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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Poisson hurdle model-based method for clustering microbiome features

Abstract Motivation High-throughput sequencing technologies have greatly facilitated microbiome research and have generated a large volume of microbiome data with the potential to answer key questions regarding microbiome assembly, structure and function. Cluster analysis aims to group features that behave similarly across treatments, and such grouping helps to highlight the functional relationships among features and may provide biological insights into microbiome networks. However, clustering microbiome data are challenging due to the sparsity and high dimensionality. Results We propose a model-based clustering method based on Poisson hurdle models for sparse microbiome count data. We describe an expectation–maximization algorithm and a modified version using simulated annealing to conduct the cluster analysis. Moreover, we provide algorithms for initialization and choosing the number of clusters. Simulation results demonstrate that our proposed methods provide better clustering results than alternative methods under a variety of settings. We also apply the proposed method to a sorghum rhizosphere microbiome dataset that results in interesting biological findings. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PHclust. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1977

Study of the Brazil and Falkland currents using their images of Nimbus 5 and oceanographic data in 1972 - 1973

The Western Edge of the Sub-tropical Convergence of the South-western Atlantic Ocean, called the Front, which is a thermal discontinuity between the Brazil and Falkland Currents, was studied utilizing the Temperature Humidity Infrared Radiometer (THIR) of Nimbus V in the 10.5 to 12.5 micrometers channel and historical oceanographic data. Some important results obtained are: the oceanographic Front could be detected from Nimbus THIR data; oceanographic charts showed that the transition zone where the Brazil and the Falkland Currents meet was the Front detected from satellite data; ocean current speeds calculated with THIR data were of the same order of magnitude as those calculated oceanographically; fisheries statistics for Pargo Roseo showed that the maximum catches were in September of 1973, in the period when the Front was observed most distinctly and clearly. The results showed the great potentiality of satellite data to study surface thermal structures, surface currents and oceanic fisheries.

Tseng, Y. C.↗
NASA NTRS1992

Comparison of drift potential derived from Mars GCM with rock abundance from IRTM

A General Circulation Model (GCM) for Mars developed at the Ames Research Center makes global predictions of wind shear stress as a function of season and dust optical depth. The surface shear stress, pressure and temperature from the GCM are used here to calculate the drift potential, or the amount of sediment that could be moved by the wind. A map of sand roses based on the calculations was generated for comparison with predictions of the distribution of rocks on Mars from Christensen, which is derived from infrared thematic mapper (IRTM) data. The drift potential was defined for Earth as the amount of sand moved at a given place for a stated period of time, and was adapted here for Mars.

Xu, Pengyang↗
DOE OSTI2019

U.S. Efforts in Support of Examinations at Fukushima Daiichi: 2019 Evaluations

Much is still not known about the end-state of core materials in each unit that was operating on March 11, 2011 at the Fukushima Daiichi Nuclear Power Station (Daiichi). Information obtained from Daiichi is required to inform Decontamination and Decommissioning (D&D) activities, improving the ability of the Tokyo Electric Power Company Holdings, Incorporated (TEPCO Holdings) to characterize potential hazards and to ensure the safety of workers involved with cleanup activities. This document, which has been updated to include Fiscal Year 2019 (FY2019) information, summarizes results from a U.S. effort to review Daiichi information and extract insights to enhance the safety of existing and future nuclear power plant designs. This U.S. effort, which was initiated in 2014 by the Department of Energy Office of Nuclear Energy (DOE-NE), is completed by a group of experts in reactor safety and plant operations that identify examination needs and evaluate recent Daiichi examination data to address these needs. Each year, annual reports are issued that document significant safety insights being obtained in areas of special emphasis. For FY2019, the areas emphasized by the expert panel include component performance, radionuclide surveys and sampling, debris end-state location, combustible gas effects, and plant operations and maintenance. In addition to reducing uncertainties related to severe accident modeling progression, these insights have and continue to be used to update guidance for severe accident prevention, mitigation, and emergency planning. Reduced uncertainties in modeling the events at Daiichi improve the realism of reactor safety evaluations that inform future D&D activities. In addition, the process established by this U.S. effort provides an example that Japan may wish to implement in future international efforts to support D&D. As emphasized in this FY2019 report, important insights are being gained from examinations of the affected units at Daiichi. Although many results from prior years have been moved to appendices, key areas of interest are highlighted in the main document. For example, larger intact components and fuel containing materials observed on the floor of the primary containment and retained on ex-vessel structures continue to be emphasized because of insights that may be gained with respect to vessel failure and ex-vessel interactions with structures and concrete. In addition, the main report presents insights gained by focused evaluations of plant data because of potential use of systems and components during severe accident conditions and alternate perspectives regarding accident progression that may be inferred from such data. The report also illustrates how systems analysis codes can be applied to reduce uncertainties about the progression of events at Daiichi and how practical insights obtained from Daiichi can be used to enhance radiation safety for the existing fleet. Recent and planned updates to systems analysis codes are also discussed. As in prior years, this report contains an appendix with a list of examination needs that was updated after U.S. experts reviewed recently obtained information from Daiichi. Additional details for higher priority, near-term, examination activities are also provided. This report also includes an appendix with a description of a website that was developed to assist U.S. experts by providing information in an archived retrievable location.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Critical Infrastructure Classification via CNN-based Modeling and Image Analysis

With recent advances in the fields of satellite imagery and machine learning we now have the ability to develop explainable deep learning models that enhance critical infrastructure analysis. Funded through Idaho National Laboratory’s (INL) Laboratory Directed Research and Development (LDRD) office we are in the process of developing a deep learning model capable of identifying critical infrastructure facilities and embedded features within those facilities. Utilizing current limit of practice techniques in the machine learning areas of explainability and transfer learning our model, once complete, will have the capacity to be used on multiple different imagery data sets and produce results that not only classify critical infrastructure facilities, but also explain why a critical infrastructure facility was classified as a certain type of facility. These advancements eliminate the ‘black box’ approach deep learning models have had in the past, where a user will have to trust the conclusion of a model without understanding what reasoning when into the model’s classification process. They also expand a model’s usefulness, traditionally a deep learning model will have to use the same data set it was originally trained on. Given the long training times of deep learning models this is impractical in a number of scenarios. By utilizing transfer learning advancements, we are eliminating the need to train our model on the same data set it is then run on to classify critical infrastructure facilities. We are also enabling the analysis and classification of data sets that are potentially too small to be divided into a training and testing data set. Once completed this model can provide a foundation to enhanced critical infrastructure analysis, dependency analysis, and potential disaster relief efforts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Uncovering New Physics in the Cosmic Microwave Background: Developing Novel Theoretical Models and Machine-Learning-Powered Constraints

The search for evidence of new physics via its signatures in the cosmos is a cornerstone goal of the Office of High Energy Physics in the DOE Office of Science. Indeed, the current concordance cosmological model provides intriguing hints for beyond-the-standard-model (BSM) physics, such as dark matter and dark energy. Recently, a potential breakdown has appeared in this model, which could be initial evidence toward a further important revision in our fundamental theoretical understanding of cosmology. This breakdown is reflected in disagreements between inferences of the current expansion rate of the universe, H 0 (the Hubble constant), based on indirect, cosmological data (e.g., from the early universe) and based on direct, local measurements. Despite significant effort, a compelling new concordance cosmological model has yet to be found; achieving significant progress on this front was the first major focus of the project. Theoretical considerations indicate that if the observational discrepancies are not due to systematic errors, they strongly suggest new physics operating in the redshift range just prior to recombination, when cosmic microwave background (CMB) photons last scattered. Crucially, almost all such models produce unique signatures in the CMB temperature and polarization power spectra, which will be measured with unprecedented precision by ongoing and upcoming experiments, including the DOE-supported CMB-S4 project. However, these subtle hints of new physics must be uncovered from beneath a swath of Galactic and extragalactic foreground contamination. Current CMB analysis methods, although powerful, do not optimally infer the CMB power spectrum in the presence of non-Gaussian foregrounds. There is thus scope for theoretical improvement in this foundational challenge of cosmological inference, which formed the second major focus of the project. The primary objectives of the project were two-fold: (1) to develop new theoretical models in cosmology that can restore concordance amongst the full suite of cosmological data sets, thereby potentially providing evidence of novel BSM physics; (2) to develop new theoretical machinery to enable significant sensitivity improvements in searches for new physics in cosmology, particularly via the CMB power spectrum. The two objectives are intertwined, as the analysis methodology improvements in (2) will enable the tightest possible constraints on the signatures of new physics predicted by the novel scenarios in (1). The theoretical approaches to restore concordance focused on models involving novel scalar field dynamics in the pre-recombination universe (the “early dark energy” scenario and modifications thereof), as well as couplings between this field and other components in the standard cosmological model, such as dark matter. We also studied a model featuring a generalization of the decaying dark matter scenario, in which a sub-component of dark matter converts into dark radiation at late times in cosmic history. While these ideas are mostly driven by phenomenological considerations, this tactic has proven extremely successful in cosmology throughout the past few decades, including in the early history of evidence for dark matter and dark energy. The new theoretical machinery envisioned in (2) is undergirded by developments in signal processing and machine learning, which will enable improvements in CMB power spectrum estimation in the presence of non-Gaussian foreground contaminants. In turn, this will yield optimal sensitivity in searches for new physics in the CMB, by maximizing the cosmological information that is extracted from this observable. The most ambitious outcome of this work would be the construction of a new cosmological model that restores concordance amongst data sets. Although the individual models studied here did not fully achieve that goal, significant progress in narrowing down the model space was made, as described below. Moreover, the outcome of the methodological improvements in (2) will significantly impact a wide range of theoretical cosmology, by enabling the tightest possible constraints on any model that leaves novel signatures in the CMB temperature and polarization power spectra.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1978

Emerging markets for satellite data communications in the public service

The paper discusses some of the current and potential markets for satellite data communications as projected by the Public Service Satellite Consortium (PSSC). Organizations in the public service sector are divided into three categories, depending on their expected benefits and organizational changes due to increased satellite telecommunications use: A - modest institutional adjustments are necessary and significant productivity gains are likely; B - institutional requirements picture is promising, but more information is needed to assess benefits and risk; and C - major institutional adjustments are needed, risks are high but possible benefits are high. These criteria are applied to the U.S. health care system, continuing education, equipment maintenance, libraries, environmental monitoring, and other potential markets. The potential revenues are seen to be significant, but what is needed is a cooperative effort by common carriers and major public service institutions to aggregate the market.

Potter, J. G.↗
DOE OSTI2022

The impact of detection rate changes and correlations on random-coincidence background measurements

Coincidence detection of multiple particles emitted during an experiment can yield a new depth of understanding of the underlying process under study. However, the probability of detecting particles that are generated from the same physical event within a given coincidence time window is generally much lower than that of detecting particles that appear in the same coincidence time window, but were not created from the same physical event, and are therefore detected randomly in coincidence with each other. Thus, accurate and precise methods of measuring this random-coincidence background are essential for a wide variety of fields of science. A method to determine this background directly using the data themselves without any additional experimental run time or fake signals introduced in the data was recently established (O’Donnell, 2016). This method yields a statistical uncertainty on the random-coincidence background that is orders of magnitude smaller than that of the true coincidence data, though the potential for systematic errors of backgrounds from this method was never explored. In this work, we discuss common varieties of correlated and uncorrelated changes in the detection rates of each particle detected in an experiment. Here we demonstrate here that a correlation between particle detection rates from, for example, an incident particle beam that initiates a physical process of interest, creates systematic errors in the random-coincidence background measurement. We also discuss the impact of a variety of other realistic scenarios for rate changes in experiments. Lastly, a method is introduced to correct for errors in the random-coincidence background from any source, yielding an optimization between statistical precision and eliminating potential lingering systematic errors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

FrESCO: Framework for Exploring Scalable Computational Oncology

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information from electronic health records. This data is then used to create summary statistics about cancer incidence and mortality to facilitate population health monitoring. Extracting phenotypic information from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating the information extraction process from cancer pathology reports has the potential to improve data quality by extracting information in a consistent manner across registries. It can also improve patient outcomes by reducing the time from diagnosis, enabling rapid case ascertainment for clinical trials. Here we present FrESCO, a modular deep-learning natural language processing (NLP) library initially designed for extracting pathology information from clinical text documents. This repository is not solely limited to clinical medical text, but may also be used by researchers just getting started with NLP methods and those looking for a robust solution for their classification problems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Deep Learning Based Approach to Integrate MyShake's Trigger Data with ShakeAlert for Faster and Robust EEW Alerts

Earthquake Early Warning (EEW) systems depend on the dense seismic network to make fast and accurate earthquake detections to issue useful early alerts. The recent development and implementation of the ShakeAlert system is relying on the west coast seismic network to detect and estimate the earthquake parameters in real-time. While working on making improvements on the current system, one potential approach is to include more seismic recordings from various sources, such as the low-cost smartphone seismic network, such as the MyShake network, or the Google Android smartphones, since these smartphone seismic networks have much more portable stations (phones) to potentially provide useful data to the system. This report summarizes the initial exploration of using a deep learning approach to combine the data from both traditional seismic stations and the smartphone data in California. Due to the heterogeneity nature of the data, we aggregate data from the phones (using simulation data), as well as that from traditional seismic stations to grid cells. By generating real-time triggering ratio grid cell maps, the designed deep learning algorithm can process the data from multiple sources and detect the earthquake faster than only using that from a traditional seismic network.

58 GEOSCIENCES↗
NASA NTRS1976

Computer program for design and performance analysis of navigation-aid power systems

The paper examines the requirements, design rationale, operation, and verification of a computer program designated as design synthesis/performance analysis (DSPA) computer program, which is capable of performing all the calculations necessary to understand the overall characteristics of solar array/battery power systems for navigation-aid applications. Despite the uncertainties in the erratic solar array degradation data and the potential impact on actual battery behavior, verification of the DSPA is considered successful. The program is shown to have the capability of simulating the performance of solar array/battery navigation-aid power systems. It can also be used to synthesize power system designs and provide essential design and cost data.

Weiner, H.↗
NASA NTRS2023

Deriving New Mixing Ratios for Venus Atmospheric Gases Using Data From the Pioneer Venus Large Probe Neutral Mass Spectrometer

We present the first published method to convert data obtained by the Pioneer Venus Large Probe Neutral Mass Spectrometer (LNMS) into units of mixing ratio (ppm) and volume percent (v%) against CO 2 and N 2 , the dominant Venus atmospheric gases, including conversion to density (kg m −3 ). These unit conversions are key to unlocking the untapped potential of the data, which represents a significant challenge given the scant calibration data in the literature. Herein, we show that our data treatments and conversions yield mixing ratios and volume percent values for H 2 O, N 2 , and SO 2 that are within error to those reported for the gas chromatograph (LGC) on the Pioneer Venus Large Probe (PVLP). For the noble gases, we developed strategies to correct for instrument biases by treating the data as a relative scale and using PVLP and Venera-based measurements as calibration points. Together, these methods, conversions, calibrations, and comparisons afford novel unit conversions for the LNMS data and yield unified measures for Venus’ atmosphere from the LNMS and LGC on the PVLP. - Conversion into mixing ratio (ppm), volume percent (v%), and density (kg m −3 ). - Mixing ratios are expressed against CO 2 and N 2 . - LNMS and LGC measurements on the PVLP are consistent.

Venus↗
DOE OSTI2015

Immobilized Low-Activity Waste Glass Release Data Package for the Integrated Disposal Facility Performance Assessment

The Hanford Site in southeastern Washington State has been used extensively by the U.S. Department of Energy (DOE) to produce nuclear materials for the U.S. defense arsenal. A large inventory of radioactive and mixed waste has accumulated in 177 buried single- and double-shell tanks. Liquid waste recovered from the tanks will be pretreated to separate the low-activity fraction from the high-level and transuranic wastes. The low-activity waste (LAW) is planned to be immobilized in glass, via vitrification, and placed into a near-surface disposal system on the Hanford Site. Before the immobilized low-activity waste (ILAW) can be placed into the disposal system, DOE must approve a performance assessment (PA), which documents the assumptions and provides the quantitative demonstration of compliance with the performance objectives for the long-term protection of the public and the environment. A critical component of the PA will be to demonstrate that releases from the selected glass waste form do not result in the performance objectives being exceeded. The PA must also, for purposes of establishing limits on radionuclides that may be buried near-surface, assess impacts to a hypothetical intruder and impacts to water resources. The key information presented in this data package consists of the specific glass compositions and associated glass dissolution rate law parameters for five ILAW glasses. In addition, the results of geochemical modeling performed on several glasses that are not specifically described in this document are provided as supplemental data to help identify potential end-point alteration phases that may form during the weathering of ILAW glasses. These data (glass compositions, rate law parameters, and potential alteration phases) will be used to identify contaminant release source terms for the Integrated Disposal Facility PA. In addition, a general discussion on nuclear waste glass corrosion and a brief overview of modeling approaches and corresponding uncertainties are discussed. Table ES.1 through Table ES.4 provide the specific data to be used to support the 2017 PA.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2016

A Database of Interplanetary and Interstellar Dust Detected by the Wind Spacecraft

It was recently discovered that the WAVES instrument on the Wind spacecraft has been detecting, in situ, interplanetary and interstellar dust of approximately 1 micron radius for the past 22 years. These data have the potential to enable advances in the study of cosmic dust and dust-plasma coupling within the heliosphere due to several unique properties: the Wind dust database spans two full solar cycles; it contains over 107,000 dust detections; it contains information about dust grain direction of motion; it contains data exclusively from the space environment within 350 Earth radii of Earth; and it overlaps by 12 years with the Ulysses dust database. Further, changes to the WAVES antenna response and the plasma environment traversed by Wind over the lifetime of the Wind mission create an opportunity for these data to inform investigations of the physics governing the coupling of dust impacts on spacecraft surfaces to electric field antennas. A Wind dust database has been created to make the Wind dust data easily accessible to the heliophysics community and other researchers. This work describes the motivation, methodology, contents, and accessibility of the Wind dust database.

interplaneary dust↗
DOE OSTI2017

Mechanisms of interfacial reactivity in near surface and extreme environments

The local water structure surrounding ions in aqueous solutions greatly affects their chemical properties such as reaction rates, ion association, and proton and electron transport. These properties result in the behavior of ions in natural aqueous environments. For example ore transport is facilitated by chloride ion pair formation and the reaction of ions in an interface is strongly dependent on the dehydration of the ion hydration shell. We are developing the use of high-­resolution XAFS observations and 1st principles based MD-­XAFS analysis (spectra simulated using 1st principle methods with no adjustable parameters, AIMD) to interpret the solution properties of strongly interacting aqueous solutes under arbitrary pressure and temperature conditions. In the 1st principle MD-­XAFS method density functional theory (DFT) based MD simulations(Car and Parrinello, 1985) are used to generate a large ensemble of structural snap shots of the hydration region. These are then used to generate scattering intensities. I emphasize three points about this novel approach to analyzing XAFS data. 1st: As illustrated in Figure 1, the level of agreement between the calculated and observed intensities is considerably higher than has been obtained by any XAFS analysis to date (note 2nd shell region, R> 2 Å). 2nd: This result was obtained from a parameter free simulation with no fitting of the interaction potentials to any data. This supports the use of these methods for more difficult environments and more complex solutes (polyions). 3rd: New information about the shell structure (Figure 1) is now available because of this more detailed agreement. We note also that both multiple scattering and second shell features are well represented in the analysis. As far as we know this is the 1st analysis of second shell structure and multiple scattering. Excellent agreement has been obtained for most of the third row metal ions: Ca 2+ , Zn 2+ , Cu 2+ , Ni 2+ , Co 2+ , Mn 2+ , Fe 3+ , Cr 3+ . Calculations on these systems are demanding because of their open electronic shells, and high ionic charge. Principal Investigator: Professor John Weare (University of California, San Diego) The prediction of the interactions of geochemical fluids with minerals, nanoparticles, and colloids under extreme near surface conditions of temperature (T) and pressure (P) is a grand challenge research need in geosciences (U.S. DOE 2007, Basic Research Needs for Geosciences: Facilitating the 21st Energy Systems.). To evaluate the impact of these processes on energy production and management strategies it is necessary to have a high level of understanding of the interaction between complex natural fluids and mineral formations. This program emphasizes 1st principle parameter free simulations of complex chemical processes in solutions, in the mineral phase, and in the interfaces between these phases The development of new computational tools (with emphasis on oxide materials and reaction dynamics) tailored to treat wide range of conditions and time scales experienced in such geochemical applications is has been developed. Because of the sensitivity of the interaction in these systems to electronic structure and local bonding environments, and of the need to describe bond breaking/formation, our simulations are based on interactions calculated at the electronic structure level (ab-initio molecular dynamics, AIMD). The progress in the computational aspects of program may be summarized in terms of the following themes (objectives); Development of efficient parameter free dynamical simulation technology based on 1st principles force and energy calculations especially adapted for geochemical applications (e.g., mineral, interfaces and aqueous solutions) (continuing program); Calculation of the dynamics of water structure of in the surface-water interface of transition metal oxides and oxihydroxides; and Development of improved (beyond DFT+GGA) electronic structure calculations for minerals and the interface region that more accurately calculate electron correlation, spin density, and localization. The focus of the program is also on the iron oxide and oxihydroxide minerals and Fe 2+ (aq)/Fe 3+ (cr) oxidation in the mineral solution interface region. These methods included the development of model Hamilitonian methods that can be solved to near convergence for single site models (DMFT) and many-body perturbation methods (MP2, GW); Development of time decomposition methods to extend time scales of molecular dynamics (MD) simulations and support the use of high complexity electronic structure calculations (MP2, CCSD(T)) of forces for use in dynamical simulations where very high chemical accuracy is required (microsolvated reactions in absorbed surface layers); and The development of a new linear scaling finite element solver for eigenvalue problem that supports solution of quantum problems with unusual potential and boundary values. Application progress of the above new simulation technology to problems of geochemical interests includes; The prediction of metal oxide surface structure and the reduction/oxidation of Fe 3+ (cr)/Fe 2+ (aq) in metal oxide (hematite, goethite)/solution interfaces. Result: water interacts strongly with the 001 Hematite surface; interaction of water with the 100 goethite is weak; The study of ion solvation and the composition of ion hydration shells under extreme conditions (focus on Fe 3+/2+ , Al 3+ and Mg 2+ and their hydroxide speciation). Result: Ion association in water solutions can be calculated from 1st principle methods. Efficient sampling of the free energy requires more development; The continued development of new high resolution analysis of XAFS scattering of disordered systems (particularly Al, Mg) and of XANES calculations for aqueous ions. Result: EXAFS spectra can be calculated to high accuracy with DFT level dynamic simulations; The exploration of electron localization and electron transport in metal oxides (highly correlated materials). Result: Proper description of electron localization requires levels of calculation beyond DFT; and Localization of electrons in DFT type Hamiltonians was studied. Result: For very Dirac high exchange new solutions (New unphysical bifrucations) to the eigenvalue problem are found. The program was highly collaborative involving faculty and students in mathematics, physics and computer science departments as well as coworkers at the Pacific Northwest National Laboratories (PNNL). The students in this program had the opportunity to develop skills in the development of methods, the implementation of method on high performance parallel computers and the application of these methods to problem in geochemical science. Much of the software that was developed was incorporated in the NWchem software package maintained by PNNL.

58 GEOSCIENCES↗
DOE OSTI2017

Quantum geometry of resurgent perturbative/nonperturbative relations

For a wide variety of quantum potentials, including the textbook ‘instanton’ examples of the periodic cosine and symmetric double-well potentials, the perturbative data coming from fluctuations about the vacuum saddle encodes all non-perturbative data in all higher non-perturbative sectors. Here we unify these examples in geometric terms, arguing that the all-orders quantum action determines the all-orders quantum dual action for quantum spectral problems associated with a classical genus one elliptic curve. Furthermore, for a special class of genus one potentials this relation is particularly simple: this class includes the cubic oscillator, symmetric double-well, symmetric degenerate triple-well, and periodic cosine potential. These are related to the Chebyshev potentials, which are in turn related to certain N = 2 supersymmetric quantum field theories, to mirror maps for hypersurfaces in projective spaces, and also to topological c = 3 Landau-Ginzburg models and ‘special geometry’. These systems inherit a natural modular structure corresponding to Ramanujan’s theory of elliptic functions in alternative bases, which is especially important for the quantization. Insights from supersymmetric quantum field theory suggest similar structures for more complicated potentials, corresponding to higher genus. Lastly, our approach is very elementary, using basic classical geometry combined with all-orders WKB.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Constraints on neutrino oscillation parameters with the NOvA experiment

NOvA is a long-baseline neutrino oscillation experiment, which consists of two finely-segmented liquid-scintillator detectors operating 14 mrad off-axis from the NuMI muon neutrino beam. With an 810 km baseline, the combined measurements of muon neutrino disappearance and electron neutrino appearance allow the determination of neutrino oscillation unknowns, namely the mass hierarchy, the octant of the largest neutrino mixing angle, and the CP violating phase. In this dissertation I present the joint analysis of $\nu_\mu \to \nu_\mu$ and $\nu_\mu \to \nu_e$ oscillation data with an exposure of $8.85\times10^{20}$ protons on target on the 14 kton detector. It includes the estimation of neutrino energy distributions in the far detector using near detector data constraints, details of the implementation of the simultaneous fit of \numu and \nue samples, and the effect of systematic uncertainties on the measurement of oscillation parameters. I also discuss NOvA's projected sensiti vity to determine the mass hierarchy and discover CP violation in future analyses with increased exposure and the addition of antineutrino datasets. With 66 electron neutrino and 126 muon neutrino candidates, the best fit to the data corresponds to the normal mass hierarchy, $\Delta m^2_{32}=2.44\times 10^{-3} eV^2/c^4$, $\sin^2 \theta_{23}=0.56$, and $\delta_{CP} = 1.21\pi$, with the allowed regions $\Delta m^2_{32} \in [2.37,2.52]\times 10^{-3} eV^2/c^4$, $\sin^2 \theta_{23} \in [0.43,0.51] \cup [0.52,0.60]$ and $\delta_{CP} \in [0,0.12\pi] \cup [0.91\pi,2\pi]$ at the 68.3\% C.L. Our data disfavor maximal mixing by 0.8$\sigma$, $\delta_{CP} = \pi/2$ in the inverted hierarchy at higher than 3$\sigma$, and the entire inverted mass hierarchy at the 95\% confidence level. % By increasing the size of the samples and adding anti-neutrino data, \nova can potentially resolve the neutrino mass hierarchy at 4$\sigma$ C.L. or higher for some combinations of oscillation parameters. Im provements to the sensitivity to the determination of the ma! ss hierarchy and CP violation in the neutrino sector can be achieved through gains in the NuMI beam intensity, the inclusion of external data, and refinements in the analysis methodology.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗