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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells

Nanoscale or single cell technologies are critical for biomedical applications. However, current mass spectrometry (MS)-based proteomic approaches require samples comprising a minimum of thousands of cells to provide in-depth profiling. Here, we report the development of a nanoPOTS (Nanodroplet Processing in One pot for Trace Samples) platform as a major advance in overall sensitivity. NanoPOTS dramatically enhances the efficiency and recovery of sample processing by downscaling processing volumes to <200 nL to minimize surface losses. When combined with ultrasensitive LC-MS, nanoPOTS allows identification of ~1500 to ~3,000 proteins from ~10 to ~140 cells, respectively. By incorporating the Match Between Runs algorithm of MaxQuant, >3000 proteins were consistently identified from as few as 10 cells. Furthermore, we demonstrate robust quantification of ~2400 proteins from single human pancreatic islet thin sections from type 1 diabetic and control donors, illustrating the application of nanoPOTS for spatially resolved proteome measurements from clinical tissues.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2015

Probing the deep nonlinear stage of the ablative Rayleigh-Taylor instability in indirect drive experiments on the National Ignition Facility

Academic tests in physical regimes not encountered in Inertial Confinement Fusion will help to build a better understanding of hydrodynamic instabilities and constitute the scientifically grounded validation complementary to fully integrated experiments. Under the National Ignition Facility (NIF) Discovery Science program, recent indirect drive experiments have been carried out to study the ablative Rayleigh-Taylor Instability (RTI) in transition from weakly nonlinear to highly nonlinear regime [A. Casner et al., Phys. Plasmas 19, 082708 (2012)]. In these experiments, a modulated package is accelerated by a 175 eV radiative temperature plateau created by a room temperature gas-filled platform irradiated by 60 NIF laser beams. The unique capabilities of the NIF are harnessed to accelerate this planar sample over much larger distances (≃1.4 mm) and longer time periods (≃12 ns) than previously achieved. This extended acceleration could eventually allow entering into a turbulent-like regime not precluded by the theory for the RTI at the ablation front. Simultaneous measurements of the foil trajectory and the subsequent RTI growth are performed and compared with radiative hydrodynamics simulations. We present RTI growth measurements for two-dimensional single-mode and broadband multimode modulations. The dependence of RTI growth on initial conditions and ablative stabilization is emphasized, and we demonstrate for the first time in indirect-drive a bubble-competition, bubble-merger regime for the RTI at ablation front.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Optimized dark matter searches in deep observations of Segue 1 with MAGIC

We present the results of stereoscopic observations of the satellite galaxy Segue 1 with the MAGIC Telescopes, carried out between 2011 and 2013. With almost 160 hours of good-quality data, this is the deepest observational campaign on any dwarf galaxy performed so far in the very high energy range of the electromagnetic spectrum. We search this large data sample for signals of dark matter particles in the mass range between 100 GeV and 20 TeV. For this we use the full likelihood analysis method, which provides optimal sensitivity to characteristic gamma-ray spectral features, like those expected from dark matter annihilation or decay. In particular, we focus our search on gamma-rays produced from different final state Standard Model particles, annihilation with internal bremsstrahlung, monochromatic lines and box-shaped signals. Our results represent the most stringent constraints to the annihilation cross-section or decay lifetime obtained from observations of satellite galaxies, for masses above few hundred GeV. In particular, our strongest limit (95% confidence level) corresponds to a ∼500 GeV dark matter particle annihilating into τ{sup +}τ{sup −}, and is of order <σ{sub ann}v>≃ 1.2×10{sup −24} cm{sup 3} s{sup −1} — a factor ∼40 above the <σ{sub ann}v>≃ thermal value.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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 OSTI2018

Deep SOAR follow-up photometry of two Milky Way outer-halo companions discovered with Dark Energy Survey

We report the discovery of a new star cluster, DES 3, in the constellation of Indus, and deeper observations of the previously identified satellite DES J0222.7−5217 (Eridanus III). DES 3 was detected as a stellar overdensity in first-year Dark Energy Survey data, and confirmed with deeper photometry from the 4.1-m Southern Astrophysical Research (SOAR) telescope. The new system was detected with a relatively high significance and appears in the DES images as a compact concentration of faint blue point sources. We determine that DES 3 is located at a heliocentric distance of ≃76.2 kpc and it is dominated by an old (≃9.8 Gyr) and metal-poor ([Fe/H] ≃ −1.84) population. While the age and metallicity values of DES 3 are comparable to typical globular clusters (objects with a high stellar density, stellar mass of ∼10^5 M_⊙ and luminosity ∼ −7.3), its half-light radius (r_h ∼ 6.87 pc) and luminosity (M_V ∼ −1.7) are more indicative of faint star cluster. Based on the angular size, DES 3, with a value of r_h ∼ 0$${^{\prime}_{.}}$$31, is among the smallest faint star clusters known to date. Furthermore, using deeper imaging of DES J0222.7−5217 taken with the SOAR telescope, we update structural parameters and perform the first isochrone modelling. Our analysis yields the first age (≃12.6 Gyr) and metallicity ([Fe/H] ≃ −2.01) estimates for this object. The half-light radius (r_h ≃ 11.24 pc) and luminosity (M_V ≃ −2.4) of DES J0222.7−5217 suggest that it is likely a faint star cluster. The discovery of DES 3 indicates that the census of stellar systems in the Milky Way is still far from complete, and demonstrates the power of modern wide-field imaging surveys to improve our knowledge of the Galaxy’s satellite population.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Deep learning: Extrapolation tool for ab initio nuclear theory

Ab initio approaches in nuclear theory, such as the no-core shell model (NCSM), have been developed for approximately solving finite nuclei with realistic strong interactions. The NCSM and other approaches require an extrapolation of the results obtained in a finite basis space to the infinite basis space limit and assessment of the uncertainty of those extrapolations. Each observable requires a separate extrapolation and many observables have no proven extrapolation method. Herein, we propose a feed-forward artificial neural network (ANN) method as an extrapolation tool to obtain the ground-state energy and the ground-state point-proton root-mean-square (rms) radius along with their extrapolation uncertainties. The designed ANNs are sufficient to produce results for these two very different observables in 6 Li from the ab initio NCSM results in small basis spaces that satisfy the following theoretical physics condition: independence of basis space parameters in the limit of extremely large matrices. Comparisons of the ANN findings with other extrapolation methods are also provided.

6Li↗
DOE OSTI2015

From deep TLS validation to ensembles of atomic models built from elemental motions

Procedures are described for extracting the vibration and libration parameters corresponding to a given set of TLS matrices and their simultaneous validation. Knowledge of these parameters allows the generation of structural ensembles corresponding to these matrices. The translation–libration–screw model first introduced by Cruickshank, Schomaker and Trueblood describes the concerted motions of atomic groups. Using TLS models can improve the agreement between calculated and experimental diffraction data. Because the T, L and S matrices describe a combination of atomic vibrations and librations, TLS models can also potentially shed light on molecular mechanisms involving correlated motions. However, this use of TLS models in mechanistic studies is hampered by the difficulties in translating the results of refinement into molecular movement or a structural ensemble. To convert the matrices into a constituent molecular movement, the matrix elements must satisfy several conditions. Refining the T, L and S matrix elements as independent parameters without taking these conditions into account may result in matrices that do not represent concerted molecular movements. Here, a mathematical framework and the computational tools to analyze TLS matrices, resulting in either explicit decomposition into descriptions of the underlying motions or a report of broken conditions, are described. The description of valid underlying motions can then be output as a structural ensemble. All methods are implemented as part of the PHENIX project.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗