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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Spatio-Temporal Variability of Groundwater Storage in India

Groundwater level measurements from 3907 monitoring wells, distributed within 22 major river basins of India, are assessed to characterize their spatial and temporal variability. Ground water storage (GWS) anomalies (relative to the long-term mean) exhibit strong seasonality, with annual maxima observed during the monsoon season and minima during pre-monsoon season. Spatial variability of GWS anomalies increases with the extent of measurements, following the power law relationship, i.e., log-(spatial variability) is linearly dependent on log-(spatial extent).In addition, the impact of well spacing on spatial variability and the power law relationship is investigated. We found that the mean GWS anomaly sampled at a 0.25 degree grid scale closes to unweighted average over all wells. The absolute error corresponding to each basin grows with increasing scale, i.e., from 0.25 degree to 1 degree. It was observed that small changes in extent could create very large changes in spatial variability at large grid scales. Spatial variability of GWS anomaly has been found to vary with climatic conditions. To our knowledge, this is the first study of the effects of well spacing on groundwater spatial variability. The results may be useful for interpreting large scale groundwater variations from unevenly spaced or sparse groundwater well observations or for siting and prioritizing wells in a network for groundwater management. The output of this study could be used to maintain a cost effective groundwater monitoring network in the study region and the approach can also be used in other parts of the globe.

Groundwater monitoring↗
DOE OSTI2021

Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE

We present the performance of a semantic segmentation network, SparseSSNet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. SparseSSNet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNE's ν e -appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are re-classified into two classes more relevant to the current analysis. The output of SparseSSNet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. Here, the accuracy achieved on the test sample is ≥ 99%. For full neutrino interaction simulations, the time for processing one image is ≈ 0.5 sec, the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Quantification of a Minimum Detectable Soiling Level to Affect Photovoltaic Devices by Natural and Simulated Soils

Here, we report that soil accumulation on photovoltaic (PV) modules presents a challenge to long-term performance prediction and lifetime estimates due to the inherent difficulty in quantifying small changes over an extended period. Low mass loadings of soil are a common occurrence but remain difficult to quantify. In order to more accurately describe the specific effects of sparse soil films on PV systems, we have expanded upon an earlier technique to measure the optical losses due to an artificially applied obscurant film. A synthetic soil analog was sprayed onto glass coupons at very brief intervals with a high-volume, low-pressure pneumatic sprayer. Light transmission through the grime film was evaluated using a quantum efficiency test stand and UV/vis spectroscopy. A 0.1-g/m 2 grime loading was determined to be the limit of mass measurement sensitivity, which is similar to some reports of daily soil accumulation. Predictable, linear decreases in transmission were observed for samples with a mass loading between 0.1 and 0.5 g/m 2 . A similar change was observed for soiled coupons from an outdoor monitoring station. Collected soil from the field coupons was analyzed to develop a compositional analog for indoor studies. Lastly, natural and synthetic soils produced similar decreases in transmission.

14 SOLAR ENERGY↗
DOE OSTI2014

Temperature dependence of the radiative recombination coefficient in crystalline silicon from spectral photoluminescence

The radiative recombination coefficient B(T) in crystalline silicon is determined for the temperature range 90–363 K, and in particular from 270 to 350 K with an interval of 10 K, where only sparse data are available at present. The band-band absorption coefficient established recently by Nguyen et al. [J. Appl. Phys. 115, 043710 (2014)] via photoluminescence spectrum measurements is employed to compute the values of B(T) at various temperatures. The results agree very well with literature data from Trupke et al. [J. Appl. Phys. 94, 4930 (2003).] We present a polynomial parameterization describing the temperature dependence of the product of B(T) and the square of the intrinsic carrier density. We also find that B(T) saturates at a near constant value at room temperature and above for silicon samples with relatively low free carrier densities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Evaluating the Capability of High-Altitude Infrasound Platforms to Cover Gaps in Existing Networks.

A variety of Earth surface and atmospheric sources generate low frequency sound waves that can travel great distances. Despite a rich history of ground-based sensor studies, very few experiments have investigated the prospects of free floating microphone arrays at high altitudes. However, recent initiatives have shown that such networks have very low background noise and may sample an acoustic wave field that is fundamentally different than that at the Earth's surface. The experiments have been limited to at most two stations at altitude, limiting their utility in acoustic event detection and localization. We describe the deployment of five drifting microphone stations at altitudes between 21 and 24 km above sea level. The stations detected one of two regional ground-based explosions as well as the ocean microbarom while traveling almost 500 km across the American Southwest. The explosion signal consisted of multiple arrivals; signal amplitudes did not correlate with sensor elevation or source range. A sparse network method that employed curved wave front corrections was able to determine the backazimuth from the free flying network to the acoustic source. Episodic broad band signals similar to those seen on previous flights in the same region were noted as well, but their source remains unclear. Background noise levels were commensurate with those on infrasound stations in the International Monitoring System (IMS) below 2 seconds, but sensor self noise appears to dominate at higher frequencies.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Video-rate Raman-based metabolic imaging by Airy light-sheet illumination and photon-sparse detection

Despite its massive potential, Raman imaging represents just a modest fraction of all research and clinical microscopy to date. This is due to the ultralow Raman scattering cross-sections of most biomolecules that impose low-light or photon-sparse conditions. Bioimaging under such conditions is suboptimal, as it either results in ultralow frame rates or requires increased levels of irradiance. Here, we overcome this tradeoff by introducing Raman imaging that operates at both video rates and 1,000-fold lower irradiance than state-of-the-art methods. To accomplish this, we deployed a judicially designed Airy light-sheet microscope to efficiently image large specimen regions. Further, we implemented subphoton per pixel image acquisition and reconstruction to confront issues arising from photon sparsity at just millisecond integrations. We demonstrate the versatility of our approach by imaging a variety of samples, including the three-dimensional (3D) metabolic activity of single microbial cells and the underlying cell-to-cell variability. To image such small-scale targets, we again harnessed photon sparsity to increase magnification without a field-of-view penalty, thus, overcoming another key limitation in modern light-sheet microscopy.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2016

Space Weathering of Olivine: Samples, Experiments and Modeling

Olivine is a major constituent of chondritic bodies and its response to space weathering processes likely dominates the optical properties of asteroid regoliths (e.g. S- and many C-type asteroids). Analyses of olivine in returned samples and laboratory experiments provide details and insights regarding the mechanisms and rates of space weathering. Analyses of olivine grains from lunar soils and asteroid Itokawa reveal that they display solar wind damaged rims that are typically not amorphized despite long surface exposure ages, which are inferred from solar flare track densities (up to 10 (sup 7 y)). The olivine damaged rim width rapidly approaches approximately 120 nm in approximately 10 (sup 6 y) and then reaches steady-state with longer exposure times. The damaged rims are nanocrystalline with high dislocation densities, but crystalline order exists up to the outermost exposed surface. Sparse nanophase Fe metal inclusions occur in the damaged rims and are believed to be produced during irradiation through preferential sputtering of oxygen from the rims. The observed space weathering effects in lunar and Itokawa olivine grains are difficult to reconcile with laboratory irradiation studies and our numerical models that indicate that olivine surfaces should readily blister and amorphize on relatively short time scales (less than 10 (sup 3 y)). These results suggest that it is not just the ion fluence alone, but other variable, the ion flux that controls the type and extent of irradiation damage that develops in olivine. This flux dependence argues for caution in extrapolating between high flux laboratory experiments and the natural case. Additional measurements, experiments, and modeling are required to resolve the discrepancies among the observations and calculations involving solar wind processing of olivine.

Keller, L. P.↗
NASA NTRS1979

A summary of research on the NASA-Global Atmospheric Sampling Program performed by the Atmospheric Sciences Research Center

The annual variations of ozone near the tropopause are derived from aircraft exhibit year-to-year differences which are not explicitly accounted for by the simple, classical ozone transport theory. Phenomena such as tropopause lifting, interannual variations in the rates of stratospheric-tropospheric exchange and meridional mixing, contribute differently to the distribution of ozone in this altitude region. Ozone encounter climatologies have been represented by global maps which show the probabilities of exceeding ambient ozone levels of 200, 300, and 400 ppbV along flight routes during the year. Continuous ozone records obtained from the GASP system revealed the presence of gravity waves whose wavelength is of the order 20 km. The GASP data cannot, however, be utilized for the evaluation of horizontal fluxes of such quantities as ozone, sensible heat, and zonal momentum; the data are too sparsely and irregularly distributed for the computation of stable correlations. Multiple species data from the unique circumglobal flight of a Pan American airliner on 28-30 October 1977 are discussed with particular regard to the apparent interhemispheric differences in tropospheric species concentrations, variation between the Arctic and Antarctic stratospheres, to possible covariations between species, and to potential source regions for various constituents.

Falconer, P. D.↗
DOE OSTI2023

Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making

One of the great challenges with decision making tasks on real world systems is the fact that data is sparse and acquiring additional data is expensive. In these cases, it is often crucial to make a model of the environment to assist in making decisions. At the same time, limited data means that learned models are erroneous, making it just as important to equip the model with good predictive uncertainties. In the context of learning sequential decision making policies, these uncertainties can prove useful for informing which data to collect for the greatest improvement in policy performance \citep{mehta2021experimental, mehta2022exploration} or informing the policy about unsure regions of state and action space to avoid during test time \citep{yu2020mopo}. Additionally, assuming that realistic samples of the environment can be drawn, an adaptable policy can be trained that attempts to make optimal decisions for any given possible instance of the environment \citep{ghosh2022offline, chen2021offline}. In this work, we examine the so-called ``probabilistic neural network'' (PNN) model that is ubiquitous in model-based reinforcement learning (MBRL) works. We argue that while PNN models may have good marginal uncertainties, they form a distribution of non-smooth transition functions. Not only are these samples unrealistic and may hamper adaptability, but we also assert that this leads to poor uncertainty estimates when predicting multiple step trajectory estimates. To address this issue, we propose a simple sampling method that can be implemented on top of pre-existing models.We evaluate our sampling technique on a number of environments, including a realistic nuclear fusion task, and find that, not only do smooth transition function samples produce more calibrated uncertainties, but they also lead to better downstream performance for an adaptive policy.

Offline Reinforcement Learning↗
DOE OSTI2019

Approximate Bayesian Model Inversion for PDEs with Heterogeneous and State-Dependent Coefficients

We present two approximate Bayesian inference methods for parameter estimation in partial differential equation (PDE) models with space-dependent and state-dependent parameters. We demonstrate that these methods provide accurate and cost-effective alternatives to Markov Chain Monte Carlo simulation. We assume a parameterized Gaussian prior on the unknown functions, and approximate the posterior density by a parameterized multivariate Gaussian density. The parameters of the prior and posterior are estimated from sparse observations of the PDE model's states and the unknown functions themselves by maximizing the evidence lower bound (ELBO), a lower bound on the log marginal likelihood of the observations. The first method, Laplace-EM, employs the expectation maximization algorithm to maximize the ELBO, with a Laplace approximation of the posterior on the E-step, and minimization of a Kullback-Leibler divergence on the M-step. The second method, DSVI-EB, employs the doubly stochastic variational inference (DSVI) algorithm, in which the ELBO is maximized via gradient-based stochastic optimization, with nosiy gradients computed via simple Monte Carlo sampling and Gaussian backpropagation. We apply these methods to identifying diffusion coefficients in linear and nonlinear diffusion equations, and we find that both methods provide accurate estimates of posterior densities and the hyperparameters of Gaussian priors. While the Laplace-EM method is more accurate, it requires computing Hessians of the physics model. The DSVI-EB method is found to be less accurate but only requires gradients of the physics model.

approximate Bayesian inference, model inversion, v↗
DOE OSTI2019

Statistics for Testing Under Adverse Conditions

Here we develop a statistical basis for limited adverse testing. This type of testing simultaneously evaluates system performances against minimum requirements and minimizes costs, particularly for large scale engineering projects. Because testing is often expensive and narrow in scope, the data obtained is relatively limited—precisely the opposite of the recent “large data” movement but no less compelling. Although a remarkably common approach for industrial and large-scale government projects, a statistical basis for adverse testing remains poorly explored. Here we prove mathematically, under specific conditions, that setting each independent variable to an adverse condition leads to a similar level of adversity in the dependent variable. For example, setting all normally distributed independent variables to at least their 95th percentile values leads to a result at the 95th percentile. The analysis considers sample size estimates to clarify the value of replicates in this type of testing, determines how many of the independent variables must be set to adverse condition values, and highlights the essential assumptions, so that engineers, statisticians, and subject matter experts know when this statistical framework may be applied successfully.

42 ENGINEERING↗
NASA NTRS2016

TEM Analyses of Itokawa Regolith Grains and Lunar Soil Grains to Directly Determine Space Weathering Rates on Airless Bodies

Samples returned from the moon and Asteroid Itokawa by NASA's Apollo Missions and JAXA's Hayabusa Mission, respectively, provide a unique record of their interaction with the space environment. Space weathering effects result from micrometeorite impact activity and interactions with the solar wind. While the effects of solar wind interactions, ion implantation and solar flare particle track accumulation, have been studied extensively, the rate at which these effects accumulate in samples on airless bodies has not been conclusively determined. Results of numerical modeling and experimental simulations do not converge with observations from natural samples. We measured track densities and rim thicknesses of three olivine grains from Itokawa and multiple olivine and anorthite grains from lunar soils of varying exposure ages. Samples were prepared for analysis using a Leica EM UC6 ultramicrotome and an FEI Quanta 3D dual beam focused ion beam scanning electron microscope (FIB-SEM). Transmission electron microscope (TEM) analyses were performed on the JEOL 2500SE 200kV field emission STEM. The solar wind damaged rims on lunar anorthite grains are amorphous, lack inclusions, and are compositionally similar to the host grain. The rim width increases as a smooth function of exposure age until it levels off at approximately 180 nm after approximately 20 My (Fig. 1). While solar wind ion damage can only accumulate while the grain is in a direct line of sight to the Sun, solar flare particles can penetrate to mm-depths. To assess whether the track density accurately predicts surface exposure, we measured the rim width and track density in olivine and anorthite from the surface of rock 64455, which was never buried and has a surface exposure age of 2 My based on isotopic measurements. The rim width from 64455 (60-70nm) plots within error of the well-defined trend for solar wind amorphized rims in Fig. 1. Measured solar flare track densities are accurately reflecting the surface exposure of the grains. Track densities correlate with the amorphous rim thicknesses. While the space-weathered rims of anorthite grains are amorphous, the space-weathered rims on both Itokawa and lunar olivine grains show solar wind damaged rims that are not amorphous. Instead, the rims are nanocrystalline with high dislocation densities and sparse inclusions of nanophase Fe metal. The rim thicknesses on the olivine grains also correlate with track density. The Itokawa olivine grains have track densities that indicate surface exposures of approximately 10(exp 5) years. Longer exposures (up to approximately 10(exp 7) years) do not amorphize the rims, as evidenced by lunar soil olivines with high track densities (approximately 10(exp 11) cm(exp -2)). From the combined data, shown in Fig. 1, it is clear that olivine is damaged (but not amorphized) more rapidly by the solar wind compared to anorthite. The olivine damaged rim forms quickly (in approximately 10(exp 6) y) and saturates at approximately 120nm with longer exposure time. The anorthite damaged rims form more slowly, amorphize, and grow thicker than the olivine rims. This is in agreement with numerical modeling data which predicts that solar wind damaged rims on anorthite will be thicker than olivine. However, the models predict that both olivine and anorthite rims will amorphize and reach equilibrium widths in less than 10(exp 3) y, in contrast to what is observed for natural samples. Laboratory irradiation experiments, which show rapid formation of fully amorphous and blistered surfaces from simulated solar wind exposures are also in contrast to observations of natural samples. These results suggest that there is a flux dependence on the type and extent of irradiation damage that develops in olivine. This flux dependence suggests that great caution be used in extrapolating between high-flux laboratory experiments and the natural case, as demonstrated by. We constrain the space weathering rate through analysis of returned samples. Provided that the track densities and the solar wind damaged rim widths exhibited by the Itokawa grains are typical of the fine-grained regions of Itokawa, then the space weathering rate is on the order of 10(exp 5) y. Space weathering effects in lunar soils saturate within a few My of exposure while those in Itokawa regolith grains formed in approximately 10(exp 5) y. Olivine and anorthite respond differently to solar wind irradiation. The space weathering effects in olivine are particularly difficult to reconcile with laboratory irradiation studies and numerical models. Additional measurements, experiments, and modeling are required to resolve the discrepancies among the observations and calculations involving solar wind amorphization of different minerals on airless bodies.

Berger, Eve L.↗
DOE OSTI2016

THE NUCLEAR ACTIVITIES OF NEARBY S0 GALAXIES

We present a study of nuclear activities in nearby S0 galaxies. After cross-matching the Sloan Digital Sky Survey Data Release 7 with the Third Reference Catalog of Bright Galaxies (RC3) and visually checking the SDSS images, we derive a sample of 583 S0 galaxies with the central spectrophotometric information. In order to separate nebular emission lines from the underlying stellar contribution, we fit the stellar population model to the SDSS spectra of these S0 galaxies. According to the BPT diagram, we find that 8% of S0 galaxies show central star-forming activity, while the fractions of Seyfert, Composite, and low-ionization nuclear emission-line regions (LINERs) are 2%, 8%, and 21.4%, respectively. We also find that star-forming S0s have the lowest stellar masses, over one magnitude lower than the others, and that the active S0s are mainly located in the sparse environment, while the normal S0s are located in the dense environment, which might suggest that the environment plays an important role in quenching star formation and/or AGN activity in S0 galaxies. By performing bulge-disk decomposition of 45 star-forming S0s in g - and r -bands with the 2D fitting software Galfit, as well as exploiting the catalog of 2D photometric decompositions of Meert et al., we find that the bulges of approximately one-third of star-forming S0 galaxies (16/45) are bluer than their disks, while for other types of S0s the bulge and disk components show similar color distributions. Besides, the Sérsic index of most star-forming S0s bulges is less than two, while for normal S0s, it is between two and six.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

STAR COUNT DENSITY PROFILES AND STRUCTURAL PARAMETERS OF 26 GALACTIC GLOBULAR CLUSTERS

We used an appropriate combination of high-resolution Hubble Space Telescope observations and wide-field, ground-based data to derive the radial stellar density profiles of 26 Galactic globular clusters from resolved star counts (which can be all freely downloaded on-line). With respect to surface brightness (SB) profiles (which can be biased by the presence of sparse, bright stars), star counts are considered to be the most robust and reliable tool to derive cluster structural parameters. For each system, a detailed comparison with both King and Wilson models has been performed and the most relevant best-fit parameters have been obtained. This collection of data represents the largest homogeneous catalog collected so far of star count profiles and structural parameters derived therefrom. The analysis of the data of our catalog has shown that (1) the presence of the central cusps previously detected in the SB profiles of NGC 1851, M13, and M62 is not confirmed; (2) the majority of clusters in our sample are fit equally well by the King and the Wilson models; (3) we confirm the known relationship between cluster size (as measured by the effective radius) and galactocentric distance; (4) the ratio between the core and the effective radii shows a bimodal distribution, with a peak at {approx}0.3 for about 80% of the clusters and a secondary peak at {approx}0.6 for the remaining 20%. Interestingly, the main peak turns out to be in agreement with that expected from simulations of cluster dynamical evolution and the ratio between these two radii correlates well with an empirical dynamical-age indicator recently defined from the observed shape of blue straggler star radial distribution, thus suggesting that no exotic mechanisms of energy generation are needed in the cores of the analyzed clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The BIRDIES Experiment: Measuring Beryllium Isotopes to Resolve Dynamics in the Stratosphere

Cosmogenic beryllium-10 and beryllium-7, and the ratio of the two (10Be/7Be), are powerful atmospheric tracers of stratosphere–troposphere exchange (STE) processes; however, measurements are sparse for altitudes well above the tropopause. We present a novel high-altitude balloon campaign aimed to measure these isotopes in the mid-stratosphere called Beryllium Isotopes for Resolving Dynamics in the Stratosphere (BIRDIES). BIRDIES targeted gravity waves produced by tropopause-overshooting convection to study their propagation and impact on STE dynamics, including the production of turbulence in the stratosphere. Two custom-designed payloads called FiSH and GASP were flown at altitudes approaching 30 km to measure in situ turbulence and beryllium isotopes (on aerosols), respectively. These were flown on nine high-altitude balloon flights over Kansas, USA, in summer 2022. The atmospheric samples were augmented with a ground-based rainfall collection targeting isotopic signatures of deep convection overshooting. Our GASP samples yielded mostly negligible amounts of both 10Be and 7Be collected in the mid-stratosphere but led to design improvements to increase aerosol capture in low-pressure environments. Observations from FiSH and the precipitation collection were more fruitful. FiSH showed the presence of turbulent velocity, temperature, and acoustic fluctuations in the stratosphere, including length scales in the infra-sonic range and inertial subrange that indicated times of elevated turbulence. The precipitation collection, and subsequent statistical analysis, showed that large spatial datasets of 10Be/7Be can be measured in individual rainfall events with minimum terrestrial contamination. While the spatial patterns in rainfall suggested some evidence for overshooting convection, inter-event temporal variability was clearly observed and predicted with good agreement using the 3D chemical transport model GEOS-CHEM.

58 GEOSCIENCES↗
DOE OSTI2023

Building Alternative Indices of Socioeconomic Status for Population Modeling in Data-Sparse Contexts (Short Paper)

Population modeling requires clear definitions of socioeconomic status (SES) to ensure overall estimate accuracy and locate potentially underserved subpopulations. This presents a challenge as SES can be measured in myriad ways and for divergent purposes, and the data required to calculate these metrics may be lacking, particularly in low and middle income countries (LMICs). To support more refined SES measurement, we explore improvements upon the Demographic and Health Survey’s (DHS) Wealth Index (DHS-WI) using alternative characterizations of SES based on multiple correspondence analysis (MCA) and hierarchical clustering. We produce the MCA-derived metrics first on a full suite of household economic, demographic, and dwelling variables, then on a reduced set of occupant-only SES characteristics. We explore the utility of these metrics relative to DHS-WI based on their ability to 1) differentiate DHS household types and 2) identify mixtures of SES levels within DHS samples and mapped at high spatial resolution. We find that our full suite MCA yields more clearly defined SES segments and that our reduced MCA delineates occupant SES most clearly, suggesting potential pathways to improve upon the DHS-WI in future population modeling efforts for LMICs.

Cunningham, Angela↗
DOE OSTI2022

Inference of bipolar neutrino flavor oscillations near a core-collapse supernova based on multiple measurements at Earth

Neutrinos in compact-object environments, such as core-collapse supernovae, can experience various kinds of collective effects in flavor space, engendered by neutrino-neutrino interactions. These include “bipolar” collective oscillations, which are exhibited by neutrino ensembles where different flavors dominate at different energies. Considering the importance of neutrinos in the dynamics and nucleosynthesis in these environments, it is desirable to ascertain whether an Earth-based detection could contain signatures of bipolar oscillations that occurred within a supernova envelope. To that end, we, in this study, continue examining a cost-function formulation of statistical data assimilation (SDA) to infer solutions to a small-scale model of neutrino flavor transformation. SDA is an inference paradigm designed to optimize a model with sparse data. Our model consists of two monoenergetic neutrino beams with different energies emanating from a source and coherently interacting with each other and with a matter background, with radially varying interaction strengths. We attempt to infer flavor transformation histories of these beams using simulated measurements of the flavor content at locations “in vacuum” (that is, far from the source), which could in principle correspond to Earth-based detectors. Within the scope of this small-scale model, we found that: (i) based on such measurements, the SDA procedure is able to infer whether bipolar oscillations had occurred within the protoneutron star envelope, and (ii) if the measurements sample the full amplitude of the neutrino oscillations in vacuum, then the amplitude of the prior bipolar oscillations is well predicted. This result intimates that the inference paradigm can well complement numerical integration codes, via its ability to infer flavor evolution at physically inaccessible locations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

ON MACHINE-LEARNED CLASSIFICATION OF VARIABLE STARS WITH SPARSE AND NOISY TIME-SERIES DATA

With the coming data deluge from synoptic surveys, there is a need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly observed variables based on small numbers of time-series measurements. In this paper, we introduce a methodology for variable-star classification, drawing from modern machine-learning techniques. We describe how to homogenize the information gleaned from light curves by selection and computation of real-numbered metrics (features), detail methods to robustly estimate periodic features, introduce tree-ensemble methods for accurate variable-star classification, and show how to rigorously evaluate a classifier using cross validation. On a 25-class data set of 1542 well-studied variable stars, we achieve a 22.8% error rate using the random forest (RF) classifier; this represents a 24% improvement over the best previous classifier on these data. This methodology is effective for identifying samples of specific science classes: for pulsational variables used in Milky Way tomography we obtain a discovery efficiency of 98.2% and for eclipsing systems we find an efficiency of 99.1%, both at 95% purity. The RF classifier is superior to other methods in terms of accuracy, speed, and relative immunity to irrelevant features; the RF can also be used to estimate the importance of each feature in classification. Additionally, we present the first astronomical use of hierarchical classification methods to incorporate a known class taxonomy in the classifier, which reduces the catastrophic error rate from 8% to 7.8%. Excluding low-amplitude sources, the overall error rate improves to 14%, with a catastrophic error rate of 3.5%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗