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DOE OSTI2021

Novel usage of deep learning and high-performance computing in long-baseline neutrino oscillation experiments

Mención Internacional en el título de doctorDeep-learning methods are playing a crucial role in numerous scientific and industrialapplications. Over the past two decades, these techniques have helped in the collection,reconstruction, and analysis of large data samples in particle physics experiments. Themain topic of this PhD research is the study of deep-learning techniques in long-baselineneutrino oscillation experiments. Neutrinos are mysterious light elementary particles,and their investigation is essential to shed light on some of the remaining open questionsin physics. The work presented here describes an algorithm based on a convolutionalneural network developed to provide highly accurate and efficient selections of electronneutrino and muon neutrino interactions in the Deep Underground Neutrino Experiment(DUNE). With this algorithm, the electron neutrino (antineutrino) selection efficiencypeaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between2-5 GeV. The selection efficiency for muon neutrino (antineutrino) interactions is foundto have a maximum of 96% (97%) and exceeds 90% (95%) efficiency for reconstructedneutrino energies above 2 GeV. When considering all electron neutrino and antineutrinointeractions as signal (both those appearing from oscillations and those intrinsic tothe beam), a selection purity of 90% is achieved. These event selections are criticalto maximise the sensitivity of the experiment to CP-violating effects, key to furtherunderstand the matter-antimatter asymmetry of the Universe.In high-energy physics experiments, deep learning has also been explored for producingfast simulations and physically-motivated manipulations of simulated images. Some ofthose simulations, such as the light production and detection, are very computationallyexpensive and require novel methods to produce the necessary samples while controllingthe varied underlying physics model parameters. To do so, we invented the model-assistedgenerative adversarial network (MAGAN), first validated on simple generic case studiesand then successfully applied to the DUNE photon-detector simulation.Moreover, we also developed graph neural networks for 3D-voxel classification ofambiguities and optical crosstalk for a different particle physics experiment, most preciselyfor the proposed SuperFGD. This novel 3D-granular plastic-scintillator neutrino detectorwill be used to upgrade the near detector of the T2K neutrino oscillation experiment, and our method reports efficiencies and purities of 94-96% per event in the classificationof particle track voxels.Due to the growth and complexity of deep neural networks, researchers have beeninvestigating techniques to train those networks in a more computationally-efficient way.Many efforts have been made by the community to optimise deep-learning models byparallelising or distributing their training computation across multiple devices. In thisthesis, we study an approach based on data locality for those neural networks that cannotbenefit from scaling their computation due to a significant bottleneck in the data I/O.The research also includes a detailed study on the performance of deep neural networkson hardware accelerator boards.Los métodos de aprendizaje profundo son cada vez más utilizados en numerosas aplicacionescientíficas e industriales hoy en día. Durante las dos últimas décadas, estastécnicas se han empleado en la recolección, reconstrucción y análisis de la gran cantidadde datos generados por experimentos de física de partículas. El tema principal de estatesis doctoral es el uso de estos modelos de aprendizaje profundo en experimentos defísica de neutrinos, en concreto en los experimentos de larga distancia DUNE y T2K. Losneutrinos, partículas fundamentales neutras, de las más ligeras del Universo, pueden serclave para explicar algunas de las cuestiones todavía sin resolver en física fundamental.Entre las diferentes contribuciones que esta tesis ha hecho a su estudio, cabe destacar eldesarrollo de un algoritmo basado en una red de neuronas convolucional para seleccionarcon gran eficiencia y precisión las interacciones de neutrinos electrónicos y muónicos enel Deep Underground Neutrino Experiment (DUNE). La eficiencia de selección obtenidapara neutrinos (antineutrinos) electrónicos alcanza un máximo del 90% (94%) y supera el85% (90%) para neutrinos con energías reconstruidas en el rango 2-5 GeV. La selección deneutrinos (antineutrinos) muónicos tiene una eficiencia máxima del 96% (97%) y excedeel 90% (95%) para neutrinos con energías reconstruidas de más de 2 GeV. Considerandocomo señal todas las interacciones de neutrinos y antineutrinos electrónicos (procedentestanto de oscilaciones como intrínsecos en el haz inicial), se logra una pureza en la seleccióndel 90%. Dichas selecciones de eventos son fundamentales para maximizar la sensibilidaddel experimento a los efectos de violació...

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Flash Drought indicators at catchment scale for CONUS

Flash droughts are defined by the rapid onset and intensification of drought conditions - a feature common across various proposed indicators. However, the absence of a standardized definition and detection method makes them particularly difficult to anticipate and manage. This dataset includes flash drought classifications based on six different methods across 222 catchments (4-digit Hydrologic Units, HUC4) in the Contiguous United States (CONUS) from 1983 to 2023. It also includes the pairwise agreement between indicators and the event-level multi-indicator agreement. For a detailed description of each file, refer to README_MSDlive.pdf

Catchment scale↗

NASA NTRS1993

Signal detection theory and methods for evaluating human performance in decision tasks

Signal Detection Theory (SDT) can be used to assess decision making performance in tasks that are not commonly thought of as perceptual. SDT takes into account both the sensitivity and biases in responding when explaining the detection of external events. In the standard SDT tasks, stimuli are selected in order to reveal the sensory capabilities of the observer. SDT can also be used to describe performance when decisions must be made as to the classification of easily and reliably sensed stimuli. Numbers are stimuli that are minimally affected by sensory processing and can belong to meaningful categories that overlap. Multiple studies have shown that the task of categorizing numbers from overlapping normal distributions produces performance predictable by SDT. These findings are particularly interesting in view of the similarity between the task of the categorizing numbers and that of determining the status of a mechanical system based on numerical values that represent sensor readings. Examples of the use of SDT to evaluate performance in decision tasks are reviewed. The methods and assumptions of SDT are shown to be effective in the measurement, evaluation, and prediction of human performance in such tasks.

Obrien, Kevin↗

NASA NTRS2007

A Longitudinal Analysis of the Causal Factors in Major Maritime Accidents in the USA and Canada (1996-2006)

Accident reports provide important insights into the causes and contributory factors leading to particular adverse events. In contrast, this paper provides an analysis that extends across the findings presented over ten years investigations into maritime accidents by both the US National Transportation Safety Board (NTSB) and Canadian Transportation Safety Board (TSB). The purpose of the study was to assess the comparative frequency of a range of causal factors in the reporting of adverse events. In order to communicate our findings, we introduce J-H graphs as a means of representing the proportion of causes and contributory factors associated with human error, equipment failure and other high level classifications in longitudinal studies of accident reports. Our results suggest the proportion of causal and contributory factors attributable to direct human error may be very much smaller than has been suggested elsewhere in the human factors literature. In contrast, more attention should be paid to wider systemic issues, including the managerial and regulatory context of maritime operations.

Johnson, C. W.↗

DOE OSTI2017

Studying fish near ocean energy devices using underwater video

The effects of energy devices on fish populations are not well-understood, and studying the interactions of fish with tidal and instream turbines is challenging. To address this problem, we have evaluated algorithms to automatically detect fish in underwater video and propose a semi-automated method for ocean and river energy device ecological monitoring. The key contributions of this work are the demonstration of a background subtraction algorithm (ViBE) that detected 87% of human-identified fish events and is suitable for use in a real-time system to reduce data volume, and the demonstration of a statistical model to classify detections as fish or not fish that achieved a correct classification rate of 85% overall and 92% for detections larger than 5 pixels. Specific recommendations for underwater video acquisition to better facilitate automated processing are given. The recommendations will help energy developers put effective monitoring systems in place, and could lead to a standard approach that simplifies the monitoring effort and advances the scientific understanding of the ecological impacts of ocean and river energy devices.

Matzner, Shari↗

DOE OSTI2019

Programmed death-1 receptor (PD-1) and PD-ligand-1 (PD-L1) expression in non-small cell lung cancer and the immune-suppressive effect of anaerobic glycolysis

The microenvironment of a tumor may regulate the anti-tumor immune response. Intratumoral acidosis and hypoxia may suppress lymphocyte proliferation and migration, and this may have important implications in modern immunotherapy. The expression of PD-L1 by cancer cells and of PD-1 by tumor infiltrating lymphocytes (TILs) was assessed in tissue specimens from 98 operable NSCLC patients. Their prognostic role and their association with makers of glycolysis and anaerobic metabolism were assessed. Strong cytoplasmic/membrane PD-L1 expression was noted in 45/98 cases. Intense presence of TILs was noted in 42/98 cases (high TIL-score), and intense presence of PD-1 expressing TILs (high PIL-score) in 17/98 cases. PD-L1 expression was directly correlated with high PIL-score (p = 0.005). A significant inverse relationship was found between lactate dehydrogenase LDH5 expression and PIL-score (p = 0.008). Similarly, low PIL-score was significantly linked with high-hexokinase HXKII and monocarboxylate transporter MCT2 expression (p < 0.04). Cases with both intense TIL-score and PIL-score had significantly better survival (p < 0.05). For patients with high TIL-score or high PIL-score, PD-L1 overexpression defined significantly poorer survival (p = 0.01 and p = 0.03, respectively). In multivariate analysis, stage (p = 0.002, HR 3.33, 95%CI 1.4–4.5) and TIL-score (p = 0.02, HR 2.12, 95%CI 1.1–4.0) were independent predictive variables of death events. Given the low specificity of PD-L1 as a biomarker for anti-PD-1/PD-L1 immunotherapy, a combined assessment of TIL, PD-L1, PD-1, and LDH5 provides a tool for an immunological/metabolic classification of NSCLC tumors, with a different prognosis and different expected response to anti-PD-1/PD-L1 immunotherapy, which should be considered in relevant clinical trials.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2022

Lightweight jet reconstruction and identification as an object detection task

We apply object detection techniques based on deep convolutional blocks to end-to-end jet identification and reconstruction tasks encountered at the CERN large hadron collider (LHC). Collision events produced at the LHC and represented as an image composed of calorimeter and tracker cells are given as an input to a Single Shot Detection network. The algorithm, named PFJet-SSD performs simultaneous localization, classification and regression tasks to cluster jets and reconstruct their features. This all-in-one single feed-forward pass gives advantages in terms of execution time and an improved accuracy w.r.t. traditional rule-based methods. A further gain is obtained from network slimming, homogeneous quantization, and optimized runtime for meeting memory and latency constraints of a typical real-time processing environment. We experiment with 8-bit and ternary quantization, benchmarking their accuracy and inference latency against a single-precision floating-point. We show that the ternary network closely matches the performance of its full-precision equivalent and outperforms the state-of-the-art rule-based algorithm. Finally, we report the inference latency on different hardware platforms and discuss future applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

A systematic review of validated suicide outcome classification in observational studies

Abstract Background Suicidal outcomes, including ideation, attempt, and completed suicide, are an important drug safety issue, though few epidemiological studies address the accuracy of suicidal outcome ascertainment. Our primary objective was to evaluate validated methods for suicidal outcome classification in electronic health care database studies. Methods We performed a systematic review of PubMed and EMBASE to identify studies that validated methods for suicidal outcome classification published 1 January 1990 to 15 March 2016. Abstracts and full texts were screened by two reviewers using prespecified criteria. Sensitivity, specificity, and predictive value for suicidal outcomes were extracted by two reviewers. Methods followed PRISMA-P guidelines, PROSPERO Protocol: 2016: CRD42016042794. Results We identified 2202 citations, of which 34 validated the accuracy of measuring suicidal outcomes using International Classification of Diseases (ICD) codes or algorithms, chart review or vital records. ICD E-codes (E950-9) for suicide attempt had 2–19% sensitivity, and 83–100% positive predictive value (PPV). ICD algorithms that included events with ‘uncertain’ intent had 4–70% PPV. The three best-performing algorithms had 74–92% PPV, with improved sensitivity compared with E-codes. Read code algorithms had 14–68% sensitivity and 0–56% PPV. Studies estimated 19–80% sensitivity for chart review, and 41–97% sensitivity and 100% PPV for vital records. Conclusions Pharmacoepidemiological studies measuring suicidal outcomes often use methodologies with poor sensitivity or predictive value or both, which may result in underestimation of associations between drugs and suicidal behaviour. Studies should validate outcomes or use a previously validated algorithm with high PPV and acceptable sensitivity in an appropriate population and data source.

Swain, Richard S.↗

DOE OSTI2014

A multi-wavelength investigation of the radio-loud supernova PTF11qcj and its circumstellar environment

We present the discovery, classification, and extensive panchromatic (from radio to X-ray) follow-up observations of PTF11qcj, a supernova (SN) discovered by the Palomar Transient Factory (PTF). Our observations with the Karl G. Jansky Very Large Array show that this event is radio-loud: PTF11qcj reached a radio peak luminosity comparable to that of the famous gamma-ray-burst-associated SN 1998bw (L {sub 5} {sub GHz} ≈ 10{sup 29} erg s{sup –1} Hz{sup –1}). PTF11qcj is also detected in X-rays with the Chandra Observatory, and in the infrared band with Spitzer. Our multi-wavelength analysis probes the SN interaction with circumstellar material. The radio observations suggest a progenitor mass-loss rate of ∼10{sup –4} M {sub ☉} yr{sup –1} × (v{sub w} /1000 km s{sup –1}), and a velocity of ≈0.3-0.5 c for the fastest moving ejecta (at ≈10 days after explosion). However, these estimates are derived assuming the simplest model of SN ejecta interacting with a smooth circumstellar wind, and do not account for possible inhomogeneities in the medium and asphericity of the explosion. The radio data show deviations from such a simple model, as well as a late-time re-brightening. The X-ray flux from PTF11qcj is compatible with the high-frequency extrapolation of the radio synchrotron emission (within the large uncertainties). A light echo from pre-existing dust is in agreement with our infrared data. Our pre-explosion data from the PTF suggest that a precursor eruption of absolute magnitude M{sub r} ≈ –13 mag may have occurred ≈2.5 yr prior to the SN explosion. Overall, PTF11qcj fits the expectations from the explosion of a Wolf-Rayet star. Precursor eruptions may be a feature characterizing the final pre-explosion evolution of such stars.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Approach to fitting parameters and clustering for characterising measured voltage dips based on two-dimensional polarisation ellipses

An alternative approach to characterise real voltage dips is proposed and evaluated in this study. The proposed methodology is based on voltage-space vector solutions, identifying parameters for ellipses trajectories by using the least-squares algorithm applied on a sliding window along the disturbance. The most likely patterns are then estimated through a clustering process based on the k-means algorithm. The objective is to offer an efficient and easily implemented alternative to characterise faults and visualise the most likely instantaneous phase-voltage evolution during events through their corresponding voltage-space vector trajectories. This novel solution minimises the data to be stored but maintains extensive information about the dips including starting and ending transients. The proposed methodology has been applied satisfactorily to real voltage dips obtained from intensive field-measurement campaigns carried out in a Spanish wind power plant up to a time period of several years. A comparison to traditional minimum root mean square-voltage and time-duration classifications is also included in this study.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

An application of survival analysis to disruption prediction via Random Forests</strong

We report one of the most pressing challenges facing the fusion community is adequately mitigating or, even better, avoiding disruptions of tokamak plasmas. However, before this can be done, disruptions must first be predicted with sufficient warning time to actuate a response. The established field of survival analysis provides a convenient statistical framework for time-to-event (i.e. time-to-disruption) studies. This paper demonstrates the integration of an existing disruption prediction machine learning algorithm with the Kaplan–Meier estimator of survival probability. Specifically discussed are the implied warning times from binary classification of disruption databases and the interpretation of output signals from Random Forest algorithms trained and tested on these databases. This survival analysis approach is applied to both smooth and noisy test data to highlight important features of the survival and hazard functions. In addition, this method is applied to three Alcator C-Mod plasma discharges and compared to a threshold-based scheme for triggering alarms. In one case, both techniques successfully predict the disruption; although, in another, neither warns of the impending disruption with enough time to mitigate. For the final discharge, the survival analysis approach could avoid the false alarm triggered by the threshold method. Limitations of this analysis and opportunities for future work are also presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1983

Crosscutting relations and relative ages of ridges and faults in the Tharsis region of Mars

The results of a detailed photogeologic analysis and classification of ridge-fault cross-cutting relationships on ridged plains units of the Tharsis region is reported. The problems involved in using the morphology of ridge-fault intersections to determine relative timing of structural events are discussed. The implications of using intersection angles as well as morphology to determine possible age relationships for the tectonic history of the region are presented. It is concluded that the major ridge-forming events in the Tharsis region were roughly coincident with, and possibly prior to, the extensional events that produced the faulting of the Tempe and Mareotis regions, the Coprates and Memnonia regions, and the rifting of the Valles Marineris. The compressional events that formed most of the ridges are restricted in time both by their relationship to regional extensional events and by the age of the units on which they formed.

Watters, T. R.↗

NASA NTRS1989

Impulsiveness and energetics in solar flares with and without type II radio bursts - A comparison of hard X-ray characteristics for over 2500 solar flares

The hard X-ray characteristics of more than 2500 solar flares are used to study the relative size, impulsiveness, and energetics of flares with and without type II radio bursts. A quantitative definition of the hard X-ray impulsiveness is introduced, which may be applied to a large number of events unambiguously. It is found that the flares with type II bursts are generally not significantly larger, more impulsive, or more energetic than those without type II bursts. Also, no evidence is found to suggest a simple classification of the flares as either 'impulsive' or 'gradual'. Because type II bursts are present even in small flares with relatively unimpulsive energy releases, it is concluded that changes in the ambient conditions of the solar atmosphere causing an unusually low Alfven speed may be important in the generation of the shock wave that produces type II radio bursts.

Pearson, Douglas H.↗

NASA NTRS1993

Arctic coastal polynya observations with ERS-1 SAR and DMSP SSM/I

Work to improve the characterization of the distribution of new and young sea ice types and open water amount within Arctic coastal polynyas through the combined use of ERS-1 SAR (Synthetic Aperture Radar) and DMSP SSM/I (Defense Meteorological Satellite Program Special Sensor Microwave/Imager) data is described. Two St. Lawrence Island polynya events are studied using low resolution, geocoded SAR images and coincident SSM/I data. The SAR images are analyzed in terms of polarization and spectral gradient ratios. Results of the combined analysis show that the SAR ice type classification is consistent with that from SSM/I and that the combined use of SAR and SSM/I can improve the characterization of thin ice better than either data set can do alone.

Cavalieri, D. J.↗

NASA NTRS1988

Anorthosites: Classification, mythology, trivia, and a simple unified theory

An overview was presented of anorthosites. They were classified into six types: (1) Archean megacrystic, (2) Proterozoic massif-type, (3) stratiform, (4) oceanic, (5) inclusions, and (6) extraterrestrial. Some of the anorthosite mythology was discussed, such as the existence of a distinct, catastrophic anorthosite event in the late Proterozoic, the misconception that anorthosite is a major constituent of the lower continental crust, and the misconception that Archean anorthosites represent metamorphosed equivalents of mafic layered intrusions such as Bushveld or Stillwater. A general statement was offered about the origin of all anorthosites: They are cumulates of plagioclase from mantle-derived basaltic magmas.

Ashwal, Lewis D.↗

NASA NTRS1999

Gamma-Ray Burst Class Properties

Guided by the Supervised pattern recognition algorithm C4.5, we examine the three gamma-ray burst classes identified by Mukherjee et al. C4.5 provides strong statistical support for this classification. However, with C4.5 and our knowledge of the BATSE instrument, we demonstrate that Class 3 (intermediate fluence, intermediate duration, soft) does not have to be a distinct source population: statistical/systematic errors in measuring burst attributes combined with the well-known hardness/intensity correlation can cause low peak flux Class I (high fluence, long, intermediate hardness) bursts to take on Class 3 characteristics naturally. Based on our hypothesis that the third class is not a distinct one, we provide rules so that future events can be placed in either Class I or Class 2 (low fluence, short, hard). Using classified bursts from the BATSE 4B Catalog, we plot log(N>P) vs. log(P) curves and study spectral features of each class. We find that the two classes are relatively distinct on the basis of spectral parameters, alpha, Beta, and E(sub peak) alone. Although this does not indicate a better basis for classification, it does suggest that different physical conditions exist for Class I and Class 2 bursts. In the process of studying burst class characteristics, we identify a new bias that affects measurement of burst fluences and durations. Using a simple model of how burst duration can be underestimated, we generally characterize how this fluence duration bias affects BATSE measurements, and demonstrate the type of effect it can have on the BATSE fluence vs. peak flux diagram.

Hakkila, Jon↗

DOE OSTI2021

Climatology of Linear Mesoscale Convective System Morphology in the United States based on Random Forests Method

This study uses machine learning methods, specifically the random forest (RF), on a radar-based mesoscale convective system (MCS) tracking dataset to classify the five types of linear MCS morphology in the contiguous United States during the period 2004-2016. The algorithm is trained using radar- and satellite-derived spatial and morphological parameters, and reanalysis environmental information from 5-26yr manually identified nonlinear and five linear MCS modes. The algorithm is then used to automate the classification of linear MCSs over 8 years with high accuracy, providing a systematic, long-term climatology of linear MCSs. Results reveal that nearly 40% of MCSs are classified as linear MCSs, in which half of the linear events belong to the type of system having a leading convective line. The occurrence of linear MCSs shows large annual and seasonal variations. On average, 113 linear MCSs occur annually during the warm season (through March to October), with most of these events clustered from May through August in the central eastern Great Plains. MCS characteristics, including duration, propagation speed, orientation, and system cloud size, have large variability among the different linear modes. The systems having a trailing convective line and the systems having a back-building area of convection typically move more slowly and have higher precipitation rate, and thus have higher potential in producing extreme rainfall and flash flooding. Analysis of the environmental conditions associated with linear MCSs show that the storm-relative flow is of most importance in determining the organization mode of linear MCSs.

Cui, Wenjun↗

DOE OSTI2019

An application of survival analysis to disruption prediction via Random Forests

One of the most pressing challenges facing the fusion community is adequately mitigating or, even better, avoiding disruptions of tokamak plasmas. However, before this can be done, disruptions must first be predicted with sufficient warning time to actuate a response. The established field of survival analysis provides a convenient statistical framework for time-to-event (i.e. time-to-disruption) studies. This paper demonstrates the integration of an existing disruption prediction machine learning algorithm with the Kaplan-Meier estimator of survival probability. Specifically discussed are the implied warning times from binary classification of disruption databases and the interpretation of output signals from Random Forest algorithms trained and tested on these databases. This survival analysis approach is applied to both smooth and noisy test data to highlight important features of the survival and hazard functions. In addition, this method is applied to three Alcator C-Mod plasma discharges and compared to a threshold-based scheme for triggering alarms. In one case, both techniques successfully predict the disruption; although, in another, neither warns of the impending disruption with enough time to mitigate. For the final discharge, the survival analysis approach could avoid the false alarm triggered by the threshold method. Limitations of this analysis and opportunities for future work are also presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗