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DOE OSTI2016

Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning

In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts (0.2 < z < 1.1). Our method consists of two stages: feature extraction (obtaining the SN redshift from photometry and estimating light-curve shape parameters) and machine learning classification. We study the performance of different algorithms such as Random Forest and Boosted Decision Trees. We evaluate the performance using SN simulations and real data from the first 3 years of the Supernova Legacy Survey (SNLS), which contains large spectroscopically and photometrically classified type Ia samples. Using the Area Under the Curve (AUC) metric, where perfect classification is given by 1, we find that our best-performing classifier (Extreme Gradient Boosting Decision Tree) has an AUC of 0.98.We show that it is possible to obtain a large photometrically selected type Ia SN sample with an estimated contamination of less than 5%. When applied to data from the first three years of SNLS, we obtain 529 events. We investigate the differences between classifying simulated SNe, and real SN survey data. In particular, we find that applying a thorough set of selection cuts to the SN sample is essential for good classification. This work demonstrates for the first time the feasibility of machine learning classification in a high- z SN survey with application to real SN data.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Resistance Exercise and Inflammation in Breast Cancer Patients Undergoing Adjuvant Radiation Therapy: Mediation Analysis From a Randomized, Controlled Intervention Trial

Purpose: To explore the mediating role of inflammatory parameters in the development of fatigue, pain, and potentially related depressive symptoms during radiation therapy for breast cancer and its mitigation by resistance exercise. Methods and Materials: Breast cancer patients scheduled for adjuvant radiation therapy were randomized to 12-week progressive resistance exercise training (EX) or a relaxation control group. Interleukin-6 (IL-6) and interleukin-1 receptor antagonist (IL-1ra) were measured in serum samples collected before, at the end, and 6 weeks after radiation therapy from 103 chemotherapy-naïve participants. Fatigue was assessed with the multidimensional Fatigue Assessment Questionnaire, pain with the European Organization for Research and Treatment of Cancer QLQ-C30, and depressive symptoms with the Center for Epidemiologic Studies Depression Scale. Analysis of covariance models, partial correlations, Freedman-Schatzkin tests, and R{sup 2} effect-size measures for mediation were calculated. Results: The analysis of covariance models revealed a significant intervention effect on IL-6 (P=.010) and the IL-6/IL-1ra ratio (P=.018), characterized by a marked increase during radiation therapy among controls, but no significant change in EX. Interleukin-1 receptor antagonist did not change significantly in either group (P=.88). Increased IL-6 and IL-6/IL-1ra levels at the end of radiation therapy were significantly associated with increased physical fatigue and pain 6 weeks after radiation. We observed significant partial mediation by IL-6 and IL-6/IL-1ra of the effect of resistance exercise on physical fatigue (Freedman-Schatzkin P=.023 and P<.001) and pain (both P<.001). Hereby IL-6 and IL-6/IL-1ra mediated between 15% and 24% of the variance of physical fatigue and pain explained by the intervention. Conclusions: This randomized, controlled trial showed a significantly increased proinflammatory cytokine level after adjuvant radiation therapy in breast cancer patients. This effect was counteracted by progressive resistance exercise training. Interleukin-6 and the IL-6/IL-1ra ratio seemed to mediate the beneficial effect of exercise on physical fatigue and pain but only to a small extent.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2018

DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks

Hyperparameters employed by deep learning (DL) methods play a substantial role in the performance and reliability of these methods in practice. Unfortunately, finding performance-optimizing hyperparameter settings is a notoriously difficult task. Hyperparameter search methods typically have limited production-strength implementations or do not target scalability within a highly parallel machine, portability across different machines, experimental comparison between different methods, and tighter integration with workflow systems. In this paper, we present DeepHyper, a Python package that provides a common interface for the implementation and study of scalable hyperparameter search methods. It adopts the Balsam workflow system to hide the complexities of running large numbers of hyperparameter configurations in parallel on high-performance computing (HPC) systems. We implement and study asynchronous model-based search methods that consist of sampling a small number of input hyperparameter configurations and progressively fitting surrogate models over the input-output space until exhausting a user-defined budget of evaluations. We evaluate the efficacy of these methods relative to approaches such as random search, genetic algorithms, Bayesian optimization, and hyperband on DL benchmarks on CPU- and GPU-based HPC systems.

Balaprakash, Prasanna↗

DOE OSTI2021

Interpreting Write Performance of Supercomputer I/O Systems with Regression Models

This work seeks to advance the state of the art in HPC I/O performance analysis and interpretation. In particular, we demonstrate effective techniques to: (1) model output performance in the presence of I/O interference from production loads; (2) build features from write patterns and key parameters of the system architecture and configurations; (3) employ suitable machine learning algorithms to improve model accuracy. We train models with five popular regression algorithms and conduct experiments on two distinct production HPC platforms. We find that the lasso and random forest models predict output performance with high accuracy on both of the target systems. We also explore use of the models to guide adaptation in I/O middleware systems, and show potential for improvements of at least 15% from model-guided adaptation on 70% of samples, and improvements up to 10× on some samples for both of the target systems.

Xie, Bing↗

DOE OSTI2024

Magnetic tunnel junction random number generators applied to dynamically tuned probability trees driven by spin orbit torque

Abstract Perpendicular magnetic tunnel junction (pMTJ)-based true-random number generators (RNGs) can consume orders of magnitude less energy per bit than CMOS pseudo-RNGs. Here, we numerically investigate with a macrospin Landau–Lifshitz-Gilbert equation solver the use of pMTJs driven by spin–orbit torque to directly sample numbers from arbitrary probability distributions with the help of a tunable probability tree. The tree operates by dynamically biasing sequences of pMTJ relaxation events, called ‘coinflips’, via an additional applied spin-transfer-torque current. Specifically, using a single, ideal pMTJ device we successfully draw integer samples on the interval [0, 255] from an exponential distribution based on p -value distribution analysis. In order to investigate device-to-device variations, the thermal stability of the pMTJs are varied based on manufactured device data. It is found that while repeatedly using a varied device inhibits ability to recover the probability distribution, the device variations average out when considering the entire set of devices as a ‘bucket’ to agnostically draw random numbers from. Further, it is noted that the device variations most significantly impact the highest level of the probability tree, with diminishing errors at lower levels. The devices are then used to draw both uniformly and exponentially distributed numbers for the Monte Carlo computation of a problem from particle transport, showing excellent data fit with the analytical solution. Finally, the devices are benchmarked against CMOS and memristor RNGs, showing faster bit generation and significantly lower energy use.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2023

The hidden structure of hydrodynamic transport in random fracture networks

We study the large-scale dynamics and prediction of hydrodynamic transport in random fracture networks. The flow and transport behaviour is characterized by first passage times and displacement statistics, which show heavy tails and anomalous dispersion with a strong dependence on the injection condition. The origin of these behaviours is investigated in terms of Lagrangian velocities sampled equidistantly along particle trajectories, unlike classical sampling strategies at a constant rate. The velocity series are analysed by their copula density, the joint distribution of the velocity unit scores, which reveals a simple, albeit hidden, correlation structure that can be described by a Gaussian copula. Based on this insight, we derive a Langevin equation for the evolution of equidistant particle speeds. In this framework, particle motion is quantified by a stochastic time-domain random walk, the joint density of particle position, and speed satisfies a Klein–Kramers equation. The upscaled theory quantifies particle motion in terms of the characteristic fracture length scale and the distribution of Eulerian flow velocities. That is, it is predictive in the sense that it does not require the a priori knowledge of transport attributes. The upscaled model captures non-Fickian transport features, and their dependence on the injection conditions in terms of the velocity point statistics and average fracture length. It shows that the first passage times and displacement moments are dominated by extremes occurring at the first step. The presented approach integrates the interaction of flow and structure into a predictive model for large-scale transport in random fracture networks.

42 ENGINEERING↗

DOE OSTI2015

Monitoring, Modeling, and Diagnosis of Alkali-Silica Reaction in Small Concrete Samples

Assessment and management of aging concrete structures in nuclear power plants require a more systematic approach than simple reliance on existing code margins of safety. Structural health monitoring of concrete structures aims to understand the current health condition of a structure based on heterogeneous measurements to produce high-confidence actionable information regarding structural integrity that supports operational and maintenance decisions. This report describes alkali-silica reaction (ASR) degradation mechanisms and factors influencing the ASR. A fully coupled thermo-hydro-mechanical-chemical model developed by Saouma and Perotti by taking into consideration the effects of stress on the reaction kinetics and anisotropic volumetric expansion is presented in this report. This model is implemented in the GRIZZLY code based on the Multiphysics Object Oriented Simulation Environment. The implemented model in the GRIZZLY code is randomly used to initiate ASR in a 2D and 3D lattice to study the percolation aspects of concrete. The percolation aspects help determine the transport properties of the material and therefore the durability and service life of concrete. This report summarizes the effort to develop small-size concrete samples with embedded glass to mimic ASR. The concrete samples were treated in water and sodium hydroxide solution at elevated temperature to study how ingress of sodium ions and hydroxide ions at elevated temperature impacts concrete samples embedded with glass. Thermal camera was used to monitor the changes in the concrete sample and results are summarized.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

Controlled release of cortisone drugs from block copolymers synthetized by ATRP

Diseases affecting posterior eye segment, like macular edema, infection and neovascularization, may cause visual impairment. Traditional treatments, such as steroidal-drugs intravitreal injections, involve chronic course of therapy usually over a period of years. Moreover, they can require frequent administrations of drug in order to have an adequately disease control. This dramatically reduce patient’s compliance. Efforts have been made to develop implantable devices that offer an alternative therapeutic approach to bypass many challenges of conventional type of therapy. Implantable drug delivery systems (DDS) have been developed to optimize therapeutic properties of drugs and ensure their slow release in the specific site. Polymeric materials can play an essential role in modulating drug delivery and their use in such field has become indispensable. During last decades, acrylic polymers have obtained growing interest. Biocompatibility and chemical properties make them extremely versatile, allowing their use in many field such as biomedical. In particular, block methacrylate copolymer with a balance of hydrophilic and hydrophobic properties can be suitable for prolonged DDS in biomedical devices. In this work, we focused on the realization of a system for controlled and long term release of betamethasone 17,21-dipropionate (BDP), a cortisone drug, from methacrylic block copolymers, to be tested in the treatment of the posterior eye’s diseases. Different series of methyl methacrylate/hydroxyethyl methacrylate (MMA/HEMA) block and random copolymers, with different monomer compositions (10–60% HEMA), were synthetized by Atom Transfer Radical Polymerization (ATRP) to find the best hydrophilic/hydrophobic ratio, able to ensure optimal kinetic release. Copolymer samples were characterized by NMR spectroscopy ({sup 1}H-NMR, {sup 13}C-NMR, CosY), SEC, TGA and DSC. Monitoring of drug release from films loaded with BDP was carried out by HPLC analysis. Evaluation of different kinetic models allowed to deduce that release of BDP is controlled over time from PMMA-b-PHEMA 53/47. In particular, PMMA-b-PHEMA 53/47 showed the best release profile to achieve the therapeutic reference dose of 3 µg/die, employed in treatment of posterior eye disease, up to four months. Accordingly, PMMA-b-PHEMA 53/47 has been tested to prepare ocular inserts. Ocular inserts with different shape and the same area of polymer films have been obtained using silicon moulds made by a 3D printer.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2016

Ion irradiation-induced crystal structure changes in inverse spinel MgIn 2 O 4

We performed 400 keV Ne and 200 keV He ion irradiations on fully inverse MgIn 2 O 4 samples at cryogenic temperature (~ 77 K), in order to examine the influence of radiation-induced cation disordering on crystal structure. In the case of MgIn 2 O 4 samples irradiated with Ne ions to a peak displacement damage dose of 4 displacements per atom (dpa), a spinel-to-rocksalt phase transformation was observed. Conversely, for MgIn 2 O 4 samples irradiated with He ions to a peak displacement damage dose of 5 dpa, the only observed structural effect involved cation rearrangements from an inverse to a “random” spinel structure.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Hydrogen reverses the clustering tendency of carbon in amorphous silicon oxycarbide

Amorphous silicon oxycarbide (SiOC) is of great technological interest. However, its atomic-level structure is not well understood. Using density functional theory calculations, we show that the clustering tendency of C atoms in SiOC is extremely sensitive to hydrogen (H): without H, the C-C interaction is attractive, leading to enrichment of aggregated SiC 4 tetrahedral units; with hydrogen, the C-C interaction is repulsive, leading to enrichment of randomly distributed SiCO 3 tetrahedral units. Our results suggest that conflicting experimental characterizations of C distributions may be due to differing amounts of H present in the samples investigated. Our work also opens a path for tailoring the properties of SiOC by using the total H content to control the C distribution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

The z<1.2 optical luminosity function from a sample of ∼410,000 galaxies in BOÖtes

Using a sample of ∼410,000 galaxies to a depth of I{sub AB}=24 over 8.26 deg{sup 2} in the Boötes field (∼10 times larger than the z∼1 luminosity function (LF) studies in the prior literature), we have accurately measured the evolving B-band LF of red galaxies at z<1.2 and blue galaxies at z<1.0. In addition to the large sample size, we utilize photometry that accounts for the varying angular sizes of galaxies, photometric redshifts verified with spectroscopy, and absolute magnitudes that should have very small random and systematic errors. Our results are consistent with the migration of galaxies from the blue cloud to the red sequence as they cease to form stars and with downsizing in which more massive and luminous blue galaxies cease star formation earlier than fainter less massive ones. Comparing the observed fading of red galaxies with that expected from passive evolution alone, we find that the stellar mass contained within the red galaxy population has increased by a factor of ∼3.6 from z∼1.1 to z∼0.1. The bright end of the red galaxy LF fades with decreasing redshift, with the rate of fading increasing from ∼0.2 mag per unit redshift at z = 1.0 to ∼0.8 at z = 0.2. The overall decrease in luminosity implies that the stellar mass in individual highly luminous red galaxies increased by a factor of ∼2.2 from z = 1.1 to z = 0.1.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Observed Faraday Effects in Damped Lyα Absorbers and Lyman Limit Systems: The Magnetized Environment of Galactic Building Blocks at Redshift = 2

Protogalactic environments are typically identified using quasar absorption lines and can manifest as Damped Lyman-alpha Absorbers (DLAs) and Lyman Limit Systems (LLSs). We use radio observations of Faraday effects to test whether these galactic building blocks host a magnetized medium, by combining DLA and LLS detections with 1.4 GHz polarization data from the NRAO VLA Sky Survey (NVSS). We obtain a control, a DLA, and an LLS sample consisting of 114, 19, and 27 lines of sight, respectively. Using a Bayesian framework and weakly informative priors, we are unable to detect either coherent or random magnetic fields in DLAs: the regular coherent fields must be ⩽2.8 μG, and the lack of depolarization suggests the weakly magnetized gas in DLAs is non-turbulent and quiescent. However, we find a mild suggestive indication that LLSs have coherent magnetic fields, with a 71.5% probability that LLSs have higher |RM| than a control, although this is sensitive to the redshift distribution. We also find a strong indication that LLSs host random magnetic fields, with a 95.5% probability that LLS lines of sight have lower polarized fractions than a control. The regular coherent fields within the LLSs must be ⩽2.4 μG, and the magnetized gas must be highly turbulent with a typical turbulent length scale on the order of ≈5–20 pc. Our results are consistent with the standard dynamo paradigm, whereby magnetism in protogalaxies increases in coherence over cosmic time, and with a hierarchical galaxy formation scenario, with the DLAs and LLSs exploring different stages of magnetic field evolution in galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1988

Cosmological parameters and evolution of the galaxy luminosity function

The relationship between the observed distribution of discrete sources of a flux limited sample, the luminosity function of these sources, and the cosmological model is discussed. It is stressed that some assumptions about the form and evolution of the luminosity function must be made in order to determine the cosmological parameters from the observed distribution of sources. Presented is a method to test the validity of these assumptions using the observations. It is shown how, using higher moments of the observed distribution, one can determine, independently of the cosmological model, all parameters of the luminosity function except those describing evolution of the density and the luminosity of the luminosity function. These methods are applied to the sample of approximately 1000 galaxies recently used by Loh and Spillar to determine a value of the cosmological density parameter Omega approx = 1. It is shown that the assumptions made by Loh and Spillar about the luminosity function are inconsistent with the data, and that a self-consistent treatment of the data indicates a lower value of Omega approx = 0.2 and a flatter luminosity function. It should be noted, however, that incompleteness in the sample could cause a flattening of the luminosity function and lower the calculated value of Omega and that uncertainty in the values of these parameters due to random fluctuations is large.

Caditz, David↗

NASA NTRS1989

Cosmological parameters and evolution of the galaxy luminosity function

Caditz, David↗

NASA NTRS1995

Microwave Resonator Measurements of Atmospheric Absorption Coefficients: A Preliminary Design Study

A preliminary design study examined the feasibility of using microwave resonator measurements to improve the accuracy of atmospheric absorption coefficients and refractivity between 18 and 35 GHz. Increased accuracies would improve the capability of water vapor radiometers to correct for radio signal delays caused by Earth's atmosphere. Calibration of delays incurred by radio signals traversing the atmosphere has applications to both deep space tracking and planetary radio science experiments. Currently, the Cassini gravity wave search requires 0.8-1.0% absorption coefficient accuracy. This study examined current atmospheric absorption models and estimated that current model accuracy ranges from 5% to 7%. The refractivity of water vapor is known to 1% accuracy, while the refractivity of many dry gases (oxygen, nitrogen, etc.) are known to better than 0.1%. Improvements to the current generation of models will require that both the functional form and absolute absorption of the water vapor spectrum be calibrated and validated. Several laboratory techniques for measuring atmospheric absorption and refractivity were investigated, including absorption cells, single and multimode rectangular cavity resonators, and Fabry-Perot resonators. Semi-confocal Fabry-Perot resonators were shown to provide the most cost-effective and accurate method of measuring atmospheric gas refractivity. The need for accurate environmental measurement and control was also addressed. A preliminary design for the environmental control and measurement system was developed to aid in identifying significant design issues. The analysis indicated that overall measurement accuracy will be limited by measurement errors and imprecise control of the gas sample's thermodynamic state, thermal expansion and vibration- induced deformation of the resonator structure, and electronic measurement error. The central problem is to identify systematic errors because random errors can be reduced by averaging. Calibrating the resonator measurements by checking the refractivity of dry gases which are known to better than 0.1% provides a method of controlling the systematic errors to 0.1%. The primary source of error in absorptivity and refractivity measurements is thus the ability to measure the concentration of water vapor in the resonator path. Over the whole thermodynamic range of interest the accuracy of water vapor measurement is 1.5%. However, over the range responsible for most of the radio delay (i.e. conditions in the bottom two kilometers of the atmosphere) the accuracy of water vapor measurements ranges from 0.5% to 1.0%. Therefore the precision of the resonator measurements could be held to 0.3% and the overall absolute accuracy of resonator-based absorption and refractivity measurements will range from 0.6% to 1.

Walter, Steven J.↗

NASA NTRS2017

Derivation of Aerosol Profiles for MC3E Convection Studies and Use in Simulations of the 20 May Squall Line Case

Advancing understanding of deep convection microphysics via mesoscale modeling studies of well-observed case studies requires observation-based aerosol inputs. Here, we derive hygroscopic aerosol size distribution input profiles from ground-based and airborne measurements for six convection case studies observed during the Midlatitude Continental Convective Cloud Experiment (MC3E) over Oklahoma. We demonstrate use of an input profile in simulations of the only well-observed case study that produced extensive stratiform outflow on 20 May 2011. At well-sampled elevations between −11 and −23 C over widespread stratiform rain, ice crystal number concentrations are consistently dominated by a single mode near approx. 400 microm in randomly oriented maximum dimension (Dmax). The ice mass at −23 C is primarily in a closely collocated mode, whereas a mass mode near Dmax approx. 1000 microns becomes dominant with decreasing elevation to the −11 C level, consistent with possible aggregation during sedimentation. However, simulations with and without observation-based aerosol inputs systematically overpredict mass peak Dmax by a factor of 3-5 and underpredict ice number concentration by a factor of 4-10. Previously reported simulations with both two-moment and size-resolved microphysics have shown biases of a similar nature. The observed ice properties are notably similar to those reported from recent tropical measurements. Based on several lines of evidence, we speculate that updraft microphysical pathways determining outflow properties in the 20 May case are similar to a tropical regime, likely associated with warm-temperature ice multiplication that is not well understood or well represented in models.

Fridlind, Ann M.↗

NASA NTRS2017

Derivation of Aerosol Profiles for MC3E Convection Studies and Use in Simulations of the 20 May Squall Line Case

Advancing understanding of deep convection microphysics via mesoscale modeling studies of well-observed case studies requires observation-based aerosol inputs. Here, we derive hygroscopic aerosol size distribution input profiles from ground-based and airborne measurements for six convection case studies observed during the Midlatitude Continental Convective Cloud Experiment (MC3E) over Oklahoma. We demonstrate use of an input profile in simulations of the only well-observed case study that produced extensive stratiform outflow on 20 May 2011. At well-sampled elevations between -11 and -23 degree C over widespread stratiform rain, ice crystal number concentrations are consistently dominated by a single mode near 400 micrometer in randomly oriented maximum dimension (D[superscript max] ). The ice mass at -23 degree C is primarily in a closely collocated mode, whereas a mass mode near D[superscript max] -1000 micrometer becomes dominant with decreasing elevation to the -11 degree C level, consistent with possible aggregation during sedimentation. However, simulations with and without observation-based aerosol inputs systematically overpredict mass peak D[superscript max] by a factor of 3-5 and underpredict ice number concentration by a factor of 4-10. Previously reported simulations with both two-moment and size-resolved microphysics have shown biases of a similar nature. The observed ice properties are notably similar to those reported from recent tropical measurements. Based on several lines of evidence, we speculate that updraft microphysical pathways determining outflow properties in the 20 May case are similar to a tropical regime, likely associated with warm-temperature ice multiplication that is not well understood or well represented in models.

Fridlind, Ann M.↗

NASA NTRSDate not supplied

NASA’s Ground-Based Microgravity Simulation Facility

Since opportunities to conduct experiments in space are scarce, various microgravity simulators and analogs have been widely used in space biology ground studies. Even though microgravity simulators do not produce all of the biological effects observed in the true microgravity environment, they provide alternative test platforms that are effective, affordable, and readily available to facilitate microgravity research. The Microgravity Simulation Support Facility (MSSF) at the National Aeronautics and Space Administration (NASA) John F. Kennedy Space Center (KSC) has been established for conducting short duration experiments, typically less than 1 month, utilizing a variety of microgravity simulation devices for research at different gravity levels. The simulators include, but are not limited to, 2D Clinostats, 3D Clinostats, Random Positioning Machines, and Rotating Wall Vessels. In this presentation, we will provide an overview of current MSSF capabilities, research conducted in MSSF, new technology developments, customized sample holder designs, and the physical characteristics of these microgravity simulators.

Ye Zhang↗