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At least 1,243 records · Page 69

Managing information technology security risk

Information Technology (IT) Security Risk Management is a critical task for the organization to protect against the loss of confidentiality, integrity and availability of IT resources. As systems bgecome more complex and diverse and and attacks from intrusions and malicious content increase, it is becoming increasingly difficult to manage IT security risk. This paper describes a two-pronged approach in addressing IT security risk and risk management in the organization: 1) an institutional enterprise appraoch, and 2) a project life cycle approach.

security toolset↗

Developing an Approach for Analyzing and Verifying System Communication

This slide presentation reviews a project for developing an approach for analyzing and verifying the inter system communications. The motivation for the study was that software systems in the aerospace domain are inherently complex, and operate under tight constraints for resources, so that systems of systems must communicate with each other to fulfill the tasks. The systems of systems requires reliable communications. The technical approach was to develop a system, DynSAVE, that detects communication problems among the systems. The project enhanced the proven Software Architecture Visualization and Evaluation (SAVE) tool to create Dynamic SAVE (DynSAVE). The approach monitors and records low level network traffic, converting low level traffic into meaningful messages, and displays the messages in a way the issues can be detected.

Stratton, William C.↗

Speech Acquisition and Automatic Speech Recognition for Integrated Spacesuit Audio Systems

A voice-command human-machine interface system has been developed for spacesuit extravehicular activity (EVA) missions. A multichannel acoustic signal processing method has been created for distant speech acquisition in noisy and reverberant environments. This technology reduces noise by exploiting differences in the statistical nature of signal (i.e., speech) and noise that exists in the spatial and temporal domains. As a result, the automatic speech recognition (ASR) accuracy can be improved to the level at which crewmembers would find the speech interface useful. The developed speech human/machine interface will enable both crewmember usability and operational efficiency. It can enjoy a fast rate of data/text entry, small overall size, and can be lightweight. In addition, this design will free the hands and eyes of a suited crewmember. The system components and steps include beam forming/multi-channel noise reduction, single-channel noise reduction, speech feature extraction, feature transformation and normalization, feature compression, model adaption, ASR HMM (Hidden Markov Model) training, and ASR decoding. A state-of-the-art phoneme recognizer can obtain an accuracy rate of 65 percent when the training and testing data are free of noise. When it is used in spacesuits, the rate drops to about 33 percent. With the developed microphone array speech-processing technologies, the performance is improved and the phoneme recognition accuracy rate rises to 44 percent. The recognizer can be further improved by combining the microphone array and HMM model adaptation techniques and using speech samples collected from inside spacesuits. In addition, arithmetic complexity models for the major HMMbased ASR components were developed. They can help real-time ASR system designers select proper tasks when in the face of constraints in computational resources.

Huang, Yiteng↗

Integrated Demand Management: The FCA Balancing Algorithm

The Collaborative Trajectory Options Program (CTOP) is a new NextGen function that resides within the FAA’s Traffic Flow Management System (TFMS) software. CTOP was designed to be used in the United States’ national airspace to control the distribution of air traffic demand, both temporally and geographically, through a set of Flow Constrained Areas (FCAs). Each FCA is assigned capacity values that represent an estimated upper limit of manageable demand, and CTOP controls to that capacity by using Expect Departure Clearance Time (EDCT) air traffic control clearances, and/or rerouting flights to an alternate FCA or out of the CTOP completely. CTOP’s decisions about which control options best meet system and user needs are based on the Trajectory Options Set (TOS) submitted by airline operators for each flight. The FCA Balancing Algorithm (FBA) described in this document is a proposed decision support capability that generates CTOP capacity entries for FCAs controlling traffic to a common downstream constraint. The FBA provides an equitable distribution of impact, and addresses the added complexity of assigning FCA capacity values for this specific type of use case, greatly simplifying the FAA air traffic manager’s task.

IDM↗

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗

Modeling Deformable Linear Objects for Autonomous Robotic Outfitting of Lunar Surface Systems

The construction of in-space facilities is a significant capability for the establishment of a long-term human presence in space. Autonomous robotic technologies are a critical tool to enabling the construction and maintenance of such permanent facilities. This paper evaluates the outfitting portion of the construction process, focusing on modeling required for robotic manipulation of cable-like objects, referred to as Deformable Linear Objects (DLOs). DLOs contain a high degree of variability, which makes accurate real-time autonomous operations a difficult task. Different modeling methods for DLOs such as discrete mass-spring systems and Cosserat elastic rod models range in problem complexity and accuracy, a trade-off that must be understood to fully realize autonomous cable routing technologies. This paper validates different DLO models through simulation and a hardware experiment, highlighting the size of the state space and accuracy of different approaches. The development of DLO manipulation models for outfitting enables an autonomous architecture for the construction of lunar surface systems.

Amy M Quartaro↗

Modeling Deformable Linear Objects for Autonomous Robotic Outfitting of Lunar Surface Systems

The construction of in-space facilities is a significant capability for the establishment of a long-term human presence in space. Autonomous robotic technologies are a critical tool to enabling the construction and maintenance of such permanent facilities. This paper evaluates the outfitting portion of the construction process, focusing on modeling required for robotic manipulation of cable-like objects, referred to as Deformable Linear Objects (DLOs). DLOs contain a high degree of variability, which makes accurate real-time autonomous operations a difficult task. Different modeling methods for DLOs such as discrete mass-spring systems and Cosserat elastic rod models range in problem complexity and accuracy, a trade-off that must be understood to fully realize autonomous cable routing technologies. This paper validates different DLO models through simulation and a hardware experiment, highlighting the size of the state space and accuracy of different approaches. The development of DLO manipulation models for outfitting enables an autonomous architecture for the construction of lunar surface systems.

Deformable Modeling↗

Mathematical description of complex chemical kinetics and application to CFD modeling codes

A major effort in combustion research at the present time is devoted to the theoretical modeling of practical combustion systems. These include turbojet and ramjet air-breathing engines as well as ground-based gas-turbine power generating systems. The ability to use computational modeling extensively in designing these products not only saves time and money, but also helps designers meet the quite rigorous environmental standards that have been imposed on all combustion devices. The goal is to combine the very complex solution of the Navier-Stokes flow equations with realistic turbulence and heat-release models into a single computer code. Such a computational fluid-dynamic (CFD) code simulates the coupling of fluid mechanics with the chemistry of combustion to describe the practical devices. This paper will focus on the task of developing a simplified chemical model which can predict realistic heat-release rates as well as species composition profiles, and is also computationally rapid. We first discuss the mathematical techniques used to describe a complex, multistep fuel oxidation chemical reaction and develop a detailed mechanism for the process. We then show how this mechanism may be reduced and simplified to give an approximate model which adequately predicts heat release rates and a limited number of species composition profiles, but is computationally much faster than the original one. Only such a model can be incorporated into a CFD code without adding significantly to long computation times. Finally, we present some of the recent advances in the development of these simplified chemical mechanisms.

Bittker, D. A.↗

Mathematical Description of Complex Chemical Kinetics and Application to CFD Modeling Codes

A major effort in combustion research at the present time is devoted to the theoretical modeling of practical combustion systems. These include turbojet and ramjet air-breathing engines as well as ground-based gas-turbine power generating systems. The ability to use computational modeling extensively in designing these products not only saves time and money, but also helps designers meet the quite rigorous environmental standards that have been imposed on all combustion devices. The goal is to combine the very complex solution of the Navier-Stokes flow equations with realistic turbulence and heat-release models into a single computer code. Such a computational fluid-dynamic (CFD) code simulates the coupling of fluid mechanics with the chemistry of combustion to describe the practical devices. This paper will focus on the task of developing a simplified chemical model which can predict realistic heat-release rates as well as species composition profiles, and is also computationally rapid. We first discuss the mathematical techniques used to describe a complex, multistep fuel oxidation chemical reaction and develop a detailed mechanism for the process. We then show how this mechanism may be reduced and simplified to give an approximate model which adequately predicts heat release rates and a limited number of species composition profiles, but is computationally much faster than the original one. Only such a model can be incorporated into a CFD code without adding significantly to long computation times. Finally, we present some of the recent advances in the development of these simplified chemical mechanisms.

Bittker, D. A.↗

A Predictive GNSS Performance Monitor for Autonomous Air Vehicles in Urban Environments

The emergence and development of advanced technologies and vehicle types has created a growing demand for the introduction of new forms of flight operations. These new and increasingly complex operational paradigms such as Advanced and Urban Air Mobility (AAM/UAM) present regulatory authorities and the aviation community with several design and implementation challenges – particularly for highly autonomous vehicles. An overarching and daunting task is finding methods to integrate these emerging operations without compromising safety or disrupting traditional airspace operations. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. This paper focus on the development and testing of a prognostic service aimed at estimating the quality of Global Navigation Satellite System (GNSS) performance for an autonomous aircraft in complex environments. The intent of this function is to proactively reduce a flight operations risk of exposure to states that may induce poor or unacceptable navigation system performance by factoring in estimates of GNSS quality into pre-flight and/or in-flight route planning. Methodologies for producing quality estimates are specified and results are provided for selected simulation and flight test cases.

GNSS↗

Denoising diffusion probabilistic models for generative alloy design

Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.

36 MATERIALS SCIENCE↗

VLBI Data Interchange Format (VDIF)

One important outcome of the 7th International e-VLBI Workshop in Shanghai in June 2008 was the creation of a task force to study and recommend a universal VLBI data format that is suitable for both on-the-wire e-VLBI data transfer, as well as direct disk storage. This task force, called the VLBI Data Interchange Format (VDIF) Task Force, is the first part of a two-part effort, the second of which will address standardization of e-VLBI data-transmission-protocols. The formation of the VDIF Task Force was prompted particularly by increased e-VLBI activity and the difficulties encountered when data arrive at a correlator in different formats from various instruments in various parts of the world. The task force created a streaming packetized data format that may be used for real-time and non-realtime e-VLBI, as well as direct disk storage. The data may contain multiple channels of time-sampled data with an arbitrary number of channels, arbitrary #bits/sample up to 32, and real or complex data; data rates in excess of 100 Gbps are supported. Each data packet is completely self-identifying via a short header, and data may be decoded without reference to any external information. The VDIF task force has completed its work, and the VDIF standard was ratified at the 2009 e-VLBI workshop in Madrid.

Whitney, Alan↗

Dimensions of complexity in learning from interactive instruction

Robot systems deployed in space must exhibit flexibility. In particular, an intelligent robotic agent should not have to be reprogrammed for each of the various tasks it may face during the course of its lifetime. However, pre-programming knowledge for all of the possible tasks that may be needed is extremely difficult. Therefore, a powerful notion is that of an instructible agent, one which is able to receive task-level instructions and advice from a human advisor. An agent must do more than simply memorize the instructions it is given (this would amount to programming). Rather, after mapping instructions into task constructs that it can reason with, it must determine each instruction's proper scope of applicability. In this paper, we will examine the characteristics of instruction, and the characteristics of agents, that affect learning from instruction. We find that in addition to a myriad of linguistic concerns, both the situatedness of the instructions (their placement within the ongoing execution of tasks) and the prior domain knowledge of the agent have an impact on what can be learned.

Huffman, Scott B.↗

High-Performance Data Analysis Tools for Sun-Earth Connection Missions

The data analysis tool of choice for many Sun-Earth Connection missions is the Interactive Data Language (IDL) by ITT VIS. The increasing amount of data produced by these missions and the increasing complexity of image processing algorithms requires access to higher computing power. Parallel computing is a cost-effective way to increase the speed of computation, but algorithms oftentimes have to be modified to take advantage of parallel systems. Enhancing IDL to work on clusters gives scientists access to increased performance in a familiar programming environment. The goal of this project was to enable IDL applications to benefit from both computing clusters as well as graphics processing units (GPUs) for accelerating data analysis tasks. The tool suite developed in this project enables scientists now to solve demanding data analysis problems in IDL that previously required specialized software, and it allows them to be solved orders of magnitude faster than on conventional PCs. The tool suite consists of three components: (1) TaskDL, a software tool that simplifies the creation and management of task farms, collections of tasks that can be processed independently and require only small amounts of data communication; (2) mpiDL, a tool that allows IDL developers to use the Message Passing Interface (MPI) inside IDL for problems that require large amounts of data to be exchanged among multiple processors; and (3) GPULib, a tool that simplifies the use of GPUs as mathematical coprocessors from within IDL. mpiDL is unique in its support for the full MPI standard and its support of a broad range of MPI implementations. GPULib is unique in enabling users to take advantage of an inexpensive piece of hardware, possibly already installed in their computer, and achieve orders of magnitude faster execution time for numerically complex algorithms. TaskDL enables the simple setup and management of task farms on compute clusters. The products developed in this project have the potential to interact, so one can build a cluster of PCs, each equipped with a GPU, and use mpiDL to communicate between the nodes and GPULib to accelerate the computations on each node.

Messmer, Peter↗

Diagnostics: Chapter 8 of the special issue: on the path to tokamak burning plasma operation

This chapter presents the activity conducted by the ITPA topical group (TG) on Diagnostics over about the last 15 years. Following a general introduction of the ITER Diagnostics led by their measurement roles, the document is organized in several subchapters detailing the design support, research and development activity conducted by each of the specialist working groups (WGs) of the TG. Please note that the magnetic diagnostics were supported at the TG without a specific WG. Their status is included in the general introduction. In the following some highlights of the subchapter’s contents are provided. Recent advances in ITER first wall (FW) diagnostics for the measurements of plasma-metallic wall interaction in support of the ITER research plan are reported. An InfraRed imaging Video Bolometer for ITER has been developed and tested on several tokamaks to measure the radiated power loss. A laser-induced breakdown spectroscopy (LIBS) technique which utilizes a pulsed laser beam to ablate locally by forming a crater, will measure local tritium inventory in the FW material. Real-time Residual Gas Analyzers will measure the neutral gas composition in a divertor port and an equatorial port during plasma operation. Due to the full metallic FW environment, the plasma-wall interaction in ITER will face several challenges such as the compromised radiated power and divertor heat flux measurements by reflection. Ray tracing and analysis codes have been developed to eliminate and correct the effects of reflection in the measurements. The characteristics of the reflecting surfaces depending on the roughness and angle of the incidence have been measured by dedicated experiments, and the results were applied to the reflection elimination. For the measurement of the metallic impurity radiation induced by eroded metallic atoms, a vacuum ultraviolet spectrometer has been developed and tested. An extensive thermonuclear diagnostic suite will be required to support the operation of ITER and the planned experimental program for future burning plasma experiments. Due to the harsh environmental conditions, the implementation of diagnostic systems in ITER is a major challenge. These conditions include high levels of neutron and gamma fluxes, neutron heating, particle bombardment. Therefore, the selection and design of diagnostic systems must take into account a number of phenomena previously unseen in diagnostic design. For this reason, the measurement of neutrons and confined or lost fast ions, with particular emphasis on alpha particles, is critical to ITER. The diagnostics associated with these measurements will be important for future plasma-burning experiments at ITER. The high neutron emission and very large plasma size in ITER make neutron diagnostics the main diagnostic method used to measure plasma parameters such as fusion power, fusion power density, ion temperature, energy of fast ions and their spatial distributions in the plasma core. Active spectroscopy techniques are methods where a neutral particle beam is injected into the plasma and information on plasma parameters is extracted from the measurement of line emission resulting from the beam-plasma interaction, either by plasma ions or by beam atoms. Spatial localization is achieved by crossing the beamline and multiple observation lines. The ITER plasma will be a high temperature, moderately dense, fully ionized collisional plasma. The plasma facing surfaces are principally metallic being fashioned from beryllium or tungsten but many other elements, arising from either structural or from operational needs, may enter this plasma. The energy range of the emitted photons range from meV (infra-red) to multi keV (x-rays) and originate from all areas of the plasma volume. The primary role of passive emission diagnostics is to identify what is in the plasma from spectral signatures. Extracting quantitative information from these measurements such as impurity content, ion temperature, rotation, degree of detachment and radiated power depends on calibrated instruments, a physics model of the atomic and molecular processes and plasma transport and an analysis workflow that takes into account environmental effects such as reflections. The particular needs for ITER have prompted a multi-machine, many-year effort to address all these aspects and this chapter reviews the work on diagnostic design, experiments and new analysis techniques. An overview of the laser diagnostics to be implemented on ITER is also provided in this paper. This includes descriptions of the Thomson scattering in the core, edge and divertor regions, polarimetry and interferometry diagnostics used for measuring plasma density and also measurements of helium density in the divertor using Laser Induced Flourescence. Techniques which can allow improvements on current measurements are also addressed in particular expanding poloidal polarimetry measurements to measure field fluctuations and proposed use of dispersion interferometery which has a number of advantages over existing methods. This paper identifies particular areas where further research and testing on existing tokamaks is useful even at this advanced stage to inform the design of diagnostics for ITER. Outstanding areas of concern for the implementation of laser diagnostics, in particular with a view to reliable operation are identified. An overview of the latest developments of microwave diagnostic systems and techniques is given. The primary focus is the contributions for ITER—the next step burning plasma experiment—which is supplemented by describing recent progress of techniques applicable for fusion experiments beyond ITER. The contributions are intentionally kept concise, and are being supplemented by a rich list of references for further studies. Radiation induced effects are receiving continuous and well-deserved attention of the ITER diagnostic community and they are in many cases one of the primary design drivers of the ITER diagnostic systems. The paper summarizes recent progress in this area focusing primarily on the ITER diagnostics but in some cases provides also outlook for the possible solutions for even more demanding radiation environment of fusion reactors beyond ITER. Despite advancements in the area of modeling and simulation of various radiation induced effects, experimental testing in a nuclear environment as close as possible to the target one is still seen as unavoidable for proper qualification of particular diagnostic functional elements. Recent advancement within three diagnostic areas: optical diagnostics, magnetics and bolometers is covered. Encouraging results on qualification of silica glass vacuum window assemblies are presented. In the area of magnetic sensors, progress of irradiation tests performed on ITER in-vessel LTCC inductive sensors is presented with outlook for novel technological approaches to inductive sensors utilizing thick printing and photolithography technologies being highlighted. Summary of advancements in the area of steady state magnetic field sensors based on Hall effect is given. New results of neutron irradiation test of the ITER borosilicate glass inserts for vacuum electrical feedthroughs are summarized finding negligible swelling at target level of neutron fluence. Off-line irradiation tests of fiber optic current sensors for plasma current measurement demonstrated that both for gamma doses up to 5 MGy and a total neutron fluence up to 10 15 cm −2 , radiation induced changes are still compatible with required measurement accuracy on ITER. The ITER bolometers are given as an example how considering radiation effects may influence the diagnostic design. Finally, outlook for future main R&D directions is outlined. All optical and laser-based diagnostics in ITER will be using mirrors to guide plasma radiation toward detectors, cameras and sensors. In the hostile plasma, radiation and particle environment the optical characteristics of diagnostic mirrors will degrade directly affecting the entire performance of involved diagnostic systems. An assessment of factors affecting mirror performance is provided. Among the prime adverse factors are deposition of plasma impurities, sputtering of mirror surface and steam ingress in the vicinity of mirrors. Within the International Tokamak Physics Activity with active support by ITER central team and domestic agencies, the structured research and development (R&D) program on mitigation of risks for diagnostic mirrors is underway. Within this program the mirror material development, the passive mitigation of mirror degradation by using diagnostic ducts and shutters along with an active mirror recovery program comprising the in-situ mirror cleaning and calibration is underway. Recent developments in diagnostic mirror R&D are described in this Chapter along with an example of their implementation of R&D solutions in ITER Infrared Thermography diagnostic. An assessment of still open engineering and physics questions, considerations on mirror risks during an early phase of ITER operation are given along with an overview of diagnostic mirror evolution in the late ITER operation stage toward the demonstration fusion power plant. Several crucial areas of diagnostic R&D outlined in ITER Research Plan are addressed. The basic control groups in a fusion reactor can be broken-down in five categories: (1) plasma position, magnetic configuration, and plasma current control, (2) profile control and confinement optimization, (3) MHD control and suppression, (4) edge dissipation control, radiation and plasma exhaust control and (5) break-down optimization. These categories are coupled via the physics (a control action in one domain will affect the other domains) and via shared actuators (e.g. ECRH for impurity accumulation avoidance, current density distribution control and MHD suppression). Consequently, a supervisory control system should determine the priority of the various control tasks, their couplings, and the interfaces with the safety and interlock system. For the systematic development of the various controllers taking the complexity of the plasma and the control system into account, a model-based approach is required. A short historical overview is given of the developments in systems and control theory and control engineering with special emphasis on those developments that are most relevant for Nuclear Fusion research and operation. An overview is given of the state of the field of fusion plasma control for the control categories. It will be shown how synthetic diagnostics are being developed in ITER and how they are used in diagnostic design and design validation and how they can be in model-based controller synthesis using relatively simple models. In modern control methods, multiple diagnostics are used to constrain relatively simple models. The constrained models provide an estimate for the state. This opens the route to state controllers, such as model predictive control. A major challenge in nuclear fusion research is the coherent combination of data from heterogeneous diagnostics and modeling codes for machine control and safety as well as physics studies. Measured data from different diagnostics often provide information about the same subset of physical parameters. Additionally, information provided by some diagnostics might be needed for the analysis of other diagnostics. A joint analysis of complementary and redundant data allows, e.g. to improve the reliability of parameter estimation, to increase the spatial and temporal resolution of profiles, to obtain synergistic effects, to consider diagnostics interdependencies and to find and resolve data inconsistencies. Physics-based modeling and parameter relationships provide additional information improving the treatment of ill-posed inversion problems. A coherent combination of all kind of available information within a probabilistic framework allows for improved data analysis results. The concept of integrated data analysis (IDA) in the framework of Bayesian probability theory is outlined and contrasted with conventional data analysis. Components of the probabilistic approach are summarized and specific ingredients beneficial for data analysis at fusion devices are discussed.

ITER↗

Microreactor Demonstration and Testing Progress in FY19

Microreactors are an emerging technology for providing energy at remote locations and/or for relatively low power applications. As vendors are looking into specific microreactor designs, one goal of the DOE-NE Microreactor program is to develop advanced core, heat removal, and power conversion technologies and testing facilities at which design features can be experimentally proven. FY19 tasks included designing a nonnuclear test bed and test articles to advance microreactor development through proof-of-principle experiments without the complex infrastructure required for a full nuclear test bed. The nonnuclear test bed capability is being established at Idaho National Laboratory, and both Idaho and Los Alamos National Laboratories are designing test articles to demonstrate technology in the test bed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Conducting Beyond the Standard Model Searches in the MicroBooNE Detector with Machine Learning

MicroBooNE is one of the three neutrino detectors that comprise the Short Baseline Neutrino program at Fermilab. It utilizes Liquid Argon Time Projection Chamber (LArTPC) technology to probe the anomalous excess of electron-like events seen by its predecessor, MiniBooNE. Additionally, it provides a rich avenue of study for Beyond the Standard Model (BSM) theories. In the GeV energy regime relevant to MicroBooNE's beam neutrino program, many such theories lead to signatures which produce electron-positron (e+e-) final-states in the detector. While photons can pair produce into e+e- pairs with negligible opening angles, BSM theories often predict e+e- pairs with a broader range of opening angles. Thus, developing a tool that can reliably measure the opening angles of e+e- events is crucial for conducting rigorous BSM studies. However, these e+e- pairs result in topologically complex showers instead of clean tracks, making non-machine learning (ML) based methods such as line-fitting unsuitable for this task. This poster discusses the effectiveness of ML, namely a graph neural network called PointNet++, in accomplishing this goal. Our studies show promising results, with a resolution for the opening angle of 5 or less.

Bhelande, Vedang Adutya [Los Alamos]↗