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DOE OSTI2016

SAMBA: Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos

A new arbitrary Polynomial Chaos (aPC) method is presented for moderately high-dimensional problems characterised by limited input data availability. The proposed methodology improves the algorithm of aPC and extends the method, that was previously only introduced as tensor product expansion, to moderately high-dimensional stochastic problems. The fundamental idea of aPC is to use the statistical moments of the input random variables to develop the polynomial chaos expansion. This approach provides the possibility to propagate continuous or discrete probability density functions and also histograms (data sets) as long as their moments exist, are finite and the determinant of the moment matrix is strictly positive. For cases with limited data availability, this approach avoids bias and fitting errors caused by wrong assumptions. In this work, an alternative way to calculate the aPC is suggested, which provides the optimal polynomials, Gaussian quadrature collocation points and weights from the moments using only a handful of matrix operations on the Hankel matrix of moments. It can therefore be implemented without requiring prior knowledge about statistical data analysis or a detailed understanding of the mathematics of polynomial chaos expansions. The extension to more input variables suggested in this work, is an anisotropic and adaptive version of Smolyak's algorithm that is solely based on the moments of the input probability distributions. It is referred to as SAMBA (PC), which is short for Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos. It is illustrated that for moderately high-dimensional problems (up to 20 different input variables or histograms) SAMBA can significantly simplify the calculation of sparse Gaussian quadrature rules. SAMBA's efficiency for multivariate functions with regard to data availability is further demonstrated by analysing higher order convergence and accuracy for a set of nonlinear test functions with 2, 5 and 10 different input distributions or histograms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Approximate Bayesian Model Inversion for PDEs with Heterogeneous and State-Dependent Coefficients

We present two approximate Bayesian inference methods for parameter estimation in partial differential equation (PDE) models with space-dependent and state-dependent parameters. We demonstrate that these methods provide accurate and cost-effective alternatives to Markov Chain Monte Carlo simulation. We assume a parameterized Gaussian prior on the unknown functions, and approximate the posterior density by a parameterized multivariate Gaussian density. The parameters of the prior and posterior are estimated from sparse observations of the PDE model's states and the unknown functions themselves by maximizing the evidence lower bound (ELBO), a lower bound on the log marginal likelihood of the observations. The first method, Laplace-EM, employs the expectation maximization algorithm to maximize the ELBO, with a Laplace approximation of the posterior on the E-step, and minimization of a Kullback-Leibler divergence on the M-step. The second method, DSVI-EB, employs the doubly stochastic variational inference (DSVI) algorithm, in which the ELBO is maximized via gradient-based stochastic optimization, with nosiy gradients computed via simple Monte Carlo sampling and Gaussian backpropagation. We apply these methods to identifying diffusion coefficients in linear and nonlinear diffusion equations, and we find that both methods provide accurate estimates of posterior densities and the hyperparameters of Gaussian priors. While the Laplace-EM method is more accurate, it requires computing Hessians of the physics model. The DSVI-EB method is found to be less accurate but only requires gradients of the physics model.

approximate Bayesian inference, model inversion, v↗

DOE OSTI2018

1 H NMR-based metabolomics of antimalarial plant species traditionally used by Vha-Venda people in Limpopo Province, South Africa and isolation of antiplasmodial compounds

The Vha-Venda people living in rural areas of Limpopo Province of South Africa regularly use traditional plant-based medicines to treat malaria. In our earlier publication, twenty indigenous plant species used to treat malaria or its symptoms by Vha-Venda people were evaluated for antiplasmodial activity. The main objective of the current paper was to assess the robustness of NMR-based metabolomics in discriminating classes of secondary compounds that are responsible for the observed antimalarial activity and the isolation of antiplasmodial compounds. Twenty dichloromethane extracts were reconstituted in CDCl 3 , subjected to 1 H NMR-based metabolomic analysis on a Varian 600 MHz spectrometer and the acquired 1 H NMR spectra were then evaluated collectively using multivariate data analysis (MDA). Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures–Discriminant Analysis (OPLS-DA) were used to ‘globally’ discern antiplasmodial profiles. A contribution plot was then generated from the OPLS-DA scoring plot in an attempt to determine the classes of compounds that are responsible for the observed grouping. Further phytochemical analyses were conducted on the lipophilic extracts of Tabernaemontana elegans and Vangueria infausta subsp. infausta. These best candidates were fractionated, purified and their isolated compounds identified based on conventional chromatographic and spectroscopic techniques. The PCA did not separate the acquired profiles according to the detected antiplasmodial bioactivity. Application of a supervised OPLS-DA on the 1 H NMR profiles resulted in a discrimination pattern that could be correlated to the observed antimalarial bioactivity. A contribution plot generated from the OPLS-DA scoring plot illustrated the classes of compounds responsible for the observed grouping. Prominent peaks were observed in the aromatic, sugar-based/N-containing and aliphatic spectral regions of the contribution plot. Two known indole alkaloids were isolated from T. elegans, and identified as tabernaemontanine (IC 50 = 12.0±0.8 µM) and dregamine (IC 50 = 62.0±2.4 µM). Friedelin (IC 50 = 7.20±0.5 µM) and morindolide (IC 50 = 107.1±0.6 µM) were isolated from V. infausta subsp. infausta. This is the first report of the rare iridoid lactone, morindolide's antimalarial activity. While these two compounds have been previously identified, this is the first account of their occurrence in the genus Vangueria. The study illustrated the potential of NMR-based metabolomics in discriminating classes of compounds that may be attributed to antiplasmodial activity. Finally and additionally, the study demonstrated the potential of discovering novel antiplasmodial scaffolds from medicinal plants and the rationale for the bioprospecting antimalarial plant species used by Vha-Venda people.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Ecological Forecasting in Chesapeake Bay: Using a Mechanistic-Empirical Modelling Approach

The Chesapeake Bay Ecological Prediction System (CBEPS) automatically generates daily nowcasts and three-day forecasts of several environmental variables, such as sea-surface temperature and salinity, the concentrations of chlorophyll, nitrate, and dissolved oxygen, and the likelihood of encountering several noxious species, including harmful algal blooms and water-borne pathogens, for the purpose of monitoring the Bay's ecosystem. While the physical and biogeochemical variables are forecast mechanistically using the Regional Ocean Modeling System configured for the Chesapeake Bay, the species predictions are generated using a novel mechanistic empirical approach, whereby real-time output from the coupled physical biogeochemical model drives multivariate empirical habitat models of the target species. The predictions, in the form of digital images, are available via the World Wide Web to interested groups to guide recreational, management, and research activities. Though full validation of the integrated forecasts for all species is still a work in progress, we argue that the mechanistic–empirical approach can be used to generate a wide variety of short-term ecological forecasts, and that it can be applied in any marine system where sufficient data exist to develop empirical habitat models. This paper provides an overview of this system, its predictions, and the approach taken.

Brown, C. W.↗

DOE OSTI2016

Helium sequestration at nanoparticle-matrix interfaces in helium + heavy ion irradiated nanostructured ferritic alloys

Here we irradiated four ferritic alloys with energetic Fe and He ions: one castable nanostructured alloy (CNA) containing Ti-W-Ta-carbides, and three nanostructured ferritic alloys (NFAs). The NFAs were: 9Cr containing Y-Ti-O nanoclusters, and two Fe-12Cr-5Al NFAs containing Y-Zr-O or Y-Hf-O clusters. All four were subjected to simultaneous dual-beam Fe + He ion implantation (650 °C, ~50 dpa, ~15 appm He/dpa), simulating fusion-reactor conditions. Examination using scanning/transmission electron microscopy (STEM) revealed high-number-density helium bubbles of ~8 nm, ~10 21 m -3 (CNA), and of ~3 nm, 10 23 m -3 (NFAs). STEM combined with multivariate statistical analysis data mining suggests that the precipitate-matrix interfaces in all alloys survived ~50 dpa at 650 °C and serve as effective helium trapping sites. All alloys appear viable structural material candidates for fusion or advanced fission energy systems. Finally, among these developmental alloys the NFAs appear to sequester the helium into smaller bubbles and away from the grain boundaries more effectively than the early-generation CNA.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes

The ability of recurrent neural networks (RNN) to model nonlinear dynamics of high dimensional process data has enabled data-driven RNN-based fault detection algorithms. Previous studies have focused on detecting faults by identifying the discrepancies in data distribution between the faulty and normal data, as reflected in prediction errors generated by RNN models. However, in industrial processes, variations in data distribution can also result from changes in normal control setpoints and compensatory control adjustments in response to disturbances, making it hard to differentiate between normal and faulty conditions. This paper proposes a fault detection method utilizing a long short-term memory (LSTM) encoder–decoder structure with regularized hidden dynamics and reversible instance normalization (RevIN) to compactly represent high-dimensional measurements for effective monitoring. During training, the hidden states of the model are regularized to form a low-dimensional latent space representation of the original multivariate time series data. As a result, the prediction errors of the latent states can be used to monitor the abnormal dynamic variations, while the reconstruction errors of the measured variables are used to monitor the abnormal static variations. Furthermore, the proposed indices can reflect operating conditions, even when the distribution of test data changes, which helps distinguish faults from normal adjustments and disturbances that controllers can settle. Here, data from numerical simulation and the Tennessee Eastman process are used to illustrate the effectiveness of the proposed fault detection method.

42 ENGINEERING↗

DOE OSTI2017

Influence of physical activity on tibial bone material properties in laying hens

Laying hens develop a type of osteoporosis that arises from a loss of structural bone, resulting in high incidence of fractures. In this study, a comparison of bone material properties was made for lines of hens created by divergent selection to have high and low bone strength and housed in either individual cages, with restricted mobility, or in an aviary system, with opportunity for increased mobility. Improvement of bone biomechanics in the high line hens and in aviary housing was mainly due to increased bone mass, thicker cortical bone and more medullary bone. However, bone material properties such as cortical and medullary bone mineral composition and crystallinity as well as collagen maturity did not differ between lines. However, bone material properties of birds from the different type of housing were markedly different. The cortical bone in aviary birds had a lower degree of mineralization and bone mineral was less mature and less organized than in caged birds. Here, these differences can be explained by increased bone turnover rates due to the higher physical activity of aviary birds that stimulates bone formation and bone remodeling. Multivariate statistical analyses shows that both cortical and medullary bone contribute to breaking strengthThe cortical thickness was the single most important contributor while its degree of mineralization and porosity had a smaller contribution. Lastly, bone properties had poorer correlations with mechanical properties in cage birds than in aviary birds presumably due to the greater number of structural defects of cortical bone in cage birds.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Association Between Dispatch Complaint and Critical Prehospital Time Intervals in Suspected Stroke 911 Activations in the National Emergency Medical Services Information System, 2012–2016

Emergency Medical Services can help improve stroke outcomes by recognizing stroke symptoms, establishing response priority for 911 calls, and minimizing prehospital delays. This study examines 911 stroke events and evaluates associations between events dispatched as stroke and critical EMS time intervals. Data from the National Emergency Medical Services Information System, 2012 to 2016, were analyzed. Activations from 911 calls with a primary or secondary provider impression of stroke were included for adult patients transported to a hospital destination. Three prehospital time intervals were evaluated: (1) response time (RT) ≤8 min, (2) on-scene time (OST) ≤15 min, and (3) transport time (TT) ≤12 min. Associations between stroke dispatch complaint and prehospital time intervals were assessed using multivariate regression to estimate adjusted risk ratios (ARR) and 95% confidence intervals (CIs). Approximately 37% of stroke dispatch complaints were identified by EMS as a suspected stroke. Compared to stroke events without a stroke dispatch complaint, median OST was shorter for events with a stroke dispatch (16 min vs. 14 min, respectively). In adjusted analyses, events dispatched as stroke were more likely to meet the EMS time benchmark for OST ≤15 min (OST, 1.20 [1.20–1.21]), but not RT or TT (RT, [1.00–1.01]; TT, 0.95 [0.94–0.95]). Furthermore, our results indicate that dispatcher recognition of stroke symptoms reduces the time spent on-scene by EMS personnel. These findings can inform future EMS stroke education and quality improvement efforts to emphasize dispatcher recognition of stroke signs and symptoms, as EMS dispatchers play a crucial role in optimizing the prehospital response.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Feedforward-feedback ammonia control at a water resource recovery facility based on a digital twin with hybrid model

Ammonia-based aeration control (ABAC) at full-scale Water Resource Recovery Facilities (WRRFs) can be challenged by diurnal loading and transport delays. This work addressed these challenges using a hybrid feedforward–feedback controller built on Activated Sludge Model 1 (ASM1), marking the first full-scale deployment to pair a mechanistic feedforward core with data-driven corrections. The objectives were to improve ammonia setpoint tracking, assess performance of the mechanistic model when enhanced with data-driven corrections, and document full-scale operation. The hybrid model incorporates two data-driven components: (1) a Mechanistic Error Forecasting Engine (MEFE), consisting of a multivariate linear regressor and a long short-term memory (LSTM) ensemble. Defying expectations, low-parameter models outperformed more complex alternatives, reducing the mechanistic error by 71%. (2) A Residual Oscillation Forecasting Engine (ROFE), based on Fast Fourier Transform, reduced the remaining error by another 35%. Two proportional–integral (PI) feedback loops further (i) trim the feedforward output and (ii) eliminate residual controller error in the final aerobic zone. In full-scale operation, the controller reduced mean-squared error (MSE) by 94% over the baseline and produced more stable dissolved oxygen (DO) setpoints. Overall, it was proven that layering multi-timescale data-driven models on a mechanistic core can yield reliable ABAC performance at WRRFs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Image-guided intensity-modulated radiotherapy for prostate cancer: Dose constraints for the anterior rectal wall to minimize rectal toxicity

Rectal adverse events (AEs) are a major concern with definitive radiotherapy (RT) treatment for prostate cancer. The anterior rectal wall is at the greatest risk of injury as it lies closest to the target volume and receives the highest dose of RT. This study evaluated the absolute volume of anterior rectal wall receiving a high dose to identify potential ideal dose constraints that can minimize rectal AEs. A total of 111 consecutive patients with Stage T1c to T3a N0 M0 prostate cancer who underwent image-guided intensity-modulated RT at our institution were included. AEs were graded according to the Common Terminology Criteria for Adverse Events, version 4.0. The volume of anterior rectal wall receiving 5 to 80 Gy in 2.5-Gy increments was determined. Multivariable Cox regression models were used to identify cut points in these volumes that led to an increased risk of early and late rectal AEs. Early AEs occurred in most patients (88%); however, relatively few of them (13%) were grade ≥2. At 5 years, the cumulative incidence of late rectal AEs was 37%, with only 5% being grade ≥2. For almost all RT doses, we identified a threshold of irradiated absolute volume of anterior rectal wall above which there was at least a trend toward a significantly higher rate of AEs. Most strikingly, patients with more than 1.29, 0.73, or 0.45 cm{sup 3} of anterior rectal wall exposed to radiation doses of 67.5, 70, or 72.5 Gy, respectively, had a significantly increased risk of late AEs (relative risks [RR]: 2.18 to 2.72; p ≤ 0.041) and of grade ≥ 2 early AEs (RR: 6.36 to 6.48; p = 0.004). Our study provides evidence that definitive image-guided intensity-modulated radiotherapy (IG-IMRT) for prostate cancer is well tolerated and also identifies dose thresholds for the absolute volume of anterior rectal wall above which patients are at greater risk of early and late complications.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2019

Screening of Heritage Data for Improving Toughness of Creep-Resistant Martensitic Steels

Data science methods were used to quantify the effect of alloying additions on the tensile behavior of martensitic steels. The effort was undertaken to exploit the heritage data to establish the next experimental design space for the class of 9–12 wt% Cr steels for the application of turbine rotors with an operating temperature of 650 °C and above. Linear, lasso, and multivariate multiple regression models were utilized to identify which alloying elements contribute towards strength and ductility. Visualization techniques such as t-distributed stochastic neighbor embedding and pair-wire element specific comparisons were utilized to explore information gaps that exist within the data. Our research found that tantalum, recently added to improve the creep rupture lifetime, does not show any effect on tensile properties. All combined, the results suggest that the low tempering temperature has compensated for the low alloying additions in the past, therefore, new experiments are needed to isolate the effects of tempering temperature from those of individual elements.

36 MATERIALS SCIENCE↗

DOE OSTI2023

TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers

Dynamic graph embedding has emerged as a very effective technique for addressing diverse temporal graph analytic tasks (i.e., link prediction, node classification, recommender systems, anomaly detection, and graph generation) in various applications. Such temporal graphs exhibit heterogeneous transient dynamics, varying time intervals, and highly evolving node features throughout their evolution. Hence, incorporating long-range dependencies from the historical graph context plays a crucial role in accurately learning their temporal dynamics. In this paper, we develop a graph embedding model with uncertainty quantification, TransformerG2G, by exploiting the advanced transformer encoder to first learn intermediate node representations from its current state (t) and previous context (over timestamps [t–1,t–l], l is the length of context). Moreover, we employ two projection layers to generate lower-dimensional multivariate Gaussian distributions as each node's latent embedding at timestamp t. We consider diverse benchmarks with varying levels of "novelty" as measured by the TEA (Temporal Edge Appearance) plots. Here, our experiments demonstrate that the proposed TransformerG2G model outperforms conventional multi-step methods and our prior work (DynG2G) in terms of both link prediction accuracy and computational efficiency, especially for high degree of novelty. Furthermore, the learned time-dependent attention weights across multiple graph snapshots reveal the development of an automatic adaptive time stepping enabled by the transformer. Importantly, by examining the attention weights, we can uncover temporal dependencies, identify influential elements, and gain insights into the complex interactions within the graph structure. For example, we identified a strong correlation between attention weights and node degree at the various stages of the graph topology evolution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Improved neutron-gamma discrimination for a 3 He neutron detector using subspace learning methods

3 He gas based neutron linear-position-sensitive detectors (LPSDs) have been applied for many neutron scattering instruments. Traditional Pulse-Height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio on the orders of 10 5 -10 6 . The NGD ratios of 3 He detectors need to be improved for even better scientific results from neutron scattering. Digital Signal Processing (DSP) analyses of waveforms were proposed for obtaining better NGD ratios, based on features extracted from rise-time, pulse amplitude, charge integration, a simplified Wiener filter, and the cross-correlation between individual and template waveforms of neutron and gamma events. Fisher linear discriminant analysis (FLDA) and three multivariate analyses (MVAs) of the features were performed. The NGD ratios are improved by about 10 2 -10 3 times compared with the traditional PHA method. Finally, our results indicate the NGD capabilities of 3 He tube detectors can be significantly improved with subspace-learning based methods, which may result in a reduced data-collection time and better data quality for further data reduction.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

CsI(Tl) pulse shape discrimination with the Belle II electromagnetic calorimeter as a novel method to improve particle identification at electron–positron colliders

Here we describe the implementation and performance of CsI(Tl) pulse shape discrimination for the Belle II electromagnetic calorimeter, representing the first application of CsI(Tl) pulse shape discrimination for particle identification at an electron–positron collider. The pulse shape characterization algorithms applied by the Belle II calorimeter are described. Control samples of $γ, μ^+, π^±, K^±$ and $ρ/ \bar{ρ}$ are used to demonstrate the significant insight into the secondary particle composition of calorimeter clusters that is provided by CsI(Tl) pulse shape discrimination. Comparisons with simulation are presented and provide further validation for newly developed CsI(Tl) scintillation response simulation techniques, which when incorporated with GEANT4 simulations allow the particle dependent scintillation response of CsI(Tl) to be modelled. Comparisons between data and simulation also demonstrate that pulse shape discrimination can be a new tool to identify sources of improvement in the simulation of hadronic interactions in materials. The $K^0_L$ efficiency and photon-as-hadron fake-rate of a multivariate classifier that is trained to use pulse shape discrimination is presented and comparisons are made to a shower-shape based approach. CsI(Tl) pulse shape discrimination is shown to reduce the photon-as-hadron fake-rate by over a factor of 3 at photon energies of 0.2 GeV and over a factor 10 at photon energies of 1 GeV.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Experimental study on kinetic oxidation of graphite IG-110 by steam

Graphite is proposed for use in High-temperature Gas-cooled Reactors (HTGRs) as the fuel matrix, neutron moderator/reflector, and core structural material. One important property of nuclear grade graphite is their resistance to oxidation in high-temperature environment. Extensive investigation has been performed in the literature for graphite oxidation by air. However, available experimental data are still limited for graphite oxidation by steam under conditions comparable to a postulated steam ingress accident in HTGRs. In this study, the oxidation rate of graphite IG-110 by steam was measured at temperatures from 850 to 1100 °C with the steam partial pressure varying from 0.5 to 20.0 kPa and the hydrogen partial pressure varying from 0 to 2.0 kPa. Further analysis confirms the oxidation process in this present study is dominated by the chemical kinetics, which lends credit to the data for being used to develop numerical models. It was observed that the increase of the kinetic oxidation rate with the steam partial pressure tends to become less apparent if the steam partial pressure keeps increasing. In addition, it was found that the partitioning of hydrogen inhibits the graphite-steam reaction process even with the steam partial pressure up to 20.0 kPa. However, this inhibiting effect starts to become saturated when the hydrogen partial pressure exceeds 1.0 kPa. The oxidation rates were fitted to the conventional Langmuir-Hinshelwood (LH) and Boltzmann-enhanced Langmuir-Hinshelwood (BLH) models by a multivariable optimization algorithm. The BLH model exhibits a better accuracy than the LH model within the specified experimental conditions. The predicted oxidation rate using the BLH model shows a mean relative difference of about 24% with the maximum difference of about 55% when compared with our experimental data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Evidence for top quark production in nucleus-nucleus collisions

We report droplets of quark-gluon plasma (QGP), an exotic state of strongly interacting quantum chromodynamics (QCD) matter, are routinely produced in heavy nuclei high-energy collisions. Although the experimental signatures marked a paradigm shift away from expectations of a weakly coupled QGP, a challenge remains as to how the locally deconfined state with a lifetime of a few fm can be resolved. The only colored particle that decays mostly within the QGP is the top quark. Here we demonstrate, for the first time, that top quark decay products are identified, irrespective of whether interacting with the medium (bottom quarks) or not (leptonically decaying W bosons). Using 1.7±0.1 nb -1 of lead-lead (A = 208) collision data recorded by the CMS experiment at a nucleon-nucleon center-of-mass energy of 5.02 TeV, we report evidence of top quark pair ($t\bar{t}$) production. Dilepton final states are selected, and the cross section ($σ_{t\bar{t}}$) is measured from a likelihood fit to a multivariate discriminator using lepton kinematic variables. The $σ_{t\bar{t}}$ measurement is additionally performed considering the jets originating from the hadronization of bottom quarks, which improve the sensitivity to the $t\bar{t}$ signal process. After background subtraction and analysis corrections, the measured $σ_{t\bar{t}}$ is 2.56 ± 0.82(tot) and 2.02 ± 0.69(tot)μb in the two cases, respectively, consistent with predictions from perturbative QCD.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2019

Measurement of the single top quark and antiquark production cross sections in the t channel and their ratio in proton-proton collisions at s = 13 TeV

Measurements of the cross sections for the production of single top quarks and antiquarks in the t channel, and their ratio, are presented for proton-proton collisions at a center-of-mass energy of 13 TeV. The data set used was recorded in 2016 by the CMS detector at the LHC and corresponds to an integrated luminosity of 35.9 fb -1 . Events with one muon or electron are selected, and different categories of jet and b jet multiplicity and multivariate discriminators are applied to separate the signal from the background. The cross sections for the t-channel production of single top quarks and antiquarks are measured to be 130±1(stat)±19(syst)pb and 77±1(stat)±12(syst)pb, respectively, and their ratio is 1.68±0.02(stat)±0.05(syst). The results are in agreement with the predictions from the standard model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Observation of four top quark production in proton-proton collisions at s = 13 TeV

The observation of the production of four top quarks in proton-proton collisions is reported, based on a data sample collected by the CMS experiment at a center-of-mass energy of 13 TeV in 2016–2018 at the CERN LHC and corresponding to an integrated luminosity of 138 fb − 1 . Events with two same-sign, three, or four charged leptons (electrons and muons) and additional jets are analyzed. Compared to previous results in these channels, updated identification techniques for charged leptons and jets originating from the hadronization of b quarks, as well as a revised multivariate analysis strategy to distinguish the signal process from the main backgrounds, lead to an improved expected signal significance of 4.9 standard deviations above the background-only hypothesis. Four top quark production is observed with a significance of 5.6 standard deviations, and its cross section is measured to be 17.7 − 3.5 + 3.7 (stat) − 1.9 + 2.3 (syst) fb , in agreement with the available standard model predictions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

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