical spaces is highly consistent with APOGEE labels and stellar parameters show consistency with external high-resolution measurements from GALAH. The results in this study allow us to further studies based on LAMOST data and deepen our understanding of the accretion and evolution history of the Milky Way. The electronic version of the value added catalog is available at http://www.lamost.org/dr8/v1.1/doc/vac.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Translation and rotation equivariant normalizing flow (TRENF) for optimal cosmological analysis

ABSTRACT Our Universe is homogeneous and isotropic, and its perturbations obey translation and rotation symmetry. In this work, we develop translation and rotation equivariant normalizing flow (TRENF), a generative normalizing flow (NF) model which explicitly incorporates these symmetries, defining the data likelihood via a sequence of Fourier space-based convolutions and pixel-wise non-linear transforms. TRENF gives direct access to the high dimensional data likelihood p(x|y) as a function of the labels y, such as cosmological parameters. In contrast to traditional analyses based on summary statistics, the NF approach has no loss of information since it preserves the full dimensionality of the data. On Gaussian random fields, the TRENF likelihood agrees well with the analytical expression and saturates the Fisher information content in the labels y. On non-linear cosmological overdensity fields from N-body simulations, TRENF leads to significant improvements in constraining power over the standard power spectrum summary statistic. TRENF is also a generative model of the data, and we show that TRENF samples agree well with the N-body simulations it trained on, and that the inverse mapping of the data agrees well with a Gaussian white noise both visually and on various summary statistics: when this is perfectly achieved the resulting p(x|y) likelihood analysis becomes optimal. Finally, we develop a generalization of this model that can handle effects that break the symmetry of the data, such as the survey mask, which enables likelihood analysis on data without periodic boundaries.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Weakly supervised anomaly detection in the Milky Way

ABSTRACT Large-scale astrophysics data sets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches. To this end, we demonstrate how Classification Without Labels (CWoLa), a weakly supervised anomaly detection method, can help identify cold stellar streams within the more than one billion Milky Way stars observed by the Gaia satellite. CWoLa operates without the use of labelled streams or knowledge of astrophysical principles. Instead, it uses a classifier to distinguish between mixed samples for which the proportions of signal and background samples are unknown. As a proof of concept, we demonstrate that this computationally lightweight strategy is able to detect both simulated streams and the known stream GD-1 in data. Originally designed for high-energy collider physics, this technique may have broad applicability within astrophysics as well as other domains interested in identifying localized anomalies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Subaru Hyper Suprime-Cam revisits the large-scale environmental dependence on galaxy morphology over 360 deg2 at z = 0.3–0.6

This study investigates the role of large-scale environments on the fraction of spiral galaxies at z = 0.3–0.6 sliced to three redshift bins of Δz = 0.1. Here, we sample 276220 massive galaxies in a limited stellar mass of 5 × 1010 solar mass (~M*) over 360 deg2, as obtained from the Second Public Data Release of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). By combining projected two-dimensional density information (Shimakawa et al. 2021, MNRAS, 503, 3896) and the CAMIRA cluster catalog (Oguri et al. 2018, PASJ, 70, S20), we investigate the spiral fraction across large-scale overdensities and in the vicinity of red sequence clusters. We adopt transfer learning to reduce the cost of labeling spiral galaxies significantly and then perform stacking analysis across the entire field to overcome the limitations of sample size. Here we employ a morphological classification catalog by the Galaxy Zoo Hubble (Willett et al., 2017, MNRAS, 464, 4176) to train the deep learning model. Based on 74103 sources classified as spirals, we find moderate morphology–density relations on a 10 comoving Mpc scale, thanks to the wide-field coverage of HSC-SSP. Clear deficits of spiral galaxies have also been confirmed, in and around 1136 red sequence clusters. Furthermore, we verify whether there is a large-scale environmental dependence on rest-frame u - r colors of spiral galaxies; such a tendency was not observed in our sample.

Astronomy & Astrophysics↗
DOE OSTI2021

Where’s Swimmy?: Mining unique color features buried in galaxies by deep anomaly detection using Subaru Hyper Suprime-Cam data

Abstract We present the Swimmy (Subaru WIde-field Machine-learning anoMalY) survey program, a deep-learning-based search for unique sources using multicolored (grizy) imaging data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). This program aims to detect unexpected, novel, and rare populations and phenomena, by utilizing the deep imaging data acquired from the wide-field coverage of the HSC-SSP. This article, as the first paper in the Swimmy series, describes an anomaly detection technique to select unique populations as “outliers” from the data-set. The model was tested with known extreme emission-line galaxies (XELGs) and quasars, which consequently confirmed that the proposed method successfully selected $\sim\!\! 60\%$–$70\%$ of the quasars and $60\%$ of the XELGs without labeled training data. In reference to the spectral information of local galaxies at z = 0.05–0.2 obtained from the Sloan Digital Sky Survey, we investigated the physical properties of the selected anomalies and compared them based on the significance of their outlier values. The results revealed that XELGs constitute notable fractions of the most anomalous galaxies, and certain galaxies manifest unique morphological features. In summary, deep anomaly detection is an effective tool that can search rare objects, and, ultimately, unknown unknowns with large data-sets. Further development of the proposed model and selection process can promote the practical applications required to achieve specific scientific goals.

Astronomy & Astrophysics↗
DOE OSTI2021

Return of the Lemnaceae: duckweed as a model plant system in the genomics and postgenomics era

Abstract The aquatic Lemnaceae family, commonly called duckweed, comprises some of the smallest and fastest growing angiosperms known on Earth. Their tiny size, rapid growth by clonal propagation, and facile uptake of labeled compounds from the media were attractive features that made them a well-known model for plant biology from 1950 to 1990. Interest in duckweed has steadily regained momentum over the past decade, driven in part by the growing need to identify alternative plants from traditional agricultural crops that can help tackle urgent societal challenges, such as climate change and rapid population expansion. Propelled by rapid advances in genomic technologies, recent studies with duckweed again highlight the potential of these small plants to enable discoveries in diverse fields from ecology to chronobiology. Building on established community resources, duckweed is reemerging as a platform to study plant processes at the systems level and to translate knowledge gained for field deployment to address some of society’s pressing needs. This review details the anatomy, development, physiology, and molecular characteristics of the Lemnaceae to introduce them to the broader plant research community. We highlight recent research enabled by Lemnaceae to demonstrate how these plants can be used for quantitative studies of complex processes and for revealing potentially novel strategies in plant defense and genome maintenance.

Biochemistry & Molecular Biology↗
DOE OSTI2021

The metabolic origins of non-photorespiratory CO 2 release during photosynthesis: a metabolic flux analysis

Respiration in the light (R L ) releases CO 2 in photosynthesizing leaves and is a phenomenon that occurs independently from photorespiration. Since R L lowers net carbon fixation, understanding R L could help improve plant carbon-use efficiency and models of crop photosynthesis. Although R L was identified more than 75 years ago, its biochemical mechanisms remain unclear. To identify reactions contributing to R L , we mapped metabolic fluxes in photosynthesizing source leaves of the oilseed crop and model plant camelina (Camelina sativa). We performed a flux analysis using isotopic labeling patterns of central metabolites during 13 CO 2 labeling time course, gas exchange, and carbohydrate production rate experiments. To quantify the contributions of multiple potential CO 2 sources with statistical and biological confidence, we increased the number of metabolites measured and reduced biological and technical heterogeneity by using single mature source leaves and quickly quenching metabolism by directly injecting liquid N 2 ; we then compared the goodness-of-fit between these data and data from models with alternative metabolic network structures and constraints. Our analysis predicted that R L releases 5.2 μmol CO2 g –1 FW h –1 of CO 2 , which is relatively consistent with a value of 9.3 μmol CO 2 g –1 FW h –1 measured by CO 2 gas exchange. The results indicated that ≤10% of R L results from TCA cycle reactions, which are widely considered to dominate R L . Further analysis of the results indicated that oxidation of glucose-6-phosphate to pentose phosphate via 6-phosphogluconate (the G6P/OPP shunt) can account for >% of CO 2 released by R L .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Hamiltonian learning using machine-learning models trained with continuous measurements

Here, we build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Interpretable machine learning study of the many-body localization transition in disordered quantum Ising spin chains

We apply support vector machine (SVM) to study the phase transition between many-body localized and thermal phases in a disordered quantum Ising chain in a transverse external field. The many-body eigenstate energy E is bounded by a bandwidth W = E max – E min . The transition takes place on a phase diagram spanned by the energy density ε = 2(E – E min )/W and the disorder strength δJ of the spin interaction uniformly distributed within [ – δJ, δJ], formally parallel to the mobility edge in Anderson localization. In our paper, we use the labeled probability density of eigenstate wave functions belonging to the deeply localized and thermal regimes at two different energy densities (ε's) as the training set, i.e., providing labeled data at four corners of the phase diagram. Then we employ the trained SVM to predict the whole phase diagram. The obtained phase boundary qualitatively agrees with previous work using entanglement entropy to characterize these two phases. We further analyze the decision function of the SVM to interpret its physical meaning and find that it is analogous to the inverse participation ratio in configuration space. Our findings demonstrate the ability of the SVM to capture potential quantities that may characterize the many-body localization phase transition.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Graph neural network for neutrino physics event reconstruction

Liquid argon time projection chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its full potential requires sophisticated automated reconstruction techniques. Here, this article describes NUGRAPH 2, a graph neural network for low-level reconstruction of simulated neutrino interactions in a LArTPC detector. Simulated neutrino interactions in the MicroBooNE detector geometry are described as heterogeneous graphs, with energy depositions on each detector plane forming nodes on planar subgraphs. The network utilizes a multihead attention message-passing mechanism to perform background filtering and semantic labeling on these graph nodes, identifying those associated with the primary physics interaction with 98.0% efficiency and labeling them according to particle type with 94.9% efficiency. The network operates directly on detector observables across multiple two-dimensional representations but utilizes a three-dimensional-context-aware mechanism to encourage consistency between these representations. Model inference takes 0.12 s / event on a CPU and 0.005 s / event batched on a GPU. This architecture is designed to be a general-purpose solution for particle reconstruction in neutrino physics, with the potential for deployment across a broad range of detector technologies, and offers a core convolution engine that can be leveraged for a variety of tasks beyond the two described in this paper.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Diffraction pattern simulation of cellulose fibrils using distributed and quantized pair distances

Intensity simulation of X-ray scattering from large twisted cellulose molecular fibrils is important in understanding the impact of chemical or physical treatments on structural properties such as twisting or coiling. This paper describes a highly efficient method for the simulation of X-ray diffraction patterns from complex fibrils using atom-type-specific pair-distance quantization. Pair distances are sorted into arrays which are labelled by atom type. Histograms of pair distances in each array are computed and binned and the resulting population distributions are used to represent the whole pair-distance data set. These quantized pair-distance arrays are used with a modified and vectorized Debye formula to simulate diffraction patterns. This approach utilizes fewer pair distances in each iteration, and atomic scattering factors are moved outside the iteration since the arrays are labelled by atom type. As a result, this algorithm significantly reduces the computation time while maintaining the accuracy of diffraction pattern simulation, making possible the simulation of diffraction patterns from large twisted fibrils in a relatively short period of time, as is required for model testing and refinement.

09 BIOMASS FUELS↗
DOE OSTI2018

Neutron scattering in the biological sciences: progress and prospects

The scattering of neutrons can be used to provide information on the structure and dynamics of biological systems on multiple length and time scales. Pursuant to a National Science Foundation-funded workshop in February 2018, recent developments in this field are reviewed here, as well as future prospects that can be expected given recent advances in sources, instrumentation and computational power and methods. Crystallography, solution scattering, dynamics, membranes, labeling and imaging are examined. For the extraction of maximum information, the incorporation of judicious specific deuterium labeling, the integration of several types of experiment, and interpretation using high-performance computer simulation models are often found to be particularly powerful.

59 BASIC BIOLOGICAL SCIENCES↗