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NASA NTRS2004

Education Outreach Associated with Technology Transfer in a Colonia of South Texas: Green Valley Farms Science and Space Club for Middle School Aged Children in Green Valley Farms, San Benito, Texas

Texas colonias are unincorporated subdivisions characterized by inadequate water and wastewater infrastructure, inadequate drainage and road infrastructure, substandard housing, and poverty. Since 1989 the Texas Legislature has implemented policies to halt further development of colonias and to address water and wastewater infrastructure needs in existing and new colonias along the border with Mexico. Government programs and non-government and private organization projects aim to address these infrastructure needs. Texas Tech University's Water Resources Center demonstrated the use of alternative on-site wastewater treatment in the Green Valley Farms colonia, San Benito, Texas. The work in Green Valley Farms was a component of a NASA-funded project entitled Evaluation of NASA's Advanced Life Support Integrated Water Recovery System for Non-Optimal Conditions and Terrestrial Applications. Two households within the colonia are demonstration sites for constructed wetlands. A colonia resident and activist identified educational opportunities for colonia children as a primary goal for many colonia residents. Colonia parents view education as the door to opportunity and escape from poverty for their children. The educational outreach component of the project in Green Valley Farms was a Science and Space Club for middle-school age students. Involved parents, schoolteachers, and school administrators enthusiastically supported the monthly club meetings and activities. Each month, students participated in interactive learning experiences about water use and reuse in space and on earth. Activities increased knowledge and interest in water resource issues and in science and engineering fields. The Institute for the Development and Enrichment of Advanced Learners (IDEAL) at Texas Tech University provided full scholarships for five students from Green Valley Farms to attend the Shake Hands With Your Future camp at Texas Tech University in June 2003. The educational outreach component was evaluated in February 2004 using survey instruments for students and parents, and interviews with science teachers and counselors.

Potess, Marla D.↗

NASA NTRS2009

The Art and Science of Systems Engineering

The National Aeronautics and Space Administration (NASA) was established in 1958, and its Marshall Space Flight Center was founded in 1960, as space-related work was transferred from the Army Ballistic Missile Agency at Redstone Arsenal, where Marshall is located. With this heritage, Marshall contributes almost 50 years of systems engineering experience with human-rated launch vehicles and scientific spacecraft to fulfill NASA's mission exploration and discovery. These complex, highly specialized systems have provided vital platforms for expanding the knowledge base about Earth, the solar system, and cosmos; developing new technologies that also benefit life on Earth; and opening new frontiers for America's strategic space goals. From Mercury and Gemini, to Apollo and the Space Shuttle, Marshall's systems engineering expertise is an unsurpassed foundational competency for NASA and the nation. Current assignments comprise managing Space Shuttle Propulsion systems; developing environmental control and life support systems and coordinating science operations on the International Space Station; and a number of exploration-related responsibilities. These include managing and performing science missions, such as the Lunar Crater Observation and Sensing Satellite and the Lunar Reconnaissance Orbiter slated to launch for the Moon in April 2009, to developing the Ares I crew launch vehicle upper stage and integrating the vehicle stack in house, as well as designing the Ares V cargo launch vehicle and contributing to the development of the Altair Lunar Lander and an International Lunar Network with communications nodes and other infrastructure.

Singer, Christopher E.↗

NASA NTRS2009

Integrated System Health Management: Foundational Concepts, Approach, and Implementation

Implementation of integrated system health management (ISHM) capability is fundamentally linked to the management of data, information, and knowledge (DIaK) with the purposeful objective of determining the health of a system. It is akin to having a team of experts who are all individually and collectively observing and analyzing a complex system, and communicating effectively with each other in order to arrive to an accurate and reliable assessment of its health. This paper presents concepts, procedures, and a specific approach as a foundation for implementing a credible ISHM capability. The capability stresses integration of DIaK from all elements of a subsystem. The intent is also to make possible implementation of on-board ISHM capability, in contrast to a remote capability. The information presented is the result of many years of research, development, and maturation of technologies, and of prototype implementations in operational systems (rocket engine test facilities). The paper will address the following topics: ISHM Model of a system; detection of anomaly indicators; determination and confirmation of anomalies; diagnostic of causes and determination of effects; consistency checking cycle; sharing of health information; sharing of display information; storage and retrieval of health information; and example implementation.

Figueroa, Fernando↗

NASA NTRS2010

Propulsion System Choices and Their Implications

In defining a space vehicle architecture, the propulsion system and related subsystem choices will have a major influence on achieving the goals and objectives desired. There are many alternatives and the choices made must produce a system that meets the performance requirements, but at the same time also provide the greatest opportunity of reaching all of the required objectives. Recognizing the above, the SPST Functional Requirements subteam has drawn on the knowledge, expertise, and experience of its members, to develop insight that wiIJ effectively aid the architectural concept developer in making the appropriate choices consistent with the architecture goals. This data not only identifies many selected choices, but also, more importantly, presents the collective assessment of this subteam on the "pros" and the "cons" of these choices. The propulsion system choices with their pros and cons are presented in five major groups. A. System Integration Approach. Focused on the requirement for safety, reliability, dependability, maintainability, and low cost. B. Non-Chemical Propulsion. Focused on choice of propulsion type. C. Chemical Propulsion. Focused on propellant choice implications. D. Functional Integration. Focused on the degree of integration of the many propulsive and closely associated functions, and on the choice of the engine combustion power cycle. E. Thermal Management. Focused on propellant tank insulation and integration. Each of these groups is further broken down into subgroups, and at that level the consensus pros and cons are presented. The intended use of this paper is to provide a resource of focused material for architectural concept developers to use in designing new advanced systems including college design classes. It is also a possible source of input material for developing a model for designing and analyzing advanced concepts to help identify focused technology needs and their priorities.

Joyner, Claude R., II↗

NASA NTRS1987

Commonality analysis as a knowledge acquisition problem

Commonality analysis is a systematic attempt to reduce costs in a large scale engineering project by discontinuing development of certain components during the design phase. Each discontinued component is replaced by another component that has sufficient functionality to be considered an appropriate substitute. The replacement strategy is driven by economic considerations. The System Commonality Analysis Tool (SCAT) is based on an oversimplified model of the problem and incorporates no knowledge acquisition component. In fact, the process of arriving at a compromise between functionality and economy is quite complex, with many opportunities for the application of expert knowledge. Such knowledge is of two types: general knowledge expressible as heuristics or mathematical laws potentially applicable to any set of components, and specific knowledge about the way in which elements of a given set of components interrelate. Examples of both types of knowledge are presented, and a framework is proposed for integrating the knowledge into a more general and useable tool.

Yeager, Dorian P.↗

NASA NTRSDate not supplied

Developing the Foundations of an Exploration Class Medical System: Bridging the Capability Gap between LEO and Mars

NASA's overarching mission is to drive advances in science, technology, aeronautics, and space exploration to enhance knowledge, education, innovation, economic vitality, and Earth stewardship. Its Artemis Program encompasses the next steps in human space exploration, critical elements of which include the Orion vehicle and Gateway outpost in lunar orbit. Artemis is the first "exploration class" space travel system with the stated mission to establish sustainable exploration of the lunar surface and serve as a bridge to future human exploration of Mars. Success requires understanding both the commonalities and the differences between our experience to date, predominantly in Low-Earth-Orbit, and the challenges expected in long-duration exploration missions. In addition, these missions will incur significant constraints on both vehicle and human systems. To that end, NASA Exploration Medical Capability (ExMC) element clinicians, engineers, and scientists are developing the "foundation" for a fully vehicle-integrated exploration-class medical system using model-based systems engineering to trace and leverage clinical and engineering content to expand and refine the fidelity and confidence in the medical system itself.

MEDICAL SYSTEMS↗

DOE OSTI2024

Summary of a Workshop on Integrating Energy Equity into Power System Planning and Operation

On November 29 and 30, 2023, the Energy Systems Integration Group convened a one-and-a-half-day workshop in Arlington, Virginia, to improve knowledge and practices for integrating energy equity considerations into power system planning and operation. Workshop participants were invited to discuss approaches for integrating energy equity aspects into models and tools and to explore possible pathways for enhanced technical practices. Participants included utility planning and modeling practitioners, social science and energy justice academics, representatives from energy and environmental justice nongovernmental organizations, non-profits, consumer advocates, and representatives from the U.S. Department of Energy, national labs, and other government agencies. This report summarizes the discussion points and outcomes of the workshop, which prioritized the technical aspects of incorporating the challenges and solutions identified by electricity-futures stakeholders and communities. These technical activities include the fundamental grid structure and hardware, detailed modeling approaches that benchmark energy system performance and justify future investments, and engineering designs and limitations for potential solutions to meet energy equity goals in communities.

energy equity↗

DOE OSTI2024

Use Case-Informed Framework for Utility Cloud Migration

This white paper presents a comprehensive methodology for assessing utilities’ cloud postures and frameworks. It aims to produce a roadmap and strategy for a cloud-enabled grid future, providing guidance for integrators, asset owners, and operators. Instead of offering a yes or no answer for cloud implementation, this framework offers strategic guidance on responsibly preparing for and deploying cloud solutions. This paper delves into cloud-service models pertinent to the electric sector, dissecting the shared responsibility model and elucidating what on-premise infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) entail. A pivotal consideration within the context of the shared responsibility model is the allocation of responsibility for foundational security aspects—a decision that will be informed by a comprehensive risk assessment. The ensuing discussion will present a checklist of certifications necessary for a secure cloud transition, equipping utilities with the knowledge to navigate this digital transformation with confidence and with a strategic roadmap. Furthermore, the paper outlines gaps in understanding the U.S. government’s role in shaping technology development and responsible use. Its purpose is to aid decision-making by offering support for risk-informed solutions that benefit those managing assets and operating in the cloud environment. The primary objective is to enhance the resilience and future readiness of a decarbonized electric grid, with cloud solutions as one viable option. The paper synthesizes information on current and future grid architectures and applications, considering both conservative and progressive energy transitions, along with scalable and distributed computing considerations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRSDate not supplied

Exploring the Moon: Training Astronauts for Artemis Missions

For the first time in over 50 years, NASA scientists are training astronauts assigned to lunar missions. The successful Artemis 1 test flight in 2022 set the stage for future human missions to the Moon. The Artemis II mission will send astronauts to fly by the Moon, providing the first opportunity since Apollo for humans to directly observe and document the lunar surface from a few thousand kilometers, including the lunar far side. Artemis III will be the first human mission to land and explore the Moon’s surface near the lunar South Pole. To prepare for Artemis exploration, geology training for the Artemis II crew and detailed training plans for Artemis III and beyond are in full swing. NASA scientists have been working for several years with the Flight Operations Directorate (FOD) at the Johnson Space Center on a progressive geology training program that starts with incoming astronaut classes, offers opportunities to maintain the geology proficiency between flight assignments, and trains the broader engineering-operations-management community in geology and field operations. In the spring of 2023, our team developed an introductory week-long class called Lunar Fundamentals and delivered this content to the Artemis II crew, as well as key NASA Flight Control Team members. The Lunar Fundamentals curriculum combines basic geographic and geologic information about the Moon; includes detailed content on major geologic processes, the lunar environment, lunar volatiles, and sample science; provides historical context of our growing knowledge about the Moon; and introduces new lunar data sets and tools for exploring the lunar surface. With the Lunar Fundamentals foundation, we continue development of the advanced planetary science classroom content for the Artemis III crew, together with field training science objectives and plans. Partnering with our FOD colleagues, we are building an integrated training plan for lunar science, exploration field methods, and operational skills to ensure that the Artemis astronauts exploring the Moon will have the necessary foundation in geology and lunar science to achieve mission success.

Astronauts training↗

DOE OSTI2019

Combining Tensor Decompositions and Graph Analytics to Provide Cyber Situational Awareness at HPC Scale

This paper describes MADHAT (Multidimensional Anomaly Detection fusing HPC, Analytics, and Tensors), an integrated workflow that demonstrates the applicability of HPC resources to the problem of maintaining cyber situational awareness. MADHAT combines two high-performance packages: ENSIGN for large-scale sparse tensor decompositions and HAGGLE for graph analytics. Tensor decompositions isolate coherent patterns of network behavior in ways that common clustering methods based on distance metrics cannot. Parallelized graph analysis then uses directed queries on a representation that combines the elements of identified patterns with other available information (such as additional log fields, domain knowledge, network topology, whitelists and blacklists, prior feedback, and published alerts) to confirm or reject a threat hypothesis, collect context, and raise alerts. MADHAT was developed using the collaborative HPC Architecture for Cyber Situational Awareness (HACSAW) research environment and evaluated on structured network sensor logs collected from Defense Research and Engineering Network (DREN) sites using HPC resources at the U.S. Army Engineer Research and Development Center DoD Supercomputing Resource Center (ERDC DSRC). To date, MADHAT has analyzed logs with over 650 million entries.

Cyber security, Tensor decomposition, graph analyt↗

NASA NTRS2015

A Vehicle Management End-to-End Testing and Analysis Platform for Validation of Mission and Fault Management Algorithms to Reduce Risk for NASA's Space Launch System

The engineering development of the new Space Launch System (SLS) launch vehicle requires cross discipline teams with extensive knowledge of launch vehicle subsystems, information theory, and autonomous algorithms dealing with all operations from pre-launch through on orbit operations. The characteristics of these spacecraft systems must be matched with the autonomous algorithm monitoring and mitigation capabilities for accurate control and response to abnormal conditions throughout all vehicle mission flight phases, including precipitating safing actions and crew aborts. This presents a large and complex system engineering challenge, which is being addressed in part by focusing on the specific subsystems involved in the handling of off-nominal mission and fault tolerance with response management. Using traditional model based system and software engineering design principles from the Unified Modeling Language (UML) and Systems Modeling Language (SysML), the Mission and Fault Management (M&FM) algorithms for the vehicle are crafted and vetted in specialized Integrated Development Teams (IDTs) composed of multiple development disciplines such as Systems Engineering (SE), Flight Software (FSW), Safety and Mission Assurance (S&MA) and the major subsystems and vehicle elements such as Main Propulsion Systems (MPS), boosters, avionics, Guidance, Navigation, and Control (GNC), Thrust Vector Control (TVC), and liquid engines. These model based algorithms and their development lifecycle from inception through Flight Software certification are an important focus of this development effort to further insure reliable detection and response to off-nominal vehicle states during all phases of vehicle operation from pre-launch through end of flight. NASA formed a dedicated M&FM team for addressing fault management early in the development lifecycle for the SLS initiative. As part of the development of the M&FM capabilities, this team has developed a dedicated testbed that integrates specific M&FM algorithms, specialized nominal and off-nominal test cases, and vendor-supplied physics-based launch vehicle subsystem models. Additionally, the team has developed processes for implementing and validating these algorithms for concept validation and risk reduction for the SLS program. The flexibility of the Vehicle Management End-to-end Testbed (VMET) enables thorough testing of the M&FM algorithms by providing configurable suites of both nominal and off-nominal test cases to validate the developed algorithms utilizing actual subsystem models such as MPS. The intent of VMET is to validate the M&FM algorithms and substantiate them with performance baselines for each of the target vehicle subsystems in an independent platform exterior to the flight software development infrastructure and its related testing entities. In any software development process there is inherent risk in the interpretation and implementation of concepts into software through requirements and test cases into flight software compounded with potential human errors throughout the development lifecycle. Risk reduction is addressed by the M&FM analysis group working with other organizations such as S&MA, Structures and Environments, GNC, Orion, the Crew Office, Flight Operations, and Ground Operations by assessing performance of the M&FM algorithms in terms of their ability to reduce Loss of Mission and Loss of Crew probabilities. In addition, through state machine and diagnostic modeling, analysis efforts investigate a broader suite of failure effects and associated detection and responses that can be tested in VMET to ensure that failures can be detected, and confirm that responses do not create additional risks or cause undesired states through interactive dynamic effects with other algorithms and systems. VMET further contributes to risk reduction by prototyping and exercising the M&FM algorithms early in their implementation and without any inherent hindrances such as meeting FSW processor scheduling constraints due to their target platform - ARINC 653 partitioned OS, resource limitations, and other factors related to integration with other subsystems not directly involved with M&FM such as telemetry packing and processing. The baseline plan for use of VMET encompasses testing the original M&FM algorithms coded in the same C++ language and state machine architectural concepts as that used by Flight Software. This enables the development of performance standards and test cases to characterize the M&FM algorithms and sets a benchmark from which to measure the effectiveness of M&FM algorithms performance in the FSW development and test processes.

Trevino, Luis↗

NASA NTRSDate not supplied

Nancy Grace Roman Space Telescope Observatory Implementation and Challenges

NASA’s Nancy Grace Roman Space Telescope (Roman), previously referred to as Wide Field Infrared Survey Telescope (WFIRST), was named after Dr. Nancy Grace Roman, an astronomer and NASA pioneer of modern space-based astronomy who is known as the “mother of the Hubble Space Telescope”. Roman is a deep space infrared observatory with a Hubble-sized telescope and wide field of view instrument (greater than 100 times that of Hubble’s) that will conduct a high latitude time-domain survey, a high latitude imaging and spectroscopic survey, and a galactic bulge time-domain survey to characterize dark energy and expand the census of exoplanets in our galaxy while allowing a broad range of astrophysics research. Roman will also demonstrate exoplanet coronagraphy with active wave front control technology and provide general investigator programs for the science community. Roman is finishing the critical design phase and is planning for launch in 2026. It will operate in a quasi-halo orbit about Sun-Earth L2, 1.5 million kilometers from Earth, for a five-year primary mission life. The Observatory features an Optical Telescope Assembly with an existing, repurposed 2.4m primary mirror, a Wide Field Instrument with a focal plane array comprised of 18 HgCdTe near-infrared detectors and a grism, prism and filter elements for imaging and spectroscopy in support of the primary surveys, as well as a Coronagraph instrument technology demonstration with starlight suppression technology for direct imaging and spectroscopy of exoplanets. The telescope is mounted to the Instrument Carrier composite truss structure which also optically meters each instrument, includes a Launch Load and Vibration Isolation System to provide passive isolation of spacecraft jitter sources while also supporting the payload during launch and is attached to the Spacecraft Bus. The Spacecraft also includes a Solar Array Sunshield ,Deployable Aperture Cover, Lower Instrument Sunshade, High Gain Antenna System, and Outer Barrel Assembly. Figure 1 shows an overview of the Roman Observatory. When fully integrated, Roman will be the largest Observatory assembled and tested at NASA’s Goddard Space Flight Center. Figure 1. Roman Observatory Overview Development of scientific satellites is challenging by nature, as the pursuit to broaden scientific knowledge always pushes the boundary of what has come before. The implementation of the Roman mission is a prime example and expected challenges have been augmented by the foundational decision to use the existing telescope components, developed in the early 2000s by another Government agency for a different application. Other unique aspects of the Roman mission, such as its survey nature, the vast amount of data required to meet science objectives, and packaging of the Observatory elements around the existing telescope components, create constrained design spaces that drive competing requirements across Observatory subsystems. Given these challenges, systems engineering has been a critical discipline in balancing implementation decisions for the Roman mission and will continue to play a key role going forward. This paper will discuss details of the Roman Observatory configuration, as well as some of the systems engineering challenges and the decision-making process used to mature the Roman Space Telescope preliminary design to implementation.

Lisa Ml Bartusek↗

NASA NTRSDate not supplied

Overview of NASA's RESOURCE (Resource Exploration and Science of OUR Cosmic Environment) Project

The RESOURCE(Resource Exploration and Science of OUR Cosmic Environment) project is supported by NASA’s SSERVI (Solar System Exploration Research Virtual Institute) and is led by Principal Investigator (PI) Dr. Jennifer L. Heldmann and Deputy PIs Dr. Matthew Deans and Dr. Alexander Sehlke at NASA Ames Research Center. RESOURCE is focused on enabling In Situ Resource Utilization (ISRU) near the sites of robotic and/or human missions to enable sustainable and affordable exploration of the Moon and near-Earth objects (NEOs). This year RESOURCE has supported the development of a summary of the current state of knowledge regarding lunar polar volatiles as well as a comprehensive catalog and analysis of NEOs. RESOURCE is developing advanced mission capabilities to enable rapid, collaborative operations for lunar resource exploration missions. Hardware testing has been conducted to evaluate potential contaminants released during lunar polar regolith heating. Volatiles and particulates are the two sources of contamination that will affect the requirements of a water cleanup system, and RESOURCE research has identified the requirement for filtration prior to electrolysis for ISRU. RESOURCE also supports development of next-generation planetary drilling systems with integrated instrumentation within the drill. RESOURCE is also deeply committed to sustained efforts to engage educators, students, and broadening participation among under represented groups, and has partnered with Howard University in Washington, DC to foster minoritized students’ interest in STEAMD (science, technology, engineering, arts, math, design) careers through direct and virtual experiences with NASA Subject Matter Experts.

Jennifer L Heldmann↗

DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

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.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2004

Robust Requirements Tracing via Internet Search Technology: Improving an IV and V Technique

There are three major objectives to this phase of the work. (1) Improvement of Information Retrieval (IR) methods for Independent Verification and Validation (IV&V) requirements tracing. Information Retrieval methods are typically developed for very large (order of millions - tens of millions and more documents) document collections and therefore, most successfully used methods somewhat sacrifice precision and recall in order to achieve efficiency. At the same time typical IR systems treat all user queries as independent of each other and assume that relevance of documents to queries is subjective for each user. The IV&V requirements tracing problem has a much smaller data set to operate on, even for large software development projects; the set of queries is predetermined by the high-level specification document and individual requirements considered as query input to IR methods are not necessarily independent from each other. Namely, knowledge about the links for one requirement may be helpful in determining the links of another requirement. Finally, while the final decision on the exact form of the traceability matrix still belongs to the IV&V analyst, his/her decisions are much less arbitrary than those of an Internet search engine user. All this suggests that the information available to us in the framework of the IV&V tracing problem can be successfully leveraged to enhance standard IR techniques, which in turn would lead to increased recall and precision. We developed several new methods during Phase II; (2) IV&V requirements tracing IR toolkit. Based on the methods developed in Phase I and their improvements developed in Phase II, we built a toolkit of IR methods for IV&V requirements tracing. The toolkit has been integrated, at the data level, with SAIC's SuperTracePlus (STP) tool; (3) Toolkit testing. We tested the methods included in the IV&V requirements tracing IR toolkit on a number of projects.

Hayes, Jane↗

DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

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.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

CACTUS: Chemistry Agent Connecting Tool Usage to Science

Large language models (LLMs) have shown remarkable potential in various domains but often lack the ability to access and reason over domain-specific knowledge and tools. In this article, we introduce Chemistry Agent Connecting Tool-Usage to Science (CACTUS), an LLM-based agent that integrates existing cheminformatics tools to enable accurate and advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama3-8b, and Mistral-7b, on a benchmark of thousands of chemistry questions. Our results demonstrate that CACTUS significantly outperforms baseline LLMs, with the Gemma-7b, Mistral-7b, and Llama3-8b models achieving the highest accuracy regardless of the prompting strategy used. Moreover, we explore the impact of domain-specific prompting and hardware configurations on model performance, highlighting the importance of prompt engineering and the potential for deploying smaller models on consumer-grade hardware without a significant loss in accuracy. By combining the cognitive capabilities of open-source LLMs with widely used domain-specific tools provided by RDKit, CACTUS can assist researchers in tasks such as molecular property prediction, similarity searching, and drug-likeness assessment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗