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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.

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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability

Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Edge-to-Cloud continuum. Integrated data analysis plays a crucial role in scientific discovery, especially in the current AI era, by enabling Responsible AI development, FAIR, Reproducibility, and User Steering. However, the heterogeneous nature of science poses challenges such as dealing with multiple supporting tools, cross-facility environments, and efficient HPC execution. Building on data observability, adapter system design, and provenance, we propose MIDA: an approach for lightweight runtime Multi-workflow Integrated Data Analysis. MIDA defines data observability strategies and adaptability methods for various parallel systems and machine learning tools. With observability, it intercepts the dataflows in the background without requiring instrumentation while integrating domain, provenance, and telemetry data at runtime into a unified database ready for user steering queries. We conduct experiments showing end-to-end multi-workflow analysis integrating data from Dask and MLFlow in a real distributed deep learning use case for materials science that runs on multiple environments with up to 276 GPUs in parallel. We show near-zero overhead running up to 100,000 tasks on 1,680 CPU cores on the Summit supercomputer.

Santos Souza, Renan↗
DOE OSTI2025

Modeling betatron radiation using particle-in-cell codes for plasma wakefield accelerator diagnostics

The analysis of plasma wakefield acceleration experimental measurements, particularly in the characterization of photons emitted through the betatron radiation mechanism, requires the development of accurate numerical models. These computational models are crucial for supporting modern instrumentation designed to measure the single-shot, double-differential angular-energy radiation spectra resulting from interactions between beams and plasmas. Motivated by the needs of such applications, this paper presents detailed numerical models of betatron radiation generated in beam-plasma acceleration experiments. These models are based on the integration of the Liénard-Wiechert (LW) potentials, applied to computed particle trajectories. The particle trajectories are generated using three distinct methods: first, by tracking particles through idealized fields in the blowout regime of PWFA; second, by obtaining trajectories using the fast quasistatic particle-in-cell (PIC) code quickpic; and third, obtaining trajectories from the fully self-consistent PIC code osiris. To ensure the accuracy and reliability of these models, the paper includes various benchmark tests using analytical expressions, as well as employing the PIC code epoch, which takes an alternative approach by using a Monte Carlo quantum electrodynamics (QED)-based radiation model. Additionally, the paper presents simulations of the expected experimental betatron radiation spectra, taking into account parameters relevant to PWFA and plasma photocathode experiments at the SLAC FACET-II facility.

Yadav, M. [University of California, Los Angeles, ↗
DOE OSTI2022

A Causal Approach to Integrate Component Health Data into System Reliability Models

Two of the challenges of current plant reliability approaches are the ability to integrate plant health data, and to support decision making. Condition based data and diagnostic/prognostic information are in fact not considered into plant reliability models to inform system engineers on the most critical components. Currently, the propagation of quantitative health data from the component to the system level is a challenge given the diverse nature/structure of the data. On the other hand, plant reliability methods (which are typically based on fault-trees or reliability block diagrams) can effectively propagate data from the component to the system level, but values of failure rates or failure probabilities are an approximated integral representation of the past industry-wide operational experience, and it neglects the present component health status (e.g., diagnostic and condition-based data) and health projection (when available from prognostic data). Our first claim is that system reliability models should propagate health information from the component to the system/plant level in order to provide a quantitative snapshot of system/plant health and identify the most critical components. Our second claim is that component health should be informed solely by that specific component current and historical performance data and should not be an approximated integral representation of the past industry-wide operational experience. This paper is directly supporting these two claims by proposing a different approach to perform reliability modeling which relies on available component diagnostic, prognostic and condition-based data to measure component health, and it propagates this information through fault tree models. The propagation of health data from the component to the system level is performed not in terms of probability, but in terms of margins where margin is defined as the “distance” between the present actual status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated to a component performance, a margin-based approach focuses on the cause of an undesired component performance (i.e., component health). Hence, thinking of reliability in terms of margins implies decision making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical components.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Sentinel protist taxa of the McMurdo Dry Valley lakes, Antarctica: a review

High-latitude meromictic lakes such as those in the Antarctic McMurdo Dry Valleys (MDV) harbor aquatic ecosystems dominated by the microbial loop. Within this habitat, which is limited year-round by light and nutrients, protists, or single celled eukaryotes, play outsized roles in the food web as the dominant primary producers and the apex predators. Thus, the MDV lake ecosystem represents an ideal system to study the role of sentinel protist taxa in carbon and nutrient cycling. The perennially ice-covered lakes are part of the McMurdo Long Term Ecological Research (McM LTER; mcmlter.org) established in 1993. In this review we will highlight the diversity and trophic roles of the MDV lake protist community and compare environmental factors driving spatiotemporal patterns in key protist taxa in two lakes within the McM LTER, Lakes Bonney and Fryxell. We will then discuss lessons learned from manipulated experiments on the impact of current and future climate-driven environmental change on sensitive protist taxa. Last, we will integrate knowledge gained from 25 years of lab-controlled experiments on key photosynthetic protists to extend our understanding of the function of these extremophiles within the MDV aquatic food webs. Our research group has studied the distribution and function of the MDV microbial community for nearly two decades, training the next generation of scientists to tackle future problems of these globally significant microbes. This review article will also highlight early career scientists who have contributed to this body of work and represent the future of scientific understanding in the Anthropocene.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Adaptable Monitoring Package Development and Deployment: Lessons Learned for Integrated Instrumentation at Marine Energy Sites

Integrated instrumentation packages are an attractive option for environmental and ecological monitoring at marine energy sites, as they can support a range of sensors in a form factor compact enough for the operational constraints posed by energetic waves and currents. Here we present details of the architecture and performance for one such system—the Adaptable Monitoring Package—which supports active acoustic, passive acoustic, and optical sensing to quantify the physical environment and animal presence at marine energy sites. we describe cabled and autonomous deployments and contrast the relatively limited system capabilities in an autonomous operating mode with more expansive capabilities, including real-time data processing, afforded by shore power or in situ power harvesting from waves. Across these deployments, we describe sensor performance, outcomes for biological target classification algorithms using data from multibeam sonars and optical cameras, and the effectiveness of measures to limit biofouling and corrosion. On the basis of these experiences, we discuss the demonstrated requirements for integrated instrumentation, possible operational concepts for monitoring the environmental and ecological effects of marine energy converters using such systems, and the engineering trade-offs inherent in their development. Overall, we find that integrated instrumentation can provide powerful capabilities for observing rare events, managing the volume of data collected, and mitigating potential bias to marine animal behavior. These capabilities may be as relevant to the broader oceanographic community as they are to the emerging marine energy sector.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

A mathematical approach to using the forgetting curve to evaluate experience and training factors in human reliability analysis

Traditional human reliability analysis (HRA) methods have difficulty dealing with the dynamic nature of factors such as time and rely on static and expert-judgment-based assessments of performance-shaping factors (PSFs) across limited levels. In this study, we introduce a mathematical approach for dynamically evaluating the experience and training PSF. Our proposed method integrates the psychological concept of the “forgetting curve” to evaluate how PSFs are impacted by the number of trainings and the time elapsed since training. To confirm the validity of the model, we provide experimental data fitted by identifying the quantitative relationship between training and human performance. This research enables dynamic and objective assessments, thus reducing reliance on subjective expert judgment and improving the accuracy of HRA.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Enabling integrated AI control on DIII-D: a control system design with state-of-the-art experiments

We present the design and application of a general algorithm for Prediction And Control using MAchiNe learning (PACMAN) in DIII-D. Machine learning (ML)-based predictors and controllers have shown great promise in achieving regimes in which traditional controllers fail, such as tearing mode (TM) free scenarios, ELM-free scenarios and stable advanced tokamak conditions. The architecture presented here was deployed on DIII-D to facilitate the end-to-end implementation of advanced control experiments, from diagnostic processing to final actuation commands. This paper describes the detailed design of the algorithm and explains the motivation behind each design point. We also describe several successful ML control experiments in DIII-D using this algorithm, including a reinforcement learning controller targeting advanced non-inductive plasmas, a wide-pedestal quiescent H-mode ELM predictor, an Alfvén Eigenmode controller, a Model Predictive Control plasma profile controller and a state-machine TM predictor-controller. There is also discussion on guiding principles for real-time ML controller design and implementation.

machine learning↗
DOE OSTI2024

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

CRCNS21 Computational Models of Multisensory Integration by Upper Limb in Humanoids and Amputees

This international collaborative research project between Johns Hopkins University (JHU) and the Technical University of Munich (TUM) investigated how the human brain processes and integrates multiple types of sensory information, such as touch and force, with the goal of improving prosthetic limbs for amputees and advancing sensory capabilities in humanoid robots. The research advanced our understanding of how the brain responds to sensory feedback in upper-limb amputees. Through experiments in which amputees received electrical stimulation while performing phantom hand movements, we demonstrated that sensory feedback activates the cortical sensorimotor and multisensory regions, and that these regions communicate dynamically during stimulation. Experiments with intact-limb participants explored the integration of visual, haptic, and force feedback, as well as in virtual reality motor training, further showing how the brain processes multimodal sensory information. In addition, this research inspired work on examining the reliability of where amputees perceive sensations over time, which contributed to a successful doctoral fellowship for continued investigation. Our collaborators at TUM improved multimodal sensor technology combining tactile and thermal feedback for humanoid robots, demonstrating the feasibility of integrating multiple sensor types into a unified system for detecting and responding to environmental stimuli. The experimental methods and analysis techniques developed across both teams, including functional network analysis and multimodal sensor integration, provide a foundation for future research in prosthetics and robotics. This research benefits the public by generating knowledge about how amputees process restored sensory information. Advances in humanoid sensing contribute to safer human-robot interaction. The project also fostered international collaboration and cross-disciplinary training: one TUM doctoral student spent a summer at JHU working on multimodal sensor integration, while two JHU students traveled to TUM to host workshops on neuromorphic sensory encoding and sensory integration.

42 ENGINEERING↗
DOE OSTI2023

Toward an Autonomous Workflow for Single Crystal Neutron Diffraction

The operation of the neutron facility relies heavily on beamline scientists. Some experiments can take one or two days with experts making decisions along the way. Leveraging the computing power of HPC platforms and AI advances in image analyses, here we demonstrate an autonomous workflow for the single-crystal neutron diffraction experiments. The workflow consists of three components: an inference service that provides real-time AI segmentation on the image stream from the experiments conducted at the neutron facility, a continuous integration service that launches distributed training jobs on Summit to update the AI model on newly collected images, and a frontend web service to display the AI tagged images to the expert. Ultimately, the feedback can be directly fed to the equipment at the edge in deciding the next-step experiment without requiring an expert in the loop. With the analyses of the requirements and benchmarks of the performance for each component, this effort serves as the first step toward an autonomous workflow for real-time experiment steering at ORNL neutron facilities.

Yin, Junqi↗
DOE OSTI2021

Measurement of differential cross sections for Z bosons produced in association with charm jets in pp collisions at $\sqrt{s} =$ 13 TeV

Measurements are presented of differential cross sections for the production of Z bosons in association with at least one jet initiated by a charm quark in pp collisions at $ \sqrt{s} $ = 13 TeV. The data recorded by the CMS experiment at the LHC correspond to an integrated luminosity of 35.9 fb$^{−1}$. The final states contain a pair of electrons or muons that are the decay products of a Z boson, and a jet consistent with being initiated by a charm quark produced in the hard interaction. Differential cross sections as a function of the transverse momentum p$_{T}$ of the Z boson and p$_{T}$ of the charm jet are compared with predictions from Monte Carlo event generators. The inclusive production cross section 405.4 ± 5.6 (stat) ± 24.3 (exp) ± 3.7 (theo) pb, is measured in a fiducial region requiring both leptons to have pseudorapidity |η| < 2.4 and p$_{T}$> 10 GeV, at least one lepton with p$_{T}$> 26 GeV, and a mass of the pair in the range 71–111 GeV, while the charm jet is required to have p$_{T}$> 30 GeV and |η| < 2.4. These are the first measurements of these cross sections in proton-proton collisions at 13 TeV.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Measurement of the double-differential inclusive jet cross section in proton-proton collisions at $\sqrt{s}$ = 5.02 TeV

The inclusive jet cross section is measured as a function of jet transverse momentum pT and rapidity y. The measurement is performed using proton-proton collision data at $\sqrt{s}$ = 5.02 TeV, recorded by the CMS experiment at the LHC, corresponding to an integrated luminosity of 27.4 pb −1 . The jets are reconstructed with the anti-k T algorithm using a distance parameter of R = 0.4, within the rapidity interval |y| < 2, and across the kinematic range 0.06 < p T < 1 TeV. The jet cross section is unfolded from detector to particle level using the determined jet response and resolution. The results are compared to predictions of perturbative quantum chromodynamics, calculated at both next-to-leading order and next-to-next-to-leading order. The predictions are corrected for nonperturbative effects, and presented for a variety of parton distribution functions and choices of the renormalization/factorization scales and the strong coupling α S .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Measurement of the Z boson production cross-section in pp collisions at $ \sqrt{s}$ = 5.02 TeV

The first measurement of the Z boson production cross-section at centre-of-mass energy $\sqrt{s}$ = 5.02 TeV in the forward region is reported, using pp collision data collected by the LHCb experiment in year 2017, corresponding to an integrated luminosity of 100 ± 2 pb –1 . The production cross-section is measured for final-state muons in the pseudorapidity range 2.0 < η < 4.5 with transverse momentum p T > 20 GeV/c. The integrated cross-section is determined to be ${\sigma}_{Z\to \mu +\mu -}=39.6\pm 0.7\left(\textrm{stat}\right)\pm 0.6\left(\textrm{syst}\right)\pm 0.8\left(\textrm{lumi}\right)\textrm{pb}$ for the di-muon invariant mass in the range 60 < M μμ < 120 GeV/c 2 . This result and the differential cross-section results are in good agreement with theoretical predictions at next-to-next-to-leading order in the strong coupling constant. Based on a previous LHCb measurement of the Z boson production cross-section in pPb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV, the nuclear modification factor RpPb is measured for the first time at this energy. The measured values are ${1.2}_{-0.3}^{+0.5}$ (stat) ± 0.1(syst) in the forward region (1.53 < ${y}_{\mu}^{\ast }$ < 4.03) and ${3.6}_{-0.9}^{+1.6}$ (stat) ± 0.2(syst) in the backward region (–4.97 < ${y}_{\mu}^{\ast }$ < –2.47), where ${y}_{\mu}^{\ast }$ represents the muon rapidity in the centre-of-mass frame.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Search for long-lived particles produced in association with a Z boson in proton-proton collisions at $\sqrt{s}$ = 13 TeV

A search for long-lived particles (LLPs) produced in association with a Z boson is presented. The study is performed using data from proton-proton collisions with a center-of-mass energy of 13 TeV recorded by the CMS experiment during 2016–2018, corresponding to an integrated luminosity of 117 fb -1 . The LLPs are assumed to decay to a pair of standard model quarks that are identified as displaced jets within the CMS tracker system. Triggers and selections based on Z boson decays to electron or muon pairs improve the sensitivity to light LLPs (down to 15 GeV). This search provides sensitivity to beyond the standard model scenarios which predict LLPs produced in association with a Z boson. In particular, the results are interpreted in the context of exotic decays of the Higgs boson to a pair of scalar LLPs (H → SS). The Higgs boson decay branching fraction is constrained to values less than 6% for proper decay lengths of 10–100 mm and for LLP masses between 40 and 55 GeV. In the case of low-mass (≈ 15 GeV) scalar particles that subsequently decay to a pair of b quarks, the search is sensitive to branching fractions B(H → SS) < 20% for proper decay lengths of 10–50 mm. The use of associated production with a Z boson increases the sensitivity to low-mass LLPs of this analysis with respect to gluon fusion searches. In the case of 15 GeV scalar LLPs, the improvement corresponds to a factor of 2 at a proper decay length of 30 mm.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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↗
DOE OSTI2023

Search for nonresonant Higgs boson pair production in the four leptons plus twob jets final state in proton-proton collisions at $\sqrt{s}$ = 13 TeV

The first search for nonresonant production of Higgs boson pairs (HH) with one H decaying into four leptons and the other into a pair of b quarks is presented, using proton-proton collisions recorded at a center-of-mass energy of √s = 13 TeV by the CMS experiment. The analyzed data correspond to an integrated luminosity of 138 fb–1. A 95% confidence level upper limit of 32.4 is set on the signal strength modifier μ, defined as the ratio of the observed HH production rate in the HH → ZZ*b$\overline{b}$ → 4lb$\overline{b}$ decay channel to the standard model (SM) expectation. Possible modifications of the H trilinear coupling λHHH with respect to the SM value are investigated. The coupling modifier κ λ , defined as λHHH divided by its SM prediction, is constrained to be within the observed (expected) range –8.8 (–9.8) < κ λ < 13.4 (15.0) at 95% confidence level.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Measurement of the ratio of branching fractions $\mathcal{B}\left({B}_c^{+}\to {B}_s^0{\pi}^{+}\right)/\mathcal{B}\left({B}_c^{+}\to J/\psi {\pi}^{+}\right)$

The ratio of branching fractions of ${B}_c^{+}\to {B}_s^0{\pi}^{+}$ and ${B}_c^{+}\to J/\psi {\pi}^{+}$ decays is measured with proton-proton collision data of a centre-of-mass energy of 13 TeV. The data were collected with the LHCb experiment during 2016–2018, corresponding to an integrated luminosity of 5.4 fb –1 . The ${B}_s^0$ mesons are reconstructed via the decays ${B}_s^0\to J/\psi \phi$ and ${B}_s^0\to {D}_s^{-}{\pi}^{+}$. The ratio of branching fractions is measured to be $\mathcal{B}\left({B}_c^{+}\to {B}_s^0{\pi}^{+}\right)/\mathcal{B}\left({B}_c^{+}\to J/\psi {\pi}^{+}\right)=91\pm 10\pm 8\pm 3$ where the first uncertainty is statistical, the second is systematic and the third is due to the knowledge of the branching fractions of the intermediate state decays.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Azimuthal anisotropy of dijet events in PbPb collisions at $$ \sqrt{s_{\textrm{NN}}} $$ = 5.02 TeV

A bstract The path-length dependent parton energy loss within the dense partonic medium created in lead-lead collisions at a nucleon-nucleon center-of-mass energy of $$ \sqrt{s_{\textrm{NN}}} $$ s NN = 5 . 02 TeV is studied by determining the azimuthal anisotropies for dijets with high transverse momentum. The data were collected by the CMS experiment in 2018 and correspond to an integrated luminosity of 1.69 nb − 1 . For events containing back-to-back jets, correlations in relative azimuthal angle and pseudorapidity ( η ) between jets and hadrons, and between two hadrons, are constructed. The anisotropies are expressed as the Fourier expansion coefficients v n , n = 2–4 of these azimuthal distributions. The dijet v n values are extracted from long-range (1 . 5 < |∆ η | < 2 . 5) components of these correlations, which suppresses the background contributions from jet fragmentation processes. Positive dijet v 2 values are observed which increase from central to more peripheral events, while the v 3 and v 4 values are consistent with zero within experimental uncertainties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗