In-pixel integration of signal processing and machine learning based data filtering for particle tracking detectors
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Parpillon, Benjamin [Fermilab]↗
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Lightning and Radar Measures of Mixed-Phase Updraft Variability in Tracked Storms during the TRACER Field Campaign in Houston, Texas
Properties of 7488 thunderstorms are summarized for June–September 2022 during the Tracking Aerosol Convection Interactions Experiment (TRACER) field campaign Houston, Texas, using polarimetric weather radar and VHF 3D Lightning Mapping Array data. Automated tracking of storms linked each instrument’s measurements to a data-defined, time-evolving storm footprint. Within each storm, the depth and magnitude of episodic columns of radar differential reflectivity and specific differential phase quantified the prevalence of updrafts that activated mixed-phase precipitation pathways. Lightning measurements further distinguished the degree of rimed precipitation formation: the fraction of tracks with lightning varied from day to day and cells with lightning had stronger polarimetric columns. Track-level correlation of the lightning flash rate with radar polarimetric measures had substantial spread, showing that lightning provides an additional signal of mixed-phase precipitation processes that can complement future studies of thermodynamic and aerosol controls on cloud microphysics in the Houston region.
54 ENVIRONMENTAL SCIENCES↗
All-sky Search for Transient Astrophysical Neutrino Emission with 10 Years of IceCube Cascade Events
Abstract Neutrino flares in the sky are searched for in data collected by IceCube between 2011 and 2021 May. This data set contains cascade-like events originating from charged-current electron neutrino and tau neutrino interactions and all-flavor neutral-current interactions. IceCube’s previous all-sky searches for neutrino flares used data sets consisting of track-like events originating from charged-current muon neutrino interactions. The cascade data set is statistically independent of the track data sets, and while inferior in angular resolution, the low-background nature makes it competitive and complementary to previous searches. No statistically significant flare of neutrino emission was observed in an all-sky scan. Upper limits are calculated on neutrino flares of varying duration from 1 hr to 100 days. Furthermore, constraints on the contribution of these flares to the diffuse astrophysical neutrino flux are presented, showing that multiple unresolved transient sources may contribute to the diffuse astrophysical neutrino flux.
79 ASTRONOMY AND ASTROPHYSICS↗
System and method for transparent augmented reality
A system and method for displaying augmented reality information for a real object. A transparent display displays the information for the real object within a field of view, such that the information and the real object are simultaneously viewable by a user. Data-gathering peripheral devices gather data of the field of view, including a camera gathering visual data for tracking the real object. An articulating arm moveably supports and facilitates repositioning the display and redirecting the data-gathering peripheral devices in a particular direction. A security interlock disables the data-gathering peripherals when the articulating arm is moved beyond a pre-established threshold, thereby preventing the data-gathering peripherals from gathering data of a real environment outside of a limit of the field of view as determined by the pre-established threshold. The security interlock may include an adjustable contact switch which adjustably defines the pre-established threshold in at least one direction.
Runyan, Paul Ryan↗
Neural Networks for Prediction of Complex Chemistry in Water Treatment Process Optimization
Water chemistry plays a critical role in the design and operation of water treatment processes. Detailed chemistry modeling tools use a combination of advanced thermodynamic models and extensive databases to predict phase equilibria and reaction phenomena. The complexity and formulation of these models preclude their direct integration in equation-oriented modeling platforms, making it difficult to use their capabilities for rigorous water treatment process optimization. Neural networks (NN) can provide a pathway for integrating the predictive capability of chemistry software into equation-oriented models and enable optimization of complex water treatment processes across a broad range of conditions and process designs. Herein, we assess how NN architecture and training data impact their accuracy and use in equation-oriented water treatment models. We generate training data using PhreeqC software and determine how data generation and sample size impact the accuracy of trained NNs. The effect of NN architecture on optimization is evaluated by optimizing hypothetical black-box desalination processes using a range of feed compositions from USGS brackish water data set, tracking the number of successful optimizations, and testing the impact of initial guess on the final solution. Our results clearly demonstrate that data generation and architecture impact NN accuracy and viability for use in equation-oriented optimization problems.
Dudchenko, Alexander V↗
An Autonomous MCP Bridge to Rucio: Enhancing Data Management Accessibility for High Energy Physics
The Rucio Data Management System [1] is an important tool used by High Energy Physics experiments, including those at Fermi National Accelerator Laboratory, to store and manage exabyte-scale scientific datasets. Despite its central role in coordinating data across globally distributed storage sites, Rucio's command line interface (CLI) presents a steep learning curve, and makes it difficult for scientists to navigate through. To solve this issue, a containerized Model Context Protocol (MCP) [2] server was built that connects Large Language Models directly to Rucio, allowing AI agents to handle data tasks by using simple, natural language rather than memorized terminal commands. The core engineering focus of this project was moving the server away from slow terminal commands that require text parsing and replacing them with a native Python Client API toolset and a planned REST API framework. Moving to the Python API handles data operations directly in memory, which helps clear up formatting errors, provides the AI with clean, structured JSON data and speeds up tool execution. To prove that the system actually works, a benchmarking pipeline was also built with various questions to test the AI across four different model configurations. The questions included finding data scopes, tracking down specific datasets, and checking replication rules. Through benchmarking, early runs showed that with raw terminal text, the model would get confused and stuck, whereas switching to the Python API to feed the AI clean, structured data yielded massive improvement. By creating an intelligent and autonomous bridge to a storage network, this project shows how AI can be implemented in scientific data management, which ultimately helps scientists at Fermilab spend less time sorting through data and more time focusing on their experiments and analysis.
Akella, Kashyap [William Rainey Harper Coll.]↗
Observed Variability in Convective Cell Characteristics and Near-Storm Environments across the Sea- and Bay-Breeze Fronts in Southeast Texas
Abstract During the DOE Atmospheric Radiation Measurement (ARM) Tracking Aerosol Convection Interactions Experiment (TRACER) IOP spanning June–September 2022, two fixed ARM sites and a mobile team concurrently sampled the airmass heterogeneity across sea- and bay-breeze fronts around the greater Houston metropolitan region. Here, we quantify the spatiotemporal variability between maritime (coastal/bay side of breeze fronts) and continental (inland side of breeze fronts) air masses over 15 IOP days characterized by strong sea-breeze forcing. We analyze environmental profile data from 177 radiosondes and use S- and C-band radar data to track and quantify the variability in attributes of more than 2300 shallow and transitioning cells across different air masses. The composite analysis of environmental profiles indicates that during the early afternoon, the sea-breeze maritime air mass exhibits lower convective available potential energy (CAPE) than the bay-breeze maritime air mass. As the sea breeze advances inland with time, CAPE within the maritime air mass exceeds that of the continental air mass to the north of the breeze fronts. In general, maritime cells have a larger mean composite reflectivity and cell widths than continental cells; however, the response varies between shallow and transitioning cells. Mean composite 20-dB Z echo-top heights, however, are similar across air masses for both shallow and transitioning cells. The continental and maritime inflow air mass for transitioning cells has significantly different mean values for mixed-layer entrainment CAPE, lifted condensation level, level of free condensation, boundary layer depth, and diluted equilibrium level. For shallow cells, only total precipitable water shows a significant difference. Significance Statement The greater Houston metropolitan area is a natural laboratory for understanding the individual impacts of background meteorology and aerosols on convective clouds. Due to its proximity to the Gulf Coast and Galveston Bay, the Houston region experiences a diurnal precipitation cycle in the summer, driven by convection triggered from sea- and bay-breeze fronts. These fronts act as a boundary between air masses with distinct thermodynamic and environmental characteristics. Convergence along these fronts and interactions between storm outflow and the fronts facilitate convection initiation in different mesoscale air masses. This study quantifies the heterogeneity among these air masses while investigating their influence on cloud microphysics. We find that the effect of airmass heterogeneity is more pronounced for the bulk microphysical properties in shallow clouds.
Sharma, Milind↗
Computing material volume fractions on a superimposed mesh as applied to Monte Carlo particle transport simulations
Here, we present a newly implemented ray tracing algorithm in OpenMC for efficiently computing material volume fractions on superimposed meshes in complex geometries. By firing rays along each coordinate direction through the geometry, the approach accumulates track-length data in each mesh element, thereby determining the fractional composition of each material. Scaling studies on three different models—a random tetrahedra configuration, the Frascati Neutron Generator ITER dose rate benchmark, and a stellarator design—show excellent parallel performance, with nearly linear speedup on modern multi-threaded and distributed-memory systems. An analysis of the residual error relative to high-resolution reference solutions demonstrated that under optimal conditions it decreases as 1/R, where R is the number of rays fired, making it straightforward to achieve user-prescribed accuracy. This new functionality enables practical, mesh-based approaches for detailed nuclear analyses in production Monte Carlo workflows without resorting to expensive, fully conformal or unstructured meshing.
Monte Carlo↗
One-shot omnidirectional pressure integration through matrix inversion
In this work, we present a method to perform 2D and 3D omnidirectional pressure integration from velocity measurements with a single-iteration matrix inversion approach. This work builds upon our previous work, where the rotating parallel ray approach was extended to the limit of infinite rays by taking continuous projection integrals of the ray paths and recasting the problem as an iterative matrix inversion problem. This iterative matrix equation is now 'fast-forwarded' to the 'infinity' iteration, leading to a different matrix equation that can be solved in a single step, thereby presenting the same computational complexity as the Poisson equation. We observe computational speedups of ~10 6 when compared to brute-force omnidirectional integration methods, enabling the treatment of grids of ~10 9 points and potentially even larger in a desktop setup at the time of publication. Further examination of the boundary conditions of our one-shot method shows that omnidirectional pressure integration implements a boundary condition where the boundary points are treated as interior points to the extent that information is available. Finally, we show how the method can be extended from the regular grids typical of particle image velocimetry to the unstructured meshes characteristic of particle tracking velocimetry data.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Continuous surface-to-distributed acoustic sensor snapshots explain reactivation of individual natural fractures during an unconventional reservoir stimulation
ABSTRACT Fiber-optic sensing technologies allow petroleum engineering teams to detect hydraulic fracture interaction with boreholes during unconventional reservoir stimulation. In combination with high-repeatability seismic sources, the same distributed acoustic sensors (DASs) enable vertical seismic profiling (VSP) of the fracture evolution away from the boreholes. We discovered clear signatures of seismic scattering on activated fractures during nine days of continuous seismic monitoring of the fracturing stages at the Austin Chalk/Eagle Ford Field Laboratory. The present study applies a novel approach for quantitative analysis of the scattering events in terms of the evolution of the geometry and elastic stiffness of individual fractures. Our characterization strategy sequentially refines the fracture models: from a stack of 1D soft layers to 3D rectangular inclusions. First, we estimate the number of fracture locations and reflectivity using a modified sparse-spike deconvolution of the stacked VSP traces. The fracture set consists of five fractures spaced by 15–30 m with a reflectivity of approximately 1%. Then, we develop a scattering integral method to refine these estimates along with an inversion of the fracture top and bottom for each monitoring vintage. We find that, initially, some of the fractures are located above the monitoring fiber with the height of approximately 100 m. Then we integrate the seismic interpretation with the low-frequency DAS and pressure and microseismic monitoring to reconstruct the activation process of the fractures. Most likely, some of the natural fractures slowly grew downward to the monitoring fiber as a result of fluid injections in the stimulated well. This led to bright strain anomalies but did not trigger seismicity. The top of the fractures remained almost constant and were limited by a lithologic boundary/stress barrier. To our knowledge, this is the first time VSP data enabled tracking of the fracture evolution with such high spatial and temporal resolution, which was previously only available for crosswell surveys and at a much smaller scale.
Glubokovskikh, Stanislav↗
Search for Joint Multimessenger Signals from Potential Galactic Cosmic-Ray Accelerators with HAWC and IceCube
The origin of high-energy galactic cosmic rays is yet to be understood, but some galactic cosmic-ray accelerators can accelerate cosmic rays up to PeV energies. The high-energy cosmic rays are expected to interact with the surrounding material or radiation, resulting in the production of gamma-rays and neutrinos. To optimize for the detection of such associated production of gamma-rays and neutrinos for a given source morphology and spectrum, a multimessenger analysis that combines gamma-rays and neutrinos is required. In this study, we use the Multi-Mission Maximum Likelihood framework with IceCube Maximum Likelihood Analysis software and HAWC Accelerated Likelihood to search for a correlation between 22 known gamma-ray sources from the third HAWC gamma-ray catalog and 14 yr of IceCube track-like data. No significant neutrino emission from the direction of the HAWC sources was found. We report the best-fit gamma-ray model and 90% CL neutrino flux limit from the 22 sources. From the neutrino flux limit, we conclude that, for five of the sources, the gamma-ray emission observed by HAWC cannot be produced purely from hadronic interactions. We report the limit for the fraction of gamma-rays produced by hadronic interactions for these five sources.
79 ASTRONOMY AND ASTROPHYSICS↗
The State of the U.S. Voluntary Power Market (2024 Data)
NLR has tracked the voluntary power market since its inception in the 1990s to help corporate purchasers, utilities, and others selling renewable energy products understand available renewable options and move renewable energy forward. As a part of this report we publish a slide deck, a datebook, a Top 10 utilities and CCA list, and the report.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Energy Technology Commercialization and Entrepreneurship: Insights From the U.S. Department of Energy's Office of Technology Transitions 2024 Energy Technology University Prize Faculty Track
The Energy Technology University Prize (EnergyTech UP) was established in 2022 with the goal of challenging student teams to develop impactful business plans for energy technologies of their choosing. EnergyTech UP is part of the The American Made Challenges program portfolio, funded by the U.S. Department of Energy. EnergyTech UP is specifically funded by the Department of Energy's Office of Technology Transfer (OTT) and is administrated by staff at the National Renewable Energy Laboratory (NREL). Since its inception, the annual prize has attracted applications from approximately 1,948 students across nearly every U.S. state and territory, awarding over $1 million in funds to student teams. In 2024, the prize expanded to include a Faculty Track, which invited faculty members from degree-granting institutions across the U.S. to design entrepreneurship-based curricula or educational activities. This new track aims to foster innovation in energy entrepreneurship education by leveraging the expertise of faculty to create robust, practical, and inclusive learning experiences. This report summarizes the themes, strategies and impacts identified in proposals from EnergyTech UP's inaugural Faculty Track. Our primary data for organizing insights are the Faculty Track applications themselves. This evaluation of 2024 EnergyTech UP Faculty Track applications aims to support future EnergyTech UP Faculty Track applicants as well as others who are interested in promoting, developing, and/or implementing educational activities that focus on energy commercialization and entrepreneurship at their institutions. By presenting insights from 2024 entries to the Faculty Track, we hope to contribute to a growing inventory of open-source curriculum development resources and provide materials to facilitate the growth of similar programs at a variety of collegiate institutions.
adoption readiness level↗
CoCoMET v1.0: a unified open-source toolkit for atmospheric object tracking and analysis
Advances in performance and analysis capabilities have accelerated the development of object tracking algorithms for atmospheric research. This has resulted in a growing number of studies using Lagrangian tracking techniques to analyze the evolution of atmospheric phenomena and the underlying processes. However, the increasing complexity and variety of tracking algorithms present a steep learning curve for new users and make it difficult for existing users to compare algorithm performance. We introduce CoCoMET (Community Cloud Model Evaluation Toolkit), an open-source toolkit that addresses these issues. CoCoMET simplifies the process of running multiple tracking algorithms simultaneously and analyzing objects in both model and observational datasets by specifying parameters in a single configuration file. It standardizes input data from different sources into a consistent format and unifies the tracking output across algorithms. CoCoMET enhances the functionality of existing tracking methods by calculating additional properties such as cell growth and dissipation rates, perimeter, surface area, convexity, and irregularity. In addition, CoCoMET includes a novel method for identifying mergers and splits in 2D and 3D tracks and supports the integration of Eulerian/stationary datasets external to the tracking data for process studies. Its potential utility is demonstrated through examples of model intercomparison, model evaluation against observations, and comparisons between tracking algorithms. Designed for open-source environments, CoCoMET will continue to expand with future releases, incorporating more input data types and tracking algorithms.
54 ENVIRONMENTAL SCIENCES↗
Adapt: A Weather Radar Data Analysis and Nowcasting Platform for Informed Adaptive Scanning
SF-26-021 Adapt is a data processing platform for real-time data analysis, short term prediction of targets convective cells and tracking for archived data. It provides tools for downloading, processing, segmenting, projecting, analyzing, and visualizing storm cell data from weather radar. The pipeline includes cell detection, motion estimation using optical flow, cell property extraction, and persistence to NetCDF and SQLite/Parquet for guiding adaptive scanning.
Raut, Bhupendra Ashokrao [Argonne National Laborat↗
Object detection with deep learning for rare event search in the GADGET II TPC
In the pursuit of identifying rare two-particle events within the GADGET II Time Projection Chamber (TPC), this paper presents a comprehensive approach for leveraging Convolutional Neural Networks (CNNs) and various data processing methods. To address the inherent complexities of 3D TPC track reconstructions, the data is expressed in 2D projections and 1D quantities. This approach capitalizes on the diverse data modalities of the TPC, allowing for the efficient representation of the distinct features of the 3D events, with no loss in topology uniqueness. Additionally, it leverages the computational efficiency of 2D CNNs and benefits from the extensive availability of pre-trained models. Given the scarcity of real training data for the rare events of interest, simulated events are used to train the models to detect real events. To account for potential distribution shifts when predominantly depending on simulations, significant perturbations are embedded within the simulations. This produces a broad parameter space that works to account for potential physics parameter and detector response variations and uncertainties. These parameter-varied simulations are used to train sensitive 2D CNN object detectors. When combined with 1D histogram peak detection algorithms, this multi-modal detection framework is highly adept at identifying rare, two-particle events in data taken during experiment 21072 at the Facility for Rare Isotope Beams (FRIB), demonstrating a 100% recall for events of interest. Here, we present the methods and outcomes of our investigation and discuss the potential future applications of these techniques.
Convolutional neural network↗
Bioenergy Feedstock Library Annual Summary Report 2024
The Bioenergy Feedstock Library (BFL), part of the Biomass Feedstock National User Facility (BFNUF) located at Idaho National Laboratory (INL), is a physical sample repository and a web-accessible electronic database. The BFL stores physical and chemical characteristics of biomass and waste carbon sources for energy use, as well as samples generated from U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO) and U.S. Department of Agriculture-funded projects. The objective of this Bioenergy Feedstock Library Annual Summary Report for 2024, similar to the 2023 Annual Summary Report , is to focus on the updates to: (1) publicly available analytical data and equipment tracked through the BFNUF, (2) significant increases in the physical samples available for request, (3) sample and data archival progress from recent BETO-funded projects, and (4) publicly available data sets created upon request from BETO, INL projects, or outside entities compared to the previous annual summary reports. This report highlights key statistics and available data and information important for INL, BFL users, academics, and industry.
09 BIOMASS FUELS↗
Mechanisms Behind the Long‐Distance Diurnal Offshore Precipitation Propagation in Northwestern South America
Abstract Northwestern South America (NWSA) is the rainiest region on Earth, with diurnal precipitation exhibiting extensive westward offshore propagation of up to about 1,200 km in boreal spring (March‐May). The diurnal offshore precipitation propagation begins slowly (3–10 m s −1 ) near the coast of NWSA (<200 km) but accelerates significantly (∼20 m s −1 ) and shows an afternoon enhancement far from the coast (>400 km). However, the driving mechanisms behind this long‐distance precipitation propagation remain unclear. Using a new cloud tracking and classification data set, we found that mesoscale convective systems (MCSs) are the dominant precipitation contributors in the offshore region of NWSA. Cloud tracking shows that the long‐distance propagation and the afternoon enhancement of diurnal precipitation primarily originate from MCSs initiated in the early morning, either over open oceans or from the coast of Central America. Composite tendency analysis shows that MCSs initiated near the coast of Central America have significant upward cooling and moistening signals starting from the surface before initiation. Further analysis of surface diurnal perturbation fields indicates that the land breeze is the primary driving mechanism for MCS initiation. Conversely, for MCSs initiated over open oceans, a significant downward cooling signal from 400 hPa is observed ∼7 hr before initiation, corresponding to the passage of diurnal gravity waves emitted from the Andes. Additionally, our findings highlight the critical role of lower and mid‐level moisture conditions in MCS initiation, alongside the influence of gravity waves.
Hu, Jingyi [Department of Meteorology and Atmosphe↗