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To aid contributions to the Snowmass 2021 US Community Study on physics at the International Linear Collider and other proposed e e- colliders, we present a list of study questions that could be the basis of useful Snowmass projects. We accompany this with links to references and resources on e e- physics, and a description of a new software framework that we are preparing for e e- studies at Snowmass.
Liquid Argon Time Projection Chamber (LArTPC) neutrino detectors have emerged as a detector technology poised to perform measurements of neutrino interactions with unprecedented precision and as a result answer some of the largest open questions in neutrino physics in the coming decade. This thesis describes methods developed to tackle the computational challenges faced as LArTPC detectors increase in scale and complexity moving towards the multi-kiloton DUNE detectors. These include a new, approximated, model that enables rapid simulation of scintillation light in very large scale detectors, as well as the first demonstration of running the LArSoft software framework on a high performance computer. The high precision of LArTPC detectors designed for next-generation neutrino measurements enables them to also search for Beyond the Standard Model physics produced in high-energy proton--fixed-target collisions in neutrino beams. This thesis presents searches for two dark-sector models performed with the ArgoNeuT experiment: Heavy Neutral Leptons and Heavy QCD Axions. Between them, these models can provide solutions for various unresolved puzzles including neutrino mass generation, the baryon asymmetry of the universe, dark matter and the strong CP problem. In both cases, the dark-sector particles could be produced in the NuMI neutrino beam and can then decay to a pair of oppositely charged muons observable in ArgoNeuT and the downstream MINOS near detector. Both measurements required the development of novel experimental selection techniques and enabled new constraints to be set on the existence of these particles in previously unexplored parameter-space. These searches are both the first of their kind in LArTPC neutrino detectors and pave the way for searches at future neutrino facilities.Date of Award31 Dec 2022Original languageEnglishAwarding InstitutionThe University of ManchesterSupervisorStefan Soldner-Rembold (Supervisor) & Justin Evans (Supervisor)
Current methods for structuring nuclear waste management, disposal, and remediation decisions are subject to three major limitations. First, they are based on a series of extraordinarily conservative assumptions. The dominant way of thinking is that, for example, conservative models yield better, safer decisions. Second, they lack methods to meaningfully address and incorporate the 'human dimension'. The dominant way of thinking is that the current approach is sufficient to bring together the values and interests of both internal and external stakeholders in making a decision. And third, they lack a formal method through which both internal and external stakeholders are encouraged collectively to 'think through' complex problems that contain multiple competing objectives so that wise, socially acceptable, and scientifically-defensible decisions are made. To overcome these limitations, a paradigm shift is needed towards a holistic, integrated approach to structure nuclear waste management, disposal and remediation decisions. The first requirement of this paradigm shift is the realization and recognition that the problems faced are decision problems, and that there is a formal theory of decision analysis that can, and should, be used to support finding the best solutions. Decision analysis, very simply, is formalized common sense. The reason it is needed for complex problems is that there are too many moving parts (variables) for humans to manage with out the formalism. Decision analysis leads to addressing problem solving using a combination of a science-based model and a costs/values model. Expectation ('realism') and uncertainty are addressed in the science-based models, whereas stakeholder values, preferences, and desires are addressed when modeling costs and value judgments. Science-based modeling tools have been developed, improved, and used since the current regulatory setting was introduced about 30 years ago. However, modeling of costs and value judgments has not often been addressed using any formal approach, despite the openings that exist in regulations (e.g., CERCLA's modifying criteria and the concept of 'as low as reasonably achievable' referenced in DOE Orders). A discussion of the need and rationale for this paradigm shift is presented, followed by a description of the technical basics of this approach to decision modeling, and ending with an explanation of the steps that are taken in this approach to structured decision-making. A software framework program called Guided Interactive Statistical Decision Tools (GiSdT - pronounced 'gist') is introduced that supports this paradigm shift. (authors)
Modern astronomical surveys have multiple competing scientific goals. Optimizing the observation schedule for these goals presents significant computational and theoretical challenges, and state-of-the-art methods rely on expensive human inspection of simulated telescope schedules. Automated methods, such as reinforcement learning, have recently been explored to accelerate scheduling. However, there do not yet exist benchmark data sets or user-friendly software frameworks for testing and comparing these methods. We present DeepSurveySim -- a high-fidelity and flexible simulation tool for use in telescope scheduling. DeepSurveySim provides methods for tracking and approximating sky conditions for a set of observations from a user-supplied telescope configuration. We envision this tool being used to produce benchmark data sets and for evaluating the efficacy of ground-based telescope scheduling algorithms, particularly for machine learning algorithms that would suffer in efficacy if limited to real data for training.We introduce three example survey configurations and related code implementations as benchmark problems that can be simulated with DeepSurveySim.
Structured decision making presents an opportunity for DOE and others engaged in solving complex environmental and waste management problems to find solutions that are stakeholder engaged and cost effective. This approach represents a paradigm shift that was introduced in a series of papers and a panel session at the WM2015 Symposium. The advantages of structured decision making over less formal approaches to decision making include technical defensibility, traceability and transparency. Structured decision making also overcomes the limitations of misapplication of conservatism in modeling, addresses stakeholder concerns and competing objectives directly, and provides a formal method through which internal and external stakeholders are encouraged to collectively and carefully consider all relevant attributes of solving complex problems. The first requirement of this paradigm shift is a realization and recognition that the problems faced are inherently decision problems, and that there is a formal theory of decision analysis that can, and should, be used to support finding the best solutions. Structured decision making is, very simply, formalized common sense. The formalities include: framing the problem; identification of objectives and measures for valuing those objectives; costs or values associated with those objectives; management options or alternatives for taking action that are tied directly to the objectives; and, science-based models to evaluate the options. Decision analysis is used to evaluate objectives and choose management options by balancing probability and consequence. The science-based models are built probabilistically based on expectation and uncertainty, and costs and value judgments are used to fulfill addressing stakeholder concerns. Consequently, this approach avoids misplaced conservatism in science-based modeling and properly places values in the objectives component of a decision model. This approach honestly addresses uncertainty and value judgments and balances these competing components to find the most effective decision. A software framework program called Guided Interactive Statistical Decision Tools (GiSdT - pronounced 'gist') is used to support an application of this paradigm shift towards structured decision making. In this application objectives and management options for a waste disposal problem at the Los Alamos National Laboratory are identified, and features, events and processes are described and defined to form the basis of a conceptual site model. This establishes the decision model structure. Objectives are addressed through the three pillars of sustainability - economic, environmental, and social - and also address regulatory concerns or constraints. Management options include removal of waste to another location and engineering designs for leaving waste in place. Using structured decision making, the relationship between objectives, management options, and the conceptual site model is made clear, and leads to a comprehensive, defensible, transparent and traceable approach to effective decision making. (authors)
Sensitivity analysis (SA) and uncertainty quantification (UQ) are now widely recognized as essential parts of analyses for complex nuclear systems. Plenty of well-established methodologies are already available for performing SA and UQ, such as Monte Carlo analysis, adjoint-based method, and variance decomposition. The focus of this summary is on developing an efficient sampling-based approach to perform SA and UQ through RAVEN. RAVEN (Risk Analysis and Virtual control Environment) is a software framework able to perform SA and UQ, currently under development at the Idaho National Laboratory. In literature, a number of approaches have been developed to accelerate the process of SA and UQ for large complex systems, such as principal component analysis (PCA) of input space, input-output correlation analysis, and intersection subspace methods. The fundamental idea is to identify the dominant active directions in the parameter space prior to conducting SA and UQ. The premise is that the number of active directions is much less than the dimensionality of input space. In this work, the PCA or Karhunen-Loeve expansion or SVD method is implemented in RAVEN to identify the active directions. This approach is also combined with the forward sampling approaches, such as Monte Carlo, Grid, Latin Hypercube, and Response Surface Design sampling approaches that are currently available in RAVEN, to perform SA and UQ more efficiently. The summary is organized as follows. In the following section, we present the PCA-based forward sampling approach. The effectiveness and efficiency are discussed in the numerical results section. The paper then ends with a concluding summary. In this summary, we have presented PCA-based forward sampling approach to perform SA and UQ. The goal is to reduce the computational overhead for repetitive executions of complex nuclear systems that are generally required when performing SA and UQ. The control of correlations between input parameters is implemented in RAVEN to better represent the input relationships. In addition, we are currently examining our implementations with more realistic models, and we are also focusing on implementation of some new high-dimensional input reduction techniques in RAVEN. (authors)
Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.
Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments [https://arxiv.org/abs/2403.11872]. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.
We describe our ongoing research in adaptive computing and multi-fidelity modeling strategies. Our goal is to use a combination of low- and high-fidelity simulation models to enable computationally efficient optimization and uncertainty quantification. We develop optimization formulations that take into account the compute resources currently available, which act as a constraint with regards to the fidelity level simulation we can run while maximizing information gain. These strategies are being implemented into a software framework with a generalized API allowing its application to a broad range of applications, from power grid stability and buildings control to material synthesis and biofuels processing. We will discuss a few examples from these applications that can benefit from this approach, especially when considering challenges arising in scaling up experiments and simulations.
We created a set of standards to efficiently evaluate AI benchmarks through objective means. Although prevalent, especially in recent times, AI benchmarks have no single way to measure their effectiveness. The MLCommons team provided a set of criteria for evaluating benchmarks, although the criteria lacks a clearly defined set of evaluation rules. We created a rubric with preset factors to efficiently and objectively evaluate a benchmark’s quality. We created a software framework for processing lists of benchmarks for visualization. The framework and rating system allows researchers to quickly check if their benchmarks are effective.
Water heating accounts for about 18% of a typical US home’s energy use. Modern water heaters have enabled control options through APIs, offering customers the opportunity to reduce their energy cost and peak demand by dynamically adjusting settings. A water heater’s capacity to store energy using its storage tank makes it an asset for peak demand reduction and energy cost savings. For this reason, a mixed-integer linear programming model is proposed to minimize the energy cost of a high-performance water heater while also reducing the peak demand of the residential household under a time-of-use utility rate by dynamically changing the water heater’s running mode. Specifically, a multi-objective optimization model is formulated to determine the mode settings of the water heater considering hot water use, time-of-use rate, and peak demand limit of the residential household. The mode settings are associated with different dead bands of water temperature for triggering on/off action of the heat pump and heating element. A 66-gal hybrid electric high performance water heater was used for numerical simulation and practical experiments. The simulation results were well aligned with measurements of practical experiments, validating the soundness of the thermodynamic model. In addition, reductions of energy cost, enabling affordability, and reducing peak demand are demonstrated. The research team also developed a software framework with dashboards to automatically and continuously monitor and manage devices.
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In the second volume of the Demonstration Framework Document, the graphical representation of the demonstration framework is given. This second document was created to facilitate the reading and comprehension of the demonstration framework. It is designed to be viewed in parallel with Section 4.2 of the first volume to help give a picture of the relationships between the UOB's (Unit of Behavior) of the model. The model is quite large and the design team felt that this form of presentation would make it easier for the reader to get a feel for the processes described in this document. The IDEF3 (Process Description Capture Method) diagrams of the processes of an Information System Development are presented. Volume 1 describes the processes and the agents involved with each process, while this volume graphically shows the precedence relationships among the processes.
The Real-Time Control (RTC) Application Framework is a high-level software framework written in C++ that supports the rapid design and implementation of object-oriented application programs. This framework provides built-in functionality that solves common software development problems within distributed client-server, multi-threaded, and embedded programming environments. When using the RTC Framework to develop software for a specific domain, designers and implementers can focus entirely on the details of the domain-specific software rather than on creating custom solutions, utilities, and frameworks for the complexities of the programming environment. The RTC Framework was originally developed as part of a Space Shuttle Launch Processing System (LPS) replacement project called Checkout and Launch Control System (CLCS). As a result of the framework s development, CLCS software development time was reduced by 66 percent. The framework is generic enough for developing applications outside of the launch-processing system domain. Other applicable high-level domains include command and control systems and simulation/ training systems.
We describe a framework for model-based analysis and test case generation in the context of a heterogeneous model-based development paradigm that uses and combines Math- Works and UML 2.0 models and the associated code generation tools. This paradigm poses novel challenges to analysis and test case generation that, to the best of our knowledge, have not been addressed before. The framework is based on a common intermediate representation for different modeling formalisms and leverages and extends model checking and symbolic execution tools for model analysis and test case generation, respectively. We discuss the application of our framework to software models for a NASA flight mission.
The Framework Programmable Software Development Platform (FPP) is a project aimed at combining effective tool and data integration mechanisms with a model of the software development process in an intelligent integrated software environment. Guided by the model, this system development framework will take advantage of an integrated operating environment to automate effectively the management of the software development process so that costly mistakes during the development phase can be eliminated. The focus here is on the design of components that make up the FPP. These components serve as supporting systems for the Integration Mechanism and the Framework Processor and provide the 'glue' that ties the FPP together. Also discussed are the components that allow the platform to operate in a distributed, heterogeneous environment and to manage the development and evolution of software system artifacts.
In this paper we present a practical software fault measurement and estimation framework.