This Letter analyzes the sensitivity of event geometry estimators to the initial-state kinematics of hard scattering in proton–lead collisions. This analysis uses dijets as a proxy for the parton–parton scattering configuration, correlating it with event geometry estimators, namely the energy deposited in the Zero-Degree Calorimeter and the transverse energy recorded in the Forward Calorimeter in the Pb-going direction. The analysis uses data recorded by the ATLAS detector at the Large Hadron Collider with a nucleon–nucleon center-of-mass energy of 8.16 TeV, corresponding to an integrated luminosity of 56 nb⁻¹. The jets are measured within the pseudorapidity interval −2.8  <  η  <  4.5, where positive η values correspond to the direction of the proton beam. Results are presented as a function of the Bjorken-x of the parton originating from the proton, x_p. Both event geometry estimators are found to be dependent on x_p, with the energy deposited in the Zero-Degree Calorimeter about six times less sensitive to x_p compared with the transverse energy deposited in the Forward Calorimeter.

This paper reports the observation of electroweak diboson (WW/WZ/ZZ) production in association with a high-mass dijet system, in which final states with one boson decaying leptonically and the other boson decaying hadronically are studied. The hadronically decaying W/Z boson is reconstructed as either two small-radius jets or one large-radius jet with jet substructure requirements. The data analyzed correspond to an integrated luminosity of 140 fb−1 of proton–proton collisions at a center-of-mass energy of s√=13 TeV collected with the ATLAS detector during the 2015-2018 data taking at the Large Hadron Collider. The electroweak production of WW/WZ/ZZ in association with two jets is observed in a phase space dominated by vector-boson scattering with a significance of 7.4σ (expected 6.1σ) and the signal strength is determined to be 1.28+0.23−0.21. The corresponding production cross section in a fiducial phase space is measured in addition. The signal strengths of both electroweak and QCD associated diboson productions are furthermore measured in a two-dimensional fit, the result of which agrees with the Standard Model prediction. The data are interpreted in the context of a dimension-8 effective field theory to probe anomalous quartic gauge couplings resulting in the first set of exclusion limits on the Wilson coefficients in the semileptonic channel reported by the ATLAS Collaboration. The observed limits for the S02, T0 and M0 operators are (−3.96<fS02/Λ4<3.96) TeV−4, (−0.25<fT0/Λ4<0.22) TeV−4, (−1.26<fM0/Λ4<1.25) TeV−4.

Anisotropic flow and radial flow are two key probes of the expansion dynamics and properties of the quark-gluon plasma (QGP). While anisotropic flow has been extensively studied, radial flow, which governs the system’s radial expansion, has received less attention. Notably, direct experimental evidence for the global and collective nature of radial flow fluctuations has been lacking. This Letter presents the first measurement of transverse momentum (𝑝T) dependence of radial flow fluctuations (𝑣0⁡(𝑝T)) over 0.5 <𝑝T <10  GeV and demonstrates its collective nature using a two-particle correlation method in Pb+Pb collisions at √𝑠NN=5.02  TeV. The data reveal three key features supporting the collective nature of radial flow: long-range correlation in pseudorapidity, factorization in 𝑝T, and centrality-independent shape in 𝑝T. The comparison with a hydrodynamic model demonstrates the sensitivity of 𝑣0⁡(𝑝T) to bulk viscosity, a crucial transport property of the QGP. These findings establish a new, powerful tool for probing collective dynamics and properties of the QGP.

This paper presents the first observation of top-quark pair production in association with two photons (tt¯γγ). The measurement is performed in the single-lepton decay channel using proton-proton collision data collected by the ATLAS detector at the Large Hadron Collider. The data correspond to an integrated luminosity of 140 fb−1 recorded during Run 2 at a centre-of-mass energy of 13 TeV. The tt¯γγ production cross section, measured in a fiducial phase space based on particle-level kinematic criteria for the lepton, photons, and jets, is found to be 2.42+0.58−0.53fb, corresponding to an observed significance of 5.2 standard deviations. Additionally, the ratio of the production cross section of tt¯γγ to top-quark pair production in association with one photon is determined, yielding (3.30+0.70−0.65)×10−3.

A search is presented for long-lived particles decaying into an oppositely charged lepton pair, μ⁺, μ⁻, or e^±μ^∓, that form a vertex within the inner tracking system of the ATLAS detector at the Large Hadron Collider, displaced from the primary proton–proton interaction region. The analysis uses the 140 fb⁻¹ of Run-2 data collected at √𝑠 = 13 TeV by the ATLAS experiment in 2015–2018. The results of the analysis are interpreted in the context of three benchmark models covering masses from 0.1 to 2.2 TeV and a range of mean proper lifetimes times the speed of light from 1 to 10 000 mm. The first model is a generic Z′ boson pair-produced by a new heavy scalar, with the Z′ decaying into lepton pairs. The remaining two models are R-parity violating supersymmetric models in which the lightest neutralino decays into 𝓁+𝓁′−𝜈 (𝓁, 𝓁′= e, μ). The models differ by the mode of production of the , which can be produced via the decay of pairs of gluinos or of pairs of charginos and neutralinos (, , or ). Although each benchmark sample includes pair-produced LLPs, only a single vertex is required to be reconstructed. No dilepton displaced vertex candidate is observed and the results are presented as upper limits on the production cross-sections. This analysis sets leading limits on the production cross-sections for multiple models, including parameter space that has never been directly probed.

A search for final states characterised by at least one isolated high transverse-momentum photon, jets and large missing transverse momentum is presented. Such a final state might occur in gauge-mediated supersymmetric models where a pair of binos and higgsinos mix to form neutralinos, one of which decays into a photon plus a gravitino while the other decays into a Higgs boson, a Z boson or a photon, plus a gravitino. The search is performed using the full Run-2 data sample of 140 fb⁻¹ of s√=13 TeV proton-proton collisions collected by the ATLAS detector at the Large Hadron Collider. No significant excess of events is observed above the Standard Model prediction and model-dependent exclusion limits at the 95% confidence level are set. These limits are interpreted in terms of the masses of gauginos, which are excluded up to 1.2 TeV depending on their branching ratios, and on their branching ratios as a function of their mass.

Jet flavour tagging enables the identification of jets originating from heavy-flavour quarks in proton–proton collisions at the Large Hadron Collider, playing a critical role in its physics programmes. This paper presents GN2, a transformer-based flavour tagging algorithm deployed by the ATLAS Collaboration that represents a different methodology compared to previous approaches. Designed to classify jets based on the flavour of their constituent particles, GN2 processes low-level tracking information in an end-to-end architecture and incorporates physics-informed auxiliary training objectives to enhance both interpretability and performance. Its performance is validated in both simulation and collision data. The measured c-jet (light-jet) rejection in data is improved by a factor of 3.5 (1.8) for a 70% b-jet tagging efficiency, compared to the previous algorithm. GN2 provides substantial benefits for physics analyses involving heavy-flavour jets, such as measurements of Higgs boson pair production and the couplings of bottom and charm quarks to the Higgs boson, and demonstrates the impact of advanced machine learning methods in experimental particle physics.

A calibration of the ATLAS flavour-tagging algorithms using a new calibration procedure based on optimal transportation maps is presented. Simultaneous, continuous corrections to the b-jet, c-jet, and light-flavour jet classification probabilities from jet-tagging algorithms in simulation are derived for b-jets using data. After application of the derived calibration maps, closure between simulation and observation is achieved for jet flavour observables used in ATLAS analyses of Large Hadron Collider (LHC) Run 2 proton-proton collision data. This continuous calibration opens up new possibilities for the future use of jet flavour information in LHC analyses and also serves as a guide for deriving high-dimensional corrections to simulation via transportation maps, an important development for a broad range of inference tasks.

The production cross-section of high-mass τ-lepton pairs is measured as a function of the dilepton visible invariant mass, using 140 fb⁻¹ of s√=13 TeV proton-proton collision data recorded with the ATLAS detector at the Large Hadron Collider. The measurement agrees with the predictions of the Standard Model. A fit to the invariant mass distribution is performed as a function of b-jet multiplicity, to constrain the non-resonant production of new particles described by an effective field theory or in models containing leptoquarks or Z′ bosons that couple preferentially to third-generation fermions. The constraints on new particles improve on previous results, and the constraints on effective operators include those affecting the anomalous magnetic moment of the τ-lepton.

Neural simulation-based inference (NSBI) is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. This work develops a NSBI framework for statistical inference, using neural networks to estimate probability density ratios, which enables the application to a full-scale analysis. It incorporates a large number of systematic uncertainties, quantifies the uncertainty due to the finite number of events in training samples, develops a method to construct confidence intervals, and demonstrates a series of intermediate diagnostic checks that can be performed to validate the robustness of the method. As an example, the power and feasibility of the method are assessed on simulated data for a simplified version of an off-shell Higgs boson couplings measurement in the four-lepton final states. This approach represents an extension to the standard statistical methodology used by the experiments at the Large Hadron Collider, and can benefit many physics analyses.