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NASA NTRS2020

Disruptive Technologies and Their Putative Impacts Upon Society and Aerospace- Entering The Virtual Age

Developments in technology over the recent decades have been extraordinary. They include the IT, bio, nano, and now quantum and energetics technology arenas and their many combinatorial interactions and impacts. In the main, these are at the frontiers of the small and in a combinational, synergistic feeding frenzy with each other. They fall under the broad category of Disruptive Technologies and have greatly altered society. The outlook for the runout of these and other technology developments augers mid-term to later alterations in components of the human existence theorem, including the requirement to work for our living and our physiological makeup and longevity (Ref 1). The IT revolution began in the 1950s with the development of solid-state electronics. The biologics revolution began later in the 1960s and 1970s with DNA and genomics, and the nano revolution in the 1990s with self-forming nano systems and carbon nanotubes. Quantum technology is now developing rapidly, aided by enabling nano systems, and the energetics revolution is providing ever more efficient and less expensive renewable energy sources. The IT revolution has produced improvements of an astounding eleven orders of magnitude in computing speed since the late 1950s. As we shift from silicon to biological, optical, nano, molecular, and atomic computing, improvements of some 4 orders of magnitude are evidently possible from either optical or DNA computing [Refs 2and 3], then there are combinatorials. Then there is quantum computing, under development worldwide for an increasing number of applications and proffering phenomenal capabilities. The current fastest computers are considerably beyond human brain speed. Machine intelligence is developing well after decades of inadequate machine capability, now no longer the case, and a detour into expert systems. Researchers in machine intelligence are now pursuing deep learning approaches using neural nets, which are proving to be extremely useful. Some believe the frontier of potential human-level machine intelligence may be found in biomimetics and brain-emulation approaches. There is even a possibility of “emergence”—i.e., when the machine intelligence is complex enough that it “wakes up,” as when human intelligence emerged via evolution during the million-plus years of the hunter-gatherer epoch [ Ref 4]. In fact, some posit that human intelligence can be improved upon and is only a cul-de-sac of what is conceivable. The IT revolution has produced massive changes in human society and economics—from the Internet, enabling the rapid expansion of knowledgeability (and even what is knowable), to an increasingly pervasive trend of “tele-everything.” The extraordinary compilation, storage, and availability of truly massive amounts of information could, when combined with AI and under the mantra of “big data,” greatly improve many of our technical and commercial processes and their content including elucidating new heuristic governing laws.

Dennis M. Bushnell↗

DOE OSTI2025

Recent Developments in DFTB+, a Software Package for Efficient Atomistic Quantum Mechanical Simulations

DFTB+ is a flexible, open-source software package developed by its community, designed for fast and efficient atomistic quantum mechanical simulations. It employs various methods that approximate density functional theory (DFT), such as density functional-based tight binding (DFTB) and the extended tight binding (xTB) approach allowing simulations of large systems over extended time scales with reasonable accuracy, while being significantly faster than traditional ab initio methods. In recent years, several new extensions of the DFTB method have been developed and implemented in the DFTB+ program package in order to improve the accuracy and generality of the available simulation results. In this paper, we review those enhancements, show several use case examples and discuss the strengths and limitations of its features.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Generative Physics-Informed Neural Network Solving Multi-Scale and Multi-Phase Plasma Chemical Flow Field

Low-temperature plasmas (LTPs) are non-equilibrium systems with near-room-temperature gas and highly energetic electrons. This makes them ideal for delicate applications in biomedicine and semiconductor manufacturing, enabling processes like wound healing, sterilization, etching, and plasma-enhanced chemical vapor deposition without thermal damage. However, LTPs involve complex chemistries, with hundreds of species and thousands of reactions, complicating their diagnosis, prediction, and control. Conventional diagnostics, such as Fourier-transform infrared spectroscopy (FTIR), laser-induced fluorescence (LIF), and optical emission spectroscopy (OES), offer limited species detection, while mass spectrometry (MS) struggles with low-sensitivity species. Additionally, LTP simulations face multi-scale challenges, as macroscopic fluid dynamics and microscopic particle collisions operate on vastly different timescales. To address these issues, we developed an artificial intelligence (AI) based diagnostic system: a generative physics-informed neural network (PINN-Gen) that can predict spatially resolved species concentrations and temperatures in LTPs by integrating experimental data from planar LIF with microscopic plasma chemical kinetics and macroscopic fluid mechanics, including plasma-liquid interactions at the interface between two phases. PINN-Gen solves no equations but checks the errors of physical laws by substituting the output from neural network, and the comparison with the experimental results. Thus, it naturally avoids the multi-scale difficulty of numerical simulations and predicts the results of conventionally unsolvable multi-scale and multi-phase problems. The real-time prediction will be robust due to the physical information used in the training of such a neural network, and only very limited input of condition required due to its generative feature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Measurement of jet substructure in boosted $t\overline{t}$ events with the ATLAS detector using 140 fb -1 of 13 TeV $pp$ collisions

Measurements of the substructure of top-quark jets are presented, using 140 fb -1 of 13 TeV pp collision data recorded with the ATLAS detector at the LHC. Top-quark jets reconstructed with the anti-k t algorithm with a radius parameter R = 1.0 are selected in top-quark pair ($t\overline{t}$) events where one top quark decays semileptonically and the other hadronically, or where both top quarks decay hadronically. The top-quark jets are required to have transverse momentum p T > 350 GeV, yielding large samples of data events with jet p T values between 350 and 600 GeV. One- and two-dimensional differential cross sections for eight substructure variables, defined using only the charged components of the jets, are measured in a particle-level phase space by correcting for the smearing and acceptance effects induced by the detector. The differential cross sections are compared with the predictions of several Monte Carlo simulations in which top-quark pair-production quantum chromodynamic matrix-element calculations at next-to-leading-order precision in the strong coupling constant α S are passed to leading-order parton shower and hadronization generators. The Monte Carlo predictions for measures of the broadness, and also the two-body structure, of the top-quark jets are found to be in good agreement with the measurements, while variables sensitive to the three-body structure of the top-quark jets exhibit some tension with the measured distributions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Identifying environmentally induced calibration changes in cryogenic RF axion detector systems using deep neural networks

The axion is a compelling hypothetical particle that could account for the dark matter in our universe while simultaneously solving the strong CP problem in quantum chromodynamics. The most sensitive axion detection technique demonstrated so far makes use of a high Q cavity immersed in a strong magnetic field, where axions are converted to microwave photons. This is called an axion haloscope and has primarily targeted the 1–10 GHz range. As searches scan up in axion mass, toward the parameter space favored by theoretical predictions, individual cavity sizes decrease in order to achieve higher frequencies. This shrinking cavity volume translates directly to a loss in signal-to-noise, motivating the plan to replace individual cavity detectors with arrays of cavities. When the transition from one to (N) multiple cavities occurs, haloscope searches are anticipated to become much more complicated to operate, requiring N times as many measurements but also the new requirement that N detectors operate in unison, which can be achieved by locking them to a common frequency. To offset this anticipated increase in detector complexity, we aim to develop new tools for diagnosing experiments using neural networks. Current experiments monitor scattering parameters of their receiver for periodically measuring cavity quality factor and coupling. However, off-resonant data remain relatively useless. In this paper, we ask if it is possible that off-resonant information contained in vector network analyzer scans could be used to diagnose equipment failures/anomalies and measure physical conditions (e.g., temperatures and ambient magnetic field). We demonstrate a proof-of-concept that AI techniques can help manage the complexity of an axion haloscope search for operators.

Engel, Andrew W. [Pacific Northwest National Labor↗

DOE OSTI2025

Characterization of the Cherenkov Photon Background for Low-noise Silicon Detectors in Space

Future space observatories that seek to perform imaging and spectroscopy of faint astronomical sources will require ultra-low-noise detectors that are sensitive over a broad wavelength range. Silicon charge-coupled devices (CCDs), such as EMCCDs, skipper CCDs, multi-amplifier sensing CCDs, and single-electron sensitive read out CCDs have demonstrated the ability to detect and measure single photons from X-ray energies to near the silicon band gap (∼1.1 μm), making them candidate technologies for this application. Here, in this context, we study a relatively unexplored source of low-energy background coming from Cherenkov radiation produced by energetic cosmic rays traversing a silicon detector. We present a model for Cherenkov photon production and absorption that is calibrated to laboratory data, and we use this model to characterize the residual background rate for ultra-low-noise silicon detectors in space. We study how the Cherenkov background rate depends on detector thickness, variations in solar activity, and the contribution of heavy cosmic ray species (Z > 2). We find that for thick silicon detectors, such as those required to achieve high quantum efficiency at long wavelengths, the rate of cosmic-ray-induced Cherenkov photon production is comparable to other detector and astrophysical backgrounds. We apply our Cherenkov background model to simulated spectroscopic observations of extra-solar planets, and we find that thick detectors continue to outperform their thinner counterparts at longer wavelengths despite a larger Cherenkov background rate. Furthermore, we find that minimal masking of cosmic-ray tracks continues to maximize the signal-to-noise ratio of very faint sources despite the existence of extended halos of Cherenkov photons.

Astronomical detectors↗

DOE OSTI2026

Measurement of differential t-channel single top (anti)quark production cross-sections at 13 TeV with the ATLAS detector

The production of single top quarks and top antiquarks via the t-channel exchange of a virtual W boson is measured in proton-proton collisions at a centre-of-mass energy of 13 TeV at the Large Hadron Collider. The full Run 2 data sample recorded with the ATLAS detector in the years 2015–2018 is used, corresponding to an integrated luminosity of 140 fb−1. The absolute and normalised production cross-sections are measured differentially as a function of the transverse momentum and absolute rapidity of the top quark and top antiquark. In addition, the ratio of top quark to top antiquark production cross-sections is measured. The measured distributions are compared with next-to-leading-order quantum chromodynamics predictions obtained with different combinations of matrix-element generators, parton-shower programs and proton parton distribution functions, as well as to next-to-next-to-leading-order calculations. Overall, good agreement is observed between the measurements and the theoretical predictions. For most measured distributions, the sensitivity to differences between the predictions is limited by the systematic uncertainties in the measurement. The measured differential distributions are also interpreted in an effective field theory approach to constrain the Wilson-Coefficient CQq3,1$$ {C}_{Qq}^{3,1} $$ associated with a four-quark operator. The interpretation accounts for the effect of the selection efficiency, which is altered significantly by non-zero contributions from CQq3,1$$ {C}_{Qq}^{3,1} $$.

Aad, G↗

DOE OSTI2024

Model-independent determination of the strong-phase difference between D 0 and D ¯ 0 → π + π − π + π − decays

Measurements of the strong-phase difference between D 0 and D ¯ 0 → π + π − π + π − are performed in bins of phase space. The study exploits a sample of quantum-correlated D D ¯ mesons collected by the BESIII experiment in e + e − collisions at a center-of-mass energy of 3.773 GeV, corresponding to an integrated luminosity of 2.93 fb − 1 . Here, D denotes a neutral charm meson in a superposition of flavor eigenstates. The reported results are valuable for measurements of the C P -violating phase γ (also denoted ϕ 3 ) in B ± → D K ± , D → π + π − π + π − decays, and the binning schemes are designed to provide good statistical sensitivity to this parameter. The expected uncertainty on γ arising from the precision of the strong-phase measurements, when applied to very large samples of B -meson decays, is around 1.5° or 2°, depending on the binning scheme. The binned strong-phase parameters are combined to give a value of F + 4 π = 0.746 ± 0.010 ± 0.004 for the C P -even fraction of D 0 → π + π − π + π − decays, which is around 30% more precise than the previous best measurement of this quantity. Published by the American Physical Society 2024

Ablikim, M.↗

DOE OSTI2025

QCD Theory Meets Information Theory

We present a novel technique to incorporate precision calculations from quantum chromodynamics into fully differential particle-level Monte Carlo simulations. By minimizing an information-theoretic quantity subject to constraints, our reweighted Monte Carlo incorporates systematic uncertainties absent in individual Monte Carlo predictions, achieving consistency with the theory input in precision and its estimated systematic uncertainties. Our method can be applied to arbitrary observables known from precision calculations, including multiple observables simultaneously. It generates strictly positive weights, thus offering a clear path to statistically powerful and theoretically precise computations for current and future collider experiments. As a proof of concept, we apply our technique to event-shape observables at electron-positron colliders, leveraging existing precision calculations of thrust. Our analysis highlights the importance of logarithmic moments of event shapes, which have not been previously studied in the collider physics literature.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

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 OSTI2025

Possible evidence of excitonic condensation in a topological insulator

The transient excitonic condensate is a nonequilibrium electron-hole Bardeen-Cooper-Schrieffer state in a photoexcited semiconductor and semimetal, where electron-hole pairs undergo a phase transition and condense into a single coherent quantum state. Despite numerous experimental works to realize the predicted excitonic condensation phase, experimental evidence still remains elusive. This is largely due to the absence of direct measurements of a material's transient momentum-dependent electronic structure and the excitonic state in the condensation regime. Here, using time and angle-resolved photoemission spectroscopy, we find direct evidence of a transient excitonic condensate in the spin-polarized spatially indirect excitonic topological states in Bi2Te3. Accompanying the formation of the excitonic topological states by photoexcitation, we reveal a splitting of the hole's and electron's quasi-equilibrium chemical potential followed by the band flattening and backbending of the transient topological surface state. Moreover, within the same momentum range, we report a reshaping of the bulk valence band in the form of a Mexican-hat-like Bogoliubov dispersion-hallmarks of the excitonic condensation, followed by the opening of an energy gap at the Fermi level. The fluence and temperature dependence of these renormalization effects are reminiscent of excitonic condensation within Bardeen-Cooper-Schrieffer (BCS)-like behavior. These results, together with theoretical simulation, point to the possible formation of a transient excitonic condensate and provide opportunities to manipulate topologically protected Bose condensates with light.

ARPES↗

DOE OSTI2026

Measurement of the W-boson angular coefficients and transverse momentum in pp collisions at s=13 TeV with the ATLAS detector

The angular distributions of Drell–Yan lepton pairs provide sensitive probes of the underlying dynamics of quantum chromodynamics (QCD) effects in vector-boson production. This paper presents for the first time the measurement of the full set of angular coefficients together with the differential cross-section as a function of the transverse momentum of the W boson, in the full phase space of the decay leptons. The measurements are performed separately for the W-$$W^-$$ and W+$$W^+$$ channels. The analysis uses proton–proton collision data recorded by the ATLAS experiment at the Large Hadron Collider in 2017 and 2018, during special low-luminosity runs with a reduced number of interactions per bunch crossings (pile-up). The data correspond to an integrated luminosity of 338 pb-1$$^{-1}$$ at a centre-of-mass energy of s=13$$\sqrt{s} = 13$$ TeV. The low pile-up environment provides excellent experimental conditions for high-precision measurements of W-boson production. All results agree with theoretical predictions incorporating finite-order QCD corrections up to order αS2$$\alpha _S^2$$.

Aad, G↗

DOE OSTI2024

Measurement of t -channel production of single top quarks and antiquarks in pp collisions at 13 TeV using the full ATLAS Run 2 data sample

The production of single top quarks and top antiquarks via the t-channel exchange of a virtual W boson is measured in proton-proton collisions at a centre-of-mass energy of 13 TeV at the LHC using 140 fb -1 of ATLAS data. The total cross-sections are determined to be σ(tq) = 137$^{+8}_{-8}$ pb and σ($\bar{t}q$) = 84$^{+6}_{-5}$ pb for top-quark and top-antiquark production, respectively. The combined cross-section is found to be σ(tq + $\bar{t}q$) = 221$^{+13}_{-13}$ pb and the cross-section ratio is R t = σ(tq) / σ($\bar{t}q$) = 1.636$^{+0.036}_{-0.034}$. The predictions at next-to-next-to-leading-order in quantum chromodynamics are in good agreement with these measurements. The predicted value of R t using different sets of parton distribution functions is compared with the measured value, demonstrating the potential to further constrain the functions when using this result in global fits. The measured cross-sections are interpreted in an effective field theory approach, setting limits at the 95% confidence level on the strength of a four-quark operator and an operator coupling the third quark generation to the Higgs boson doublet: -0.37 < C$^{3,1}_{Qq}$ /Λ 2 < 0.06 and -0.87 < C$^{3}_{ΦQ}$ / Λ 2 < 1.42. The constraint |V tb | > 0.95 at the 95% confdence level is derived from the measured value of σ(tq + $\bar{t}q$), assuming that the Wtb interaction is a left-handed weak coupling and that |V tb | $\gg$ |V td |, |V ts |. In a more general approach, pairs of CKM matrix elements involving top quarks are simultaneously constrained, leading to confdence contours in the corresponding two-dimensional parameter spaces.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Observation of quantum entanglement with top quarks at the ATLAS detector

Abstract Entanglement is a key feature of quantum mechanics 1–3 , with applications in fields such as metrology, cryptography, quantum information and quantum computation 4–8 . It has been observed in a wide variety of systems and length scales, ranging from the microscopic 9–13 to the macroscopic 14–16 . However, entanglement remains largely unexplored at the highest accessible energy scales. Here we report the highest-energy observation of entanglement, in top–antitop quark events produced at the Large Hadron Collider, using a proton–proton collision dataset with a centre-of-mass energy of √s = 13 TeV and an integrated luminosity of 140 inverse femtobarns (fb) −1 recorded with the ATLAS experiment. Spin entanglement is detected from the measurement of a single observableD, inferred from the angle between the charged leptons in their parent top- and antitop-quark rest frames. The observable is measured in a narrow interval around the top–antitop quark production threshold, at which the entanglement detection is expected to be significant. It is reported in a fiducial phase space defined with stable particles to minimize the uncertainties that stem from the limitations of the Monte Carlo event generators and the parton shower model in modelling top-quark pair production. The entanglement marker is measured to beD = −0.537 ± 0.002 (stat.) ± 0.019 (syst.) for$$340\,{\rm{GeV}} < {m}_{t\bar{t}} < 380\,{\rm{GeV}}$$ 340 GeV < m t t ¯ < 380 GeV . The observed result is more than five standard deviations from a scenario without entanglement and hence constitutes the first observation of entanglement in a pair of quarks and the highest-energy observation of entanglement so far.

Science & Technology - Other Topics↗

DOE OSTI2025

Search for resonant leptoquark production via lepton-jet signatures in pp collisions at $\sqrt{s}=13$ TeV and $\sqrt{s}=13.6$ TeV with the ATLAS detector

This paper presents a search for physics beyond the Standard Model targeting a heavy resonance visible in the invariant mass of the lepton-jet system. The analysis focuses on final states with a high-energy lepton and jet, and is optimised for the resonant production of leptoquarks — a novel production mode mediated by the lepton content of the proton originating from quantum fluctuations. Four distinct and orthogonal final states are considered: e+light jet, μ+light jet, e+b-jet, and μ+b-jet, constituting the first search at the Large Hadron Collider for resonantly produced leptoquarks with couplings to electrons and muons. Events with an additional same-flavour lepton, as expected from higher-order diagrams in the signal process, are also included in each channel. The search uses proton-proton collision data from the full Run 2, corresponding to an integrated luminosity of 140 fb −1 at a centre-of-mass energy of $\sqrt{s}=13$ TeV, and from a part of Run 3 (2022–2023), corresponding to 55 fb −1 at $\sqrt{s}=13.6$ TeV. No significant excess over Standard Model predictions is observed. The results are interpreted as exclusion limits on scalar leptoquark ($\tilde{S}$ 1 ) production, substantially improving upon previous ATLAS constraints from leptoquark pair production for large coupling values. The excluded $\tilde{S}$ 1 $\tilde{S}$ 1 mass ranges depend on the coupling strength, reaching up to 3.4 TeV for quark-lepton couplings y de = 1.0, and up to 4.3 TeV, 3.1 TeV, and 2.8 TeV for y sμ , y be , and y bμ couplings set to 3.5, respectively.

Hadron-Hadron Scattering↗

DOE OSTI2026

The high explosives & affected targets (HEAT) dataset

Artificial Intelligence (AI) surrogate models offer a computationally efficient alternative to full-physics simulations, yet no existing datasets are publicly available for training, testing, and validation of machine learning models of the dynamics of high-explosive driven shocks through multiple materials. Shock propagation through materials is a computationally challenging problem because simulations must include material-specific equations of state (EOS) along with descriptions of other physical processes such as plastic deformation, phase change, damage processes, fluid instabilities, and multi-material interactions. Shocks are typically initiated by high-velocity impacts or explosive loading. The latter case necessitates the addition of models of reactive materials to represent high-explosive (HE) detonation. Here, to address the lack of an expansive dataset for multi-material shock propagation in the AI/ML community, we present the High-Explosives and Affected Targets (HEAT) Dataset. HEAT is a physics-rich collection of two-dimensional, cylindrically symmetric, simulations generated using an Eulerian, multi-material, shock-propagation code developed at Los Alamos National Laboratory. The dataset includes two partitions: (1) the expanding shock-cylinder (CYL) simulations, Figs. 1, and (2) the Perturbed Layered Interface (PLI) simulations, Fig. 2. Entries in both partitions consist of time series of arrays of thermodynamic fields (pressure, density, and temperature), kinematic fields (position and velocity), and additional fields that depend on thermodynamic and/or kinematic fields (e.g., material stress). Materials in the CYL partition include solids (aluminium, copper, depleted uranium, stainless steel, tantalum, and a generic polymer), a liquid (water), gases (air, nitrogen), and a generic detonating material (high explosive, HE). The PLI partition spans a highly varying geometry but consists of fixed materials across entries: Copper, aluminium, stainless steel, generic polymer, and generic HE. HEAT captures critical phenomena such as momentum transfer, shock propagation, plastic deformation, and thermal effects, making HEAT a valuable benchmark for development of AI/ML emulation of multi-material shock propagation.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Energy scale and resolution for anti-$k_t$ jets with radius parameters $R$ = 0.2 and 0.6 measured in proton-proton collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

Jets with different radius parameters R are an important tool for probing quantum chromodynamics processes at different angular scales. Jets with small R = 0.2 are instrumental in measurements of the substructure of large-R jets resulting from collimated hadronic decays of energetic W, Z, and Higgs bosons, top quarks, and of potential new resonances. This paper presents measurements of the energy scale, resolution, and associated uncertainties of jets with radius parameters R = 0.2 and 0.6, obtained using the ATLAS detector. The results are based on 37 fb -1 of proton–proton collision data from the Large Hadron Collider at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV. A new in situ method for measuring jet energy scale differences between data and Monte Carlo simulations is presented. The systematic uncertainties in the jet energy scale for central jets $(|\eta | < 1.2)$ typically vary from 1% to about 5% as a function of $|\eta |$ at very low transverse momentum, $p_T$ of around 20 GeV for both R = 0.2 and 0.6 jets. The relative energy resolution ranges from (35 ± 6)% at $p_T$ = 20 GeV to (6 ± 0.5)% at $p_T$ = 300 GeV for central R = 0.2 jets, and is found to be slightly worse for R + 0.6 jets. Finally, the effect of close-by hadronic activity on the jet energy scale is investigated and is found to be well modelled by the ATLAS Monte Carlo simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Measurements of W + W − production cross-sections in pp collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

Measurements of W + W − → e ± νμ ∓ ν production cross-sections are presented, providing a test of the predictions of perturbative quantum chromodynamics and the electroweak theory. The measurements are based on data from pp collisions at $\sqrt{s}$ = 13 TeV recorded by the ATLAS detector at the Large Hadron Collider in 2015–2018, corresponding to an integrated luminosity of 140 fb −1 . The number of events due to top-quark pair production, the largest background, is reduced by rejecting events containing jets with b-hadron decays. An improved methodology for estimating the remaining top-quark background enables a precise measurement of W + W − cross-sections with no additional requirements on jets. The fiducial W + W − cross-section is determined in a maximum-likelihood fit with an uncertainty of 3.1%. The measurement is extrapolated to the full phase space, resulting in a total W + W − cross-section of 127 ± 4 pb. Differential cross-sections are measured as a function of twelve observables that comprehensively describe the kinematics of W + W − events. The measurements are compared with state-of-the-art theory calculations and excellent agreement with predictions is observed. A charge asymmetry in the lepton rapidity is observed as a function of the dilepton invariant mass, in agreement with the Standard Model expectation. A CP-odd observable is measured to be consistent with no CP violation. Limits on Standard Model effective field theory Wilson coefficients in the Warsaw basis are obtained from the differential cross-sections.

Accelerator Physics↗