Results for “Graphical Models” — Page 10

DOE OSTI2021

Building Efficiency Targeting Tool for Energy Retrofits (BETTER) Web Application (BETTER Web App) v1.0

The Building Efficiency Targeting Tool for Energy Retrofits (BETTER) V.1.0 web application identifies cost-saving energy and emissions reductions in buildings and portfolios, without site visits or complex modeling. With minimal data entry, BETTER benchmarks a building's or portfolio's energy use against peers; quantifies energy, cost, and greenhouse gas (GHG) reduction potential; and recommends energy efficiency measures for individual buildings or portfolios, targeting specific energy savings levels. The source code of BETTER's modular, cross-platform analytical engine has previously been disclosed (2019-001) and is available on GitHub and can be adopted, redeveloped, and redistributed freely under an open-source license, allowing users to incorporate BETTER's analytical capabilities into their own software platforms and tools. The BETTER V.1.0 web application, developed with the Django web-framework and the Model-View-Controller (MVC) architecture being disclosed here, provides a graphical user interface (GUI) for any user to view the software tutorials, download and upload a data entry template, configure and run the BETTER analyses, and view the final analytical reports.

Szum, Carolyn↗

DOE OSTI2024

DECOVALEX-2023: Task G, SAFENET Final Report

DECOVALEX Task G deals with fracture mechanics at several scales using a combined approach of experimental work, related model development, benchmarking and experimental analysis. Fig. 0.1 provides a graphical abstract for Task G. The experimental basis for the related mechanical (M), hydro-mechanical (HM), and thermo-mechanical (TM) processes comes from the rock mechanics laboratories of Universities of Freiberg (TUBAF) and Edinburgh, as well as the Korea Institute of Civil Engineering and Building Technology (KICT). The experimental work in the rock laboratories is closely linked to the underground research laboratories (URLs) Reiche Zeche (Germany), KURT (Korea), and Mont Terri (Switzerland). The scientific key questions are related to fracture permeability evolution under THM conditions, anisotropy effects on fracturing processes, and thermal fracture slip, which are being addressed in specific steps of the task. A large variety of numerical methods have been developed and applied for experimental analysis, ranging from continuum to discontinuum approaches. A detailed comparison of mechanical and hydro-mechanical processes is given in the section 3 via the benchmarking exercises. As a result of DECOVALEX-2023 Task G we further improved our understanding and predictability of fracturing processes under THM conditions. This was based on robust numerical simulation methods and in-depth experimental analysis.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

GROWdb US River Systems - Samples

GROW Overview We developed the Genome Resolved Open Watersheds database (GROWdb), which aims to increase genomic sampling and understanding of global river microbiomes. An emphasis of GROWdb is to create a publicly available and ever-expanding microbial genome database that is focused on rivers while being interoperable with databases from other ecosystems. GROWdb is based on a network-of-networks approach to move beyond a small collection of well-studied rivers, towards a spatially distributed, global network of systematic observations. GROWdb represents the first microbial, river-focused resource parsed at various scales from genes to MAGs to community level including expression and potential based measurements that will be of interest to microbiologists, ecologists, geochemists, hydrologists, and modelers. Dataset Acknowledgement GROWdb contains data from various research campaigns, please acknowledge the following data generators, as appropriate: WHONDRS derived genomes or samples - include this statement in your acknowledgements: “This study used data from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) under the River Corridor Science Focus Area (SFA) at the Pacific Northwest National Laboratory (PNNL) that was generated at the U.S. Department of Energy (DOE) Joint Genome Institute User Facility. PNNL is operated by Battelle Memorial Institute for the U.S. DOE under Contract No. DE-AC05-76RL01830. The SFA is supported by the U.S. DOE, Office of Biological and Environmental Research (BER), Environmental System Science (ESS) Program.” Total Samples loaded onto this Narrative: 178 Note: Not all GROW samples may be loaded into KBase Data Availability The data underlying GROWdb are accessible across various platforms to ensure all levels of data structure are widely available. First, all reads and MAGs are publicly hosted on National Center for Biotechnology (NCBI) under Bioproject PRJNA946291. Second, all data related data presented here including MAG annotations, extended data tables, phylogenetic tree files, antibiotic resistance gene database files, and MAG abundance tables are available in Zenodo (link). Beyond the flat database files listed above, our aim for GROWdb was to maximize data use by making the data available in searchable and interactive platforms including the National Microbiome Data Collaborative (NMDC) data portal, the Department of Energy’s Systems Biology Knowledgebase (KBase), and a GROW specific user interface released here, GROWdb Explorer. Each platform provides different ways to interact with GROWdb: NMDC GROWdb formed a pilot project for the NMDC. Specifically, individual GROWdb datasets (metagenomes, metatranscriptomes, etc) are easily accessible and searchable through the NMDC data portal, where they are systematically connected to each other and to a rich suite of sample information and standard analysis results, following Findable, Accessible, Interoperable, and Reusable (FAIR) data practices. KBase GROWdb is publicly available within KBase, including samples (this Narrative), MAGs, and corresponding genome scale metabolic models. Access within KBase allows for immediate access and reuse of data, including comparison to private data using KBase’s 500+ analysis tools. Other linked narratives in KBase: GROW Metagenome Assembled Genomes (MAGs) GROW Metabolic Models GROWdb Explorer GROWdb data is also explorable through a graphical user interface built through the Colorado State University Geospatial Centroid (https://geocentroid.shinyapps.io/GROWdatabase/), allowing users to search and graph microbial and spatial data simultaneously. In summary, this microbial genome resource represents the first publicly available genome collection from rivers and offers data that can be leveraged across microbiome studies. GROWdb is an expanding repository to incorporate and unify global river multi-omic data for the future.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory

X-ray micro-computed tomography (X-ray μCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google’s Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Evolution of the Antarctic Ice Sheet from 2000–2300 and beyond: model sensitivity and uncertainty analysis using MPAS-Albany Land Ice

We present a description of the Antarctic Ice Sheet model configuration submitted to the ISMIP6-Antarctica-2300 experiment using the MPAS-Albany Land Ice model, along with three new sets of simulations: (1) a set of extended simulations to 2500 for three forced experiments and to 2775 for the control experiment; (2) a sensitivity analysis of our model configuration to parameters controlling basal sliding and sub-shelf melt, and to model structural choices including the choice of the energy and stress balances; and (3) a 72-member ensemble run on graphics processing units (GPUs) and analysis of variance to determine the primary sources of uncertainty in our ice-sheet model projections. Our extended simulations predict rapid retreat beginning after 2300 for SSP1-2.6 forcing and after 2500 for present-day (control) forcing, primarily in the Amundsen Sea Embayment. We find that varying the sub-shelf melt parameter between the 5th to 95th percentile values for a mean-Antarctic calibration target results in an up to ∼ ± 40 % change in sea-level contribution relative to our baseline simulations that used the median value. Using a linear basal sliding law reduces sea-level contribution by 51 %–73 % relative to our baseline nonlinear sliding law with an exponent of 1/5. When using basal sliding law exponents of 1/3 and 1/10, the overall difference from our baseline simulations at 2300 is on the order of 10 %. The Amundsen Sea Embayment region displays a strongly non-linear dependence of mass loss on the sliding law exponent, with no discernible relationship between the sliding law exponent and the mass loss by 2300, while the sectors feeding the Ross and Filchner-Ronne ice shelves exhibit more mass loss with a more-plastic sliding law. Our model fidelity sensitivity experiments reveal a 9 %–31 % increase in sea-level contribution when using a depth-integrated stress balance approximation relative to our three-dimensional solver, while using a fixed-in-time temperature field increases sea-level contribution by 14 %–88 % relative to two thermomechanically coupled configurations. Our 72-member ensemble and analysis of variance show that the uncertainty in long-term projections is dominated by the choice of Earth system model forcing and the presence or absence of hydrofracture forcing, rather than uncertainty in sliding and sub-shelf melt parameters.

58 GEOSCIENCES↗

DOE OSTI2025

ERF: Energy Research and Forecasting Model

High performance computing (HPC) architectures have undergone rapid development in recent years. As a result, established software suites face an ever increasing challenge to remain performant on and portable across modern systems. Many of the widely adopted atmospheric modeling codes cannot fully (or in some cases, at all) leverage the acceleration provided by General-Purpose Graphics Processing Units, leaving users of those codes constrained to increasingly limited HPC resources. Energy Research and Forecasting (ERF) is a regional atmospheric modeling code that leverages the latest HPC architectures, whether composed of only Central Processing Units (CPUs) or incorporating GPUs. ERF contains many of the standard discretizations and basic features needed to model general atmospheric dynamics. The modular design of ERF provides a flexible platform for exploring different physics parameterizations and numerical strategies. ERF is built on a state-of-the-art, well-supported, software framework (AMReX) that provides a performance portable interface and ensures ERF's long-term sustainability on next generation computing systems. This paper details the numerical methodology of ERF, presents results for a series of verification/validation cases, and documents ERF's performance on current HPC systems. The roughly 5× speed up of ERF (using GPUs) over Weather Research and Forecasting (CPUs only) for a 3D squall line test case highlights the significance of leveraging GPU acceleration.

17 WIND ENERGY↗

DOE OSTI2021

Hydrogen Plus Other Alternative Fuels Risk Assessment Models (HyRAM+) (Technical Reference Manual V.4)

The HyRAM+ software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen, methane, and propane infrastructure and transportation systems. HyRAM+ is designed to facilitate the use of state-of-the-art science and engineering models to conduct robust, repeatable assessments of safety, hazards, and risk. HyRAM+ includes generic probabilities for equipment failures, probabilistic models for the impact of heat flux on humans and structures, and experimentally validated first-order models of release and flame physics. HyRAM+ integrates deterministic and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing hazards (thermal effects from jet fires, overpressure effects from delayed ignition), and assessing impact on people and structures. HyRAM+ is developed at Sandia National Laboratories to support the development and revision of national and international codes and standards. HyRAM+ is a research software in active development and thus the models and data may change. This report will be updated at appropriate developmental intervals. This document provides a description of the methodology and models contained in HyRAM+ version 4.0. The most significant change for HyRAM+ version 4.0 from HyRAM version 3.1 is the incorporation of other alternative fuels, namely methane (as a proxy for natural gas) and propane into the toolkit. This change necessitated significant changes to the installable graphical user interface as well as changes to the back-end Python models. A second major change is the inclusion of physics models for the overpressure associated with the delayed ignition of an unconfined jet/plume of flammable gas.

02 PETROLEUM↗

DOE OSTI2026

Vidyut3d: A GPU accelerated fluid solver for non-equilibrium plasmas on adaptive grids

We present the numerical methods, programming methodology, verification, and performance assessment of a non-equilibrium plasma fluid solver that can effectively utilize current and upcoming central processing and graphics processing unit (CPU+GPU) architectures, in this work. Our plasma fluid model solves the coupled conservation equations for species transport, electrostatic Poisson and electron temperature on adaptive Cartesian grids. Our solver is written using performance portable adaptive-grid/particle management library, AMReX, and is portable over widely available vendor specific GPU architectures. We present verification of our solver using method of manufactured solutions that indicate formal second order accuracy with central diffusion and fifth-order weighted-essentially-non-oscillatory (WENO) advection scheme. We also verify our solver with published literature on capacitive discharges and atmospheric pressure streamer propagation. We demonstrate the use of our solver on two 3D simulation cases: an atmospheric streamer propagation in Ar-H2 mixtures and a low pressure three-electrode radio frequency reactor. Our performance studies on three different CPU+GPU architectures indicate ~ 150-400X speed-up using AMD and NVIDIA GPUs per time step compared to a single CPU core for a 4 million cell simulation with 15 species.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

merlin , an improved framework for the reconstruction of high-quality genome-scale metabolic models

Abstract Genome-scale metabolic models have been recognised as useful tools for better understanding living organisms’ metabolism. merlin (https://www.merlin-sysbio.org/) is an open-source and user-friendly resource that hastens the models’ reconstruction process, conjugating manual and automatic procedures, while leveraging the user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features have been implemented in merlin, along with deep changes in the software architecture, operational flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates increased the user base, resulting in multiple published works, including genome metabolic (re-)annotations and model reconstructions of multiple (lower and higher) eukaryotes and prokaryotes. merlin version 4.0 is the only tool able to perform template based and de novo draft reconstructions, while achieving competitive performance compared to state-of-the art tools both for well and less-studied organisms.

Capela, João (ORCID:0000000212352922)↗

DOE OSTI2020

Toward Large-Scale Image Segmentation on Summit

Semantic segmentation of images is an important computer vision task that emerges in a variety of application domains such as medical imaging, robotic vision and autonomous vehicles to name a few. While these domain-specific image analysis tasks involve relatively small image sizes (~ 10 2 × 10 2 ), there are many applications that need to train machine learning models on image data with extents that are orders of magnitude larger (~10 4 × 10 4 ). Training deep neural network (DNN) models on large extent images is extremely memory-intensive and often exceeds the memory limitations of a single graphical processing unit, a hardware accelerator of choice for computer vision workloads. Here, an efficient, sample parallel approach to train U-Net models on large extent image data sets is presented. Its advantages and limitations are analyzed and near-linear strong-scaling speedup demonstrated on 256 nodes (1536 GPUs) of the Summit supercomputer. Using a single node of the Summit supercomputer, an early evaluation of a recently released model parallel framework called GPipe is demonstrated to deliver ~ 2X speedup in executing a U-Net model with an order of magnitude larger number of trainable parameters than reported before. Performance bottlenecks for pipelined training of U-Net models are identified and mitigation strategies to improve the speedups are discussed. Together, these results open up the possibility of combining both approaches into a unified scalable pipelined and data parallel algorithm to efficiently train U-Net models with very large receptive fields on data sets of ultra-large extent images.

Seal, Sudip↗

DOE OSTI2025

Empirically-calibrated H100 node power models for accurate AI training energy estimation

Accurately quantifying the energy use of artificial intelligence (AI) training is critical for infrastructure planning, carbon accounting, and sustainable data center operation, but few studies have directly measured the power consumption of production workloads on contemporary hardware. By combining empirical measurements from Brookhaven National Laboratory during AI training on 8-graphics-processing-unit H100 systems with open-source benchmarking data, we develop statistical models relating computational intensity to node-level power consumption. We measure the gap between manufacturer-rated thermal design power (TDP) and actual power demand during AI training. Our analysis reveals that even computationally intensive workloads operate at only 76% of the 10.2 kW TDP rating. Our architecture-specific model, calibrated to floating-point operations, predicts energy consumption with 11.4% mean absolute percentage error, significantly outperforming TDP-based approaches (27%–37% error). We identified distinct power signatures between transformer and convolutional neural network architectures, with transformers showing characteristic fluctuations that may impact grid stability. These results provide a measurement-grounded basis for improving AI training energy estimates, enabling more reliable infrastructure sizing, cost projections, and environmental impact assessments.

Newkirk, Alex C↗

DOE OSTI2020

Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2020 .Net)

To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET.Net provides the user with an easy to use and fully graphical toolbox to perform life cycle analysis simulations of alternative transportation fuels and vehicle technologies in a matter of a few clicks. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. The tool includes the data of both fuel-cycle (fuel production and vehicle operation) and vehicle-cycle (vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.

Wang, Michael↗

DOE OSTI2021

Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2021 .Net)

To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET.Net provides the user with an easy to use and fully graphical toolbox to perform life cycle analysis simulations of alternative transportation fuels and vehicle technologies in a matter of a few clicks. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. The tool includes the data of both fuel-cycle (fuel production and vehicle operation) and vehicle-cycle (vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.

Wang, Michael↗

DOE OSTI2022

Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2022 .Net)

To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET.Net provides the user with an easy to use and fully graphical toolbox to perform life cycle analysis simulations of alternative transportation fuels and vehicle technologies in a matter of a few clicks. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. The tool includes the data of both fuel-cycle (fuel production and vehicle operation) and vehicle-cycle (vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.

Wang, Michael↗

DOE OSTI2023

Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2023 .Net)

To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET.Net provides the user with an easy to use and fully graphical toolbox to perform life cycle analysis simulations of alternative transportation fuels and vehicle technologies in a matter of a few clicks. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. The tool includes the data of both fuel-cycle (fuel production and vehicle operation) and vehicle-cycle (vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.

Wang, Michael↗

DOE OSTI2023

Coupling SCALE with DAKOTA for Axial Burnup Profiles Assessment in Burnup Credit

This paper presents a computational study that demonstrates the application of the SCALE code system in conjunction with the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) for the analysis of key factors influencing the evaluation of burnup credit (BUC) in pressurized water reactors (PWRs). The primary objective of this analysis is to characterize the model by utilizing parameterization, uncertainty quantification, and optimization studies. Using this approach, we can comprehensively assess the system and conduct informed predictive studies. This study highlights the effectiveness of the SCALE code system integrated within the DAKOTA framework in terms of efficiency and capability. With the coupling of the burnup code ORIGAMI with the CSAS or TSUNAMI-3D sequence embedded in a DAKOTA analysis, we can characterize the factors that influence the k eff of PWR 17x17 spent nuclear fuel (SNF) in the GBC-32 computational benchmark cask for the assessment of BUC in criticality safety analysis. The coupling methodology used in this study is not exclusive to BUC analysis. However, the choice to apply this methodology to the BUC problem is particularly significant because of the diverse range of aspects it encompasses in nuclear criticality safety analyses. This problem presents a unique opportunity to explore and address multiple facets of such analyses related to BUC and illustrates the capability of the SCALE code system with DAKOTA. This analysis makes use of historical reference data for the axial burnup profile, where the entire space within the bounds is considered. Both SCALE and DAKOTA are currently integrated in the Nuclear Energy Advanced Modeling Simulation (NEAMS) Workbench code system, which has a user-friendly graphical interface that simplifies the setup of simulations and configuration of input parameters as well as the visualization of simulation results.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Progress Report on Model Development for the Transport of Aerosol through Microchannels

This report summarizes the current progress in the development of a phenomenological model of aerosol transport, deposition, and plugging through microchannels. The purpose is to introduce a generic, reliable numerical model for the prediction of aerosol transport, deposition, and plugging in leak paths while accounting for potential plugging formation, to a user community involving researchers, regulators, and industry. In that regard, a Graphical User Interface (GUI) was generated by integrating the individual MATLAB scripts that make up the model and adding additional features to aid the general understanding of the problem to a user. This report focuses on the model development and features included. Further, predictions from the GUI are compared with experimental data for validation. The strength of the GUI is the ability of a user to plug in basic parameters such as the initial pressure conditions and canister model specifications in order to obtain a first principal approximation of vital information such as the blowdown pressure differential, aerosol penetration, and deposition as a function of time. The user can obtain this without the know-how of the underlying MATLAB scripts and thus enables the model to be readily applied by regulators, industry, and shareholders to reduce the uncertainty in the off-site radiological consequences. The report also lists future tasks under this work scope. This includes ongoing efforts to include additional crack geometries (divergent and divergent-convergent slots) to the model; GUI development and improvement based on feedback from users, and integration of aerosol source term data from Sibling Pin tests into the model as we approach realistic canister stress corrosion cracking–induced aerosol release scenarios.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Search for Higgs boson decays to a Z boson and a photon in proton-proton collisions at $ \sqrt{s} $ = 13 TeV

Results are presented from a search for the Higgs boson decay H → Zγ, where Z → ℓ$^{+}$ℓ$^{−}$ with ℓ = e or μ. The search is performed using a sample of proton-proton (pp) collision data at a center-of-mass energy of 13 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 138 fb$^{−1}$. Events are assigned to mutually exclusive categories, which exploit differences in both event topology and kinematics of distinct Higgs production mechanisms to enhance signal sensitivity. The signal strength μ, defined as the product of the cross section and the branching fraction $ \left[\sigma \left(\textrm{pp}\to \textrm{H}\right)\mathcal{B}\left(\textrm{H}\to \textrm{Z}\upgamma \right)\right] $ relative to the standard model prediction, is extracted from a simultaneous fit to the ℓ$^{+}$ℓ$^{−}$γ invariant mass distributions in all categories and is measured to be μ = 2.4 ± 0.9 for a Higgs boson mass of 125.38 GeV. The statistical significance of the observed excess of events is 2.7 standard deviations. This measurement corresponds to $ \left[\sigma \left(\textrm{pp}\to \textrm{H}\right)\mathcal{B}\left(\textrm{H}\to \textrm{Z}\upgamma \right)\right]=0.21\pm 0.08 $ pb. The observed (expected) upper limit at 95% confidence level on μ is 4.1 (1.8), where the expected limit is calculated under the background-only hypothesis. The ratio of branching fractions $ \mathcal{B}\left(\textrm{H}\to \textrm{Z}\upgamma \right)/\mathcal{B}\left(\textrm{H}\to \upgamma \upgamma \right) $ is measured to be $ {1.5}_{-0.6}^{+0.7} $, which agrees with the standard model prediction of 0.69 ± 0.04 at the 1.5 standard deviation level.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗