SEARCH · Engineering Papers

Results for “future”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Study of deeply virtual Compton scattering at the future electron-ion collider

This study presents the impact of future measurements of deeply virtual Compton scattering (DVCS) with the ePIC detector at the electron-ion collider (EIC), currently under construction at Brookhaven National Laboratory. The considered process is sensitive to generalized parton distributions (GPDs), the understanding of which is a cornerstone of the EIC physics program. Our study marks a milestone in the preparation of DVCS measurements at EIC and provides a reference point for future analyses. In addition to presenting distributions of basic kinematic variables obtained with the latest ePIC design and simulation software, we examine the impact of future measurements on the understanding of nucleon tomography and DVCS Compton form factors, which are directly linked to GPDs. We also assess the impact of radiative corrections and background contribution arising from exclusive π 0 production.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Testing lepton flavor universality at future Z factories

As one of the hypothetical principles in the Standard Model (SM), lepton flavor universality (LFU) should be tested with a precision as high as possible such that the physics violating this principle can be fully examined. The run of a Z factory at a future e + e − collider, such as the Circular Electron-Positron Collider or Future Circular Collider (electron/positron), provides a great opportunity to perform this task because of the large statistics and high reconstruction efficiencies for b hadrons at the Z pole. In this paper, we present a systematic study on the LFU test in future Z factories. The goal is threefold. First, we study the sensitivities of measuring the LFU-violating observables of b → c τ ν , i.e., R J / ψ , R D s , R D s * , and R Λ c , where τ decays muonically. For this purpose, we develop the strategies for event reconstruction, based on the track information significantly. Second, we explore the sensitivity robustness against detector performance and its potential improvement with the message of event shape or beyond the b -hadron decays. A picture is drawn on the variation of analysis sensitivities with the detector tracking resolution and soft photon detectability, and the impact of Fox-Wolfram moments is studied on the measurement of relevant flavor events. Finally, we interpret the projected sensitivities in the SM effective field theory, by combining the LFU tests of b → c τ ν and the measurements of b → s τ + τ − and b → s ν ¯ ν . We show that the limits on the LFU-violating energy scale can be pushed up to ∼ O ( 10 ) TeV for ≲ O ( 1 ) Wilson coefficients at Tera- Z . Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2024

Our Renewable Energy Future: The Remarkable Story of How Renewable Energy Will Become the Basis for Our Lives

Our Renewable Energy Future delves into the clean energy technology evolution and where our energy system is going. While the book's foundation is technology innovation, it brings a unique perspective that technology alone is not what has brought about the explosive growth of renewable energy and offers fresh insights into how technology, economics, social dynamics, policy, and geopolitics are forces affecting our energy future. This book is a culmination of Dr Arent's lifelong passion for energy, sustainable development, and renewable energy technology. It covers the journey of evolving technology, economics, political economy and geopolitics of clean energy over the last 40 years and provides insights for the coming decades. From a technology perspective, the book traces the arc of recent innovations and synthesizes innovations across multiple interacting perspectives into a description of Our Renewable Energy Future.

ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

How Frequent Will the Rarest Daily Rainfall Records of Hurricane Ida’s Remnants Be in the Future?

Abstract Gaining continued insights into the impact of global warming on the occurrence of hurricane-associated intense record downpours is essential for building climate resilient communities. This study investigates projected future changes in extreme rainfall over the Northeast United States, as represented by extreme daily amounts during Hurricane Ida in 2021. We used historical control simulations of Weather Research and Forecasting (WRF) Model generated from 40 years of weather events (1980–2014, 12 km) forced by the fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis. These simulations are thermodynamically modified (2060–2100) via an imposed warming for the high-emission scenario of shared socioeconomic pathway (SSP585) from a range of general circulation models. Ground observations from the Global Historical Climatology Network (1950–2014) and WRF simulations (historical, 1980–2014, and future, 2060–2100) are integrated into a nonstationary generalized extreme value (GEV) framework to assess the frequency of Ida’s heaviest daily rain rates under the SSP585 scenario. Results show that Ida’s daily maximum rainfall recorded at different observation locations was higher than the single highest September daily maximum observed (1950–2014) for 5 out of 17 stations (∼30% of the stations). Ida-like extreme daily rain rates are projected to be, on average, more than 2 times more likely to occur at the end of the century in the simulations (with some regions as high as 5 times). This work demonstrates that integrating a high-resolution atmospheric model’s present-day and thermodynamically modified future simulations along with ground observations, within a nonstationary statistical framework, is crucial for understanding changing characteristics of extreme weather events. Significance Statement Daily scale extreme precipitation is expected to become more frequent and severe, as evidenced by observations and model simulations. While it is important to investigate how these intensifying heavy rainfall events affect current engineering standards, fewer studies have contextualized how warming impacts the most extreme rainfall from a single storm event relative to historical heavy downpours. In this study, we focused on the daily extreme rainfall associated with the extratropical transition of Hurricane Ida (2021), particularly over the northeastern United States—some of which exceeded the commonly used hydrologic design criteria for a 100-yr storm. Using a high-resolution atmospheric model simulation, we investigated how continued warming may influence the frequency of such daily rain rates. Under a high-emission scenario, these events are projected to become up to 5 times more likely at the end of the twenty-first century.

Dollan, Ishrat J↗

DOE OSTI2026

Carbon ore, rare earth, and critical minerals (core-cm) assessment of san juan and raton coal basins: what is the future potential of coal in the san juan and raton basins, new mexico and colorado

This report summarizes results from the CORE-CM project in the San Juan and Raton basins of New Mexico and Colorado and ultimately begins to address what is the future of coal in the San Juan and Raton basins. The objective of this project was to determine the nonfuel carbon-based products (CBP), critical minerals, and rare earth elements (REE) potential in coal and related stratigraphic units in the San Juan and Raton basins and adjacent areas. This project delineated favorable geologic terrains and priority areas containing potential CBP, critical minerals, and REE deposits for the DOE mandate, which also is a priority of the NMBGMR and state of New Mexico. This project is important to the state because CBP, critical minerals, and REE resources must be identified before land use decisions are made by government officials. Future mining of CBP, critical minerals, and REE will directly benefit the economy of New Mexico. Furthermore, it is crucial to re-establish a domestic source of critical minerals and REE in the U.S. to help secure the nation’s clean energy future, to reduce the vulnerability of the U.S. to material shortages related to national defense, and to maintain our global technical and economic competitiveness. In addition to these benefits, students at New Mexico Institute of Mining and Technology (NMT) and San

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2026

Future Generation High Performance Computing Center (FG-HPCC): RFI Technical Considerations

Lawrence Livermore National Security, LLC (LLNS) is interested in receiving information about technologies that could be available in the 2029-2030 timeframe that may serve to enable the vision for a Future Generation High Performance Computing (HPC) Center (FG-HPCC) described in this document. The future HPC Center vision has been conceived to meet the future mission needs of the Advanced Simulation and Computing (ASC) Program within the National Nuclear Security Administration (NNSA). LLNS envisions a center composed not of many independent clusters, but of heterogeneous elements accessible to users as a single system. The capabilities will be integrated to create a scalable, flexible, yet tightly coupled computing center capable of integrated HPC, AI, and cloud-like workloads.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Gamma Rays as a Signature of r -process Producing Supernovae: Remnants and Future Galactic Explosions

We consider the question of whether core-collapse supernovae (CCSNe) can produce rapid neutron capture process (r-process) elements and how future MeV gamma-ray observations could address this. Rare types of CCSNe characterized by substantial magnetic fields and rotation, known as magnetorotational supernovae (MR-SNe), are theoretically predicted to produce these elements, although direct observational evidence is lacking. We suggest that this critical question be addressed through the study of some of the 11 CCSN remnants located within 10 kpc, as well as through the detection of gamma-ray emission from a future Galactic supernova. We use a two-dimensional MR-SN model to estimate the expected gamma flux stemming from nuclear decays in the range of a few tens of keV to a few MeV. Our results indicate that an observation of 126 Sn ( 126 Sb) in a remnant stands out as a signature of an r-process-producing supernova. Since the neutron-rich conditions that lead to the production of the r-process could also enhance the production of 60 Fe, the detection of substantial 60 Fe ( 60 Co) would be indicative of favorable conditions for the r-process. In the case of a future supernova explosion, when the evolution of the spectrum is studied over 10 days to a few years, a rich picture emerges. At various epochs, the second peak r-process isotopes such as 125 Sn, 131 I, 132 Te, 132 I, and 140 La produce signals that are not obscured by the gamma emission from explosive burning products and electron–positron annihilation. The weak r-process isotopes 95 Nb, 103 Ru, and 106 Rh also have periods of prominence.

Gamma-rays↗

DOE OSTI2025

A space-time tracking algorithm for high occupancy events at future colliders

We propose to explore the potential advantages of a newclass of tracking algorithms loosely inspired by the Hough transformconcept and where we include the time of arrival of each hit as anadditional coordinate to be treated in the same way as a spatialcoordinate. A remarkable property of this algorithm is that theexecution time is proportional to the total number of hits to beprocessed, making it particularly attractive for high occupancysituations expected at future colliders. The particular structureof the algorithm also lends itself naturally to parallel hardwareimplementations which, combined to its intrinsic flexibility, shouldprovide a powerful tool for triggering at future colliders. To probethe effectiveness of the algorithm, we apply it to a quasi-realisticsimulated environment of a possible future muon collider experimentand report the performance.

Casarsa, Massimo [INFN, Trieste; Royal Inst. Tech.↗

DOE OSTI2024

Future North Atlantic tropical cyclone intensities in thermodynamically modified historical environments

Tropical cyclones (TCs) rank as the deadliest and most financially crippling natural disasters in the United States for the last half-century. It is imperative to assess potential shifts in TC intensity within the paradigm of an evolving climate. In this study, we have modeled the intensities of 620 historical TC events in the North Atlantic Basin using the Risk Analysis Framework for Tropical Cyclones (RAFT)'s deep learning intensity model. By applying a thermodynamic warming signal extrapolated from Global Climate Models, we rerun historical events under eight different future climate scenarios, providing a spectrum of potential TC intensity outcomes. One of the future simulations indicates a staggering 43% increase in the number of major hurricanes, underscoring the critical impact of climate change on TC intensity. Additionally, an interactive dashboard has been created to enable users to explore individual storm simulations and understand the influence of future climate signals on environmental conditions of TC development and resulting TC intensities. This dataset and the user-friendly tool offer invaluable resources for systematic exploration of the discrete effects that changes in the air-sea thermodynamic state have on the intensities of TCs.

Climate Change↗

DOE OSTI2025

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection Description This dataset contains input and output data for the manuscript Mongird, K. et al. (under review) titled "Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection". Input data corresponds to gridded spatial siting attributes that are necessary to conduct a random forest machine learning analysis of siting feature importance. Output data includes SHAP feature analysis outputs, and classification report values. For data on power plant siting results referred to in the manuscript, please refer to the CERF: IM3 Projected Western US Power Plant Locations data download page. The downloadable data includes values for eight different future scenarios for the Western US. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States (see, https://tgw-data.msdlive.org/). These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 Technical Information The dataset includes two sets of data files: (1) CERF gridded siting parameters and (2) Feature analysis outputs and classification reports. All downloadable data is in csv file format. Files with x/y coordinate information use the Albers Equal Area Conic projection (ESRI:102003). 1. CERF Gridded Siting Parameters This directory provides a balanced sample of gridded CERF siting parameters data for eight different scenarios for the Western US through 2055, seven different technologies, and eight timesteps. This data serves as input to the feature analysis. It contains the following parameters. region_name - name of region (i.e., state) sited - binary value representing whether the grid cell received a siting of that technology type (1=True) rcp - binary value representing scenario resource concentration pathway (0 = RCP4.5, 1 = RCP8.5) ssp - binary value representing scenario shared socioeconomic pathway (0 = SSP3, 1 = SSP5) climate - binary value representing cooler (0) or hotter (1) GCM forcing tech_name - generation technology name sited_year - year that values correspond to transmission_cost - cost of transmission interconnection pipeline_cost - cost of natural gas pipeline interconnection interconnection_cost - total interconnection cost (sum of transmission cost and gas pipeline cost) lmp - associated locational marginal value ($/MWh) associated with the grid cell, timestep, scenario, and technology xcoord - x-coordinate of location ycoord - y-coordinate of location 2a. Feature Analysis Output The dataset includes the feature analysis shap output for locational marginal price and interconnection cost. It contains the following parameters. technology - generator technology name scenario - name of scenario feature - name of feature, either locational_marginal_price or interconnection_cost value - the mean of absolute value of SHAP values for given feature 2b. Feature Analysis Classification Report This download includes the classification report associated with each random forest model. The dataset contains the following parameters. technology - generation technology name scenario - name of scenario test - one of precision (the proportion of predicted positives that are actually correct), recall (the proportion of actual positives that were correctly identified), f1-score (the harmonic mean of precision and recall) 0.0 - value of test for classification of 0 (grid cell not chosen for siting) 1.0 - value of test for classification of 1 (grid cell chosen for siting) accuracy - accuracy of model (i.e., fraction of all predictions that were right) macro avg - Simple average of test values for all classes weighted avg - Weighted average of test values for all classes, weighted based on Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗

DOE OSTI2025

Investigating Building Energy Consumption and CO2 Emission in Phoenix Using AutoBEM and Future Typical Meteorological Year (fTMY) Weather Data

This research investigates the energy performance and CO2 emissions of each building stock across the Phoenix metropolitan area using the Automatic Building Energy Modeling (AutoBEM) framework and Model America v2 (MAv2) dataset from Oak Ridge National Laboratory (ORNL). Typical Meteorological Year (TMY) and Future Typical Meteorological Year (fTMY) files were used for AutoBEM simulation. The simulation results from TMY and fTMY were compared. It was found that a projected 10.28% increase in total CO2 emissions and a 9.30% rise in total energy consumption by 2080–2099 relative to current typical conditions. The results highlight the disparities in emissions among different building stocks and the influence of climate change on future energy demand. The findings underscore the necessity of targeted policy interventions and retrofitting strategies (eg. advanced HVAC systems, improved insulation, reflective roofing) to mitigate emissions in high-energy-use and emission-intensed buildings, particularly as climate conditions evolve. This study contributes to the growing understanding of building-sector emissions and their long-term implications under future climate scenarios.

Li, Hang [ORNL] (ORCID:0000000306001920)↗

DOE OSTI2026

Artificial Intelligence for Event Reconstruction and Higgs Physics at CMS and Future Colliders

This dissertation charts a trajectory in which advances in artificial intelligence (AI) play a central role in pushing the high-energy physics frontier, complementing progress driven by higher collision energies and larger colliders. The discovery potential of the LHC and future colliders relies on accurate reconstruction of increasingly complex particle collision events. In the CMS experiment, this task is performed by the particle-flow (PF) algorithm. This dissertation presents the first implementation of a machine-learning-based particle-flow (MLPF) reconstruction in the CMS detector based on transformer architectures. In simulated top quark--antiquark pair (ttbar) events under LHC Run~3 (2023--2024) conditions, MLPF improves jet energy resolution by 10--20\% compared to standard PF for jets with transverse momentum between 30--100\GeV. Runtime performance is evaluated using simulated multijet events, with a median inference time of 20\unit{ms} per event on an NVIDIA L4 GPU, compa red to approximately 110\unit{ms} for standard PF. The MLPF algorithm is also validated on Run~3 collision data, representing the first data-validated ML-based reconstruction pipeline at any LHC experiment. We then extend MLPF toward future electron--positron colliders and introduce the first full-simulation cross-detector transfer learning workflow for PF reconstruction. The model is pre-trained on simulated events from the Compact Linear Collider detector (CLICdet) and fine-tuned on the CLIC-like detector (CLD) proposed for the Future Circular Collider (FCC). This approach achieves up to a 40\% improvement in jet energy resolution over rule-based reconstruction while reducing the required training dataset size by an order of magnitude, demonstrating the potential of AI to accelerate detector development and optimization. This dissertation also demonstrates how modern AI techniques enhance the sensitivity of LHC physics analyses. A CMS search for highly Lorentz-boosted Higgs bosons decaying to \textrm{W} boson pairs is presented, focusing on the single-lepton final state. A dedicated fine-tuning strategy for \ParT yields an approximately 70\% increase in expected sensitivity relative to the baseline model. The analysis uses proton--proton collision data at a center-of-mass energy of \ensuremath{\sqrt{s}=13\TeV} collected by CMS between 2016 and 2018, corresponding to an integrated luminosity of 138\ensuremath{\ \mathrm{fb}^{-1}}. The expected significance of the search is $1.86\sigma$, with an observed signal strength of $-0.19^{+0.48}_{-0.46}$. Finally, explainable AI techniques are applied to the MLPF and \ParticleNet algorithms using layerwise relevance propagation, showing that both models base their predictions on physically meaningful features consistent with our physics intuition. Together, these results demonstrate how advanced AI methods can enhance reconstruction, analysis sensitivity, and interpretability, shaping the next era of experimental parti cle physics.

Mokhtar, Farouk [UC, San Diego]↗

DOE OSTI2024

The Future of Vehicle Grid Integration: Harnessing the Flexibility of EV Charging

This document lays out a shared vision for a beneficial, EV-integrated future where EVs are safely and securely connected, reliably served, and harmonized with the electric grid. It was developed as part of the U.S. Department of Energy’s (DOE) EVGrid Assist initiative. Stakeholder input was gathered through individual and collective conversations, including eight listening sessions with more than 100 participants representing utilities of different sizes and operating structures, manufacturers of vehicles and chargers, national associations, standards organizations, Tribes, fleet managers, consumer advocates, charging network operators, community-based organizations, utility regulators, consultants, vendors, labor, and environmental justice organizations. This document focuses on the future of electric on-road U.S. transportation, specifically the integration of light-duty vehicles (LDV) and medium- and heavy-duty vehicles (MHDV) and their charging infrastructure with the electric grid. However, many of the insights here may apply to electrifying other transportation modes, as well as other distributed energy resources (DER).

EVGrid Assist initiative, EV-integrated future, el↗

DOE OSTI2024

The Future of Vehicle Grid Integration: Harnessing the Flexibility of EV Charging

EVGrid Assist initiative, EV-integrated future, el↗

DOE OSTI2024

Who will be making wave energy? A community-driven design approach toward just and sustainable energy futures in Alaska

Renewable energy transition, once thought of as a singular, large-scale switch in industrial energy generating technologies from fossil fuels to renewables, have gained a broader set of requirements, including the need for justice and environmental protection. While discourse around policy and process has grown to consider these requirements, the focus of design of new renewable energy projects and technologies remains dominantly fixed on maximizing energy production, and renewables are falling short of meeting the requirements of just energy transitions. Meanwhile, in design studies, participatory and co-design approaches which explicitly consider justice in both process and outcomes have long been theorized and applied. However, applications of these approaches to renewable energy project design are under-theorized. In this paper, we explore the effectiveness of the adoption and adaptation of a co-design approach called Community-Driven Design (CDD) in enabling meaningful participation prior to technological maturity and bringing the attention of designers to a more diverse set of energy futures focused on justice and sustainability. Here, we do so by developing and testing the approach with community members in Sitka, AK, USA. We conclude that the approach has the potential to increase meaningful participation. Furthermore, we conclude that through diverse understandings of energy and the emergence of design narratives that engage with the complexity of vulnerability, accountability, and resilience, CDD led designers to consider justice, sustainability, and a diverse set of possible energy futures both within and beyond Sitka.

16 TIDAL AND WAVE POWER↗

DOE OSTI2026

Shadow of the Future: Developing Trust and Software within the Exascale Computing Project

Collaboration and team science are emerging areas of interest in software production. Historically, multi-institutional research collaborations are difficult to initiate and maintain, negatively impacting communication, negotiation, and dialogue between industry, government, and academic researchers. The Exascale Computing Project (ECP), a massive, multi-team, high-stakes initiative, facilitated broader research collaboration under a shared funding structure and extended timeline to support scientific discovery. Here, we conducted interviews with ECP teams, representing a variety of domain specialties, research institutions, and programming backgrounds. Using thematic analysis, we assessed how ECP’s structure created an environment of increased trust among projects and how software shared between teams facilitated sustained collaboration. We found that the expectation of future collaboration, i.e., the shadow of the future, greatly enhanced trust among teams and the quality of scientific software produced. Based on our findings within ECP projects, we connect to the existing literature on trust in software engineering and share recommendations for sustainable multi-institutional collaboration and shared best software practices.

Exascale computing project↗

DOE OSTI2026

An Overview of Potential Future Aviation Energy Carriers

Global jet fuel demand is projected to grow to 165 billion gallons by 2050 and there is growing interest in the aviation industry to understand the capabilities of alternative energy carriers. This report considers several potential energy carriers, reviewing their production potential, operational considerations, and economic implications to inform potential next steps in aeronautics research. The following energy carriers (i.e., fuels) were evaluated in this study for future use in aviation: sustainable aviation fuel (SAF) - both biomass-based and power-to-liquids (PtL) - based SAF - cryogenic hydrogen (LH 2 ) - also known as liquid hydrogen - liquefied natural gas (LNG), liquefied ethane (LE), and Jet X. The barriers and opportunities for the evaluated energy carriers vary. SAF is a commercially available drop-in fuel compatible with existing aircraft and infrastructure. SAF is limited by both higher prices and slow commercialization of multiple pathways using different feedstocks necessary to significantly grow supply. Cryogenic fuels, in contrast, have very different thermal, physical, and chemical properties than SAF and Jet A, requiring the construction of new airport storage and dispensing infrastructure and the design of new aircraft energy systems capable of mitigating heat transfer and boil-off. Cryogenic fuels offer an opportunity to diversify aviation fuel carriers from domestic energy relevant for both energy security and resilience. This means cryogenic fuels are more of a longer-term solution for aircraft but are worth exploring for potential future cost savings and emissions benefits. Jet X refers to non-drop-in liquid hydrocarbon fuels in the early stages of investigation seeking favorable properties. There are substantial barriers to non-drop-in liquid aviation fuels that are not compatible with existing aircraft and fuel infrastructure.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

From optimal observables to machine learning: an effective-field-theory analysis of e + e − → W + W − at future lepton colliders

We apply machine-learning techniques to the effective-field-theory analysis of the e + e − → W + W − processes at future lepton colliders, and demonstrate their advantages in comparison with conventional methods, such as optimal observables. In particular, we show that machine-learning methods are more robust to detector effects and backgrounds, and could in principle produce unbiased results with sufficient Monte Carlo simulation samples that accurately describe experiments. This is crucial for the analyses at future lepton colliders given the outstanding precision of the e + e − → W + W − measurement (~ 10−4 in terms of anomalous triple gauge couplings or even better) that can be reached. Our framework can be generalized to other effective-field-theory analyses, such as the one of e + e − → t t ¯ or similar processes at muon colliders.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗