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.

The top-quark Yukawa coupling is extracted from the distribution of the top-quark pair () invariant mass in proton-proton collisions using 140 fb⁻¹ of data at √s = 13 TeV collected in 2015–2018 by the ATLAS experiment at the Large Hadron Collider. In the region near the production threshold, the invariant mass spectrum is sensitive to electroweak virtual corrections, including contributions from Higgs boson exchange, thereby providing sensitivity to the top-quark Yukawa coupling. This is the first measurement in ATLAS that aims to obtain this coupling exploiting this approach. The system is reconstructed in the single-lepton final state, requiring exactly one isolated electron or muon and at least four jets with at least two identified as originating from b-quarks. The measured Yukawa coupling is found to be in good agreement with the Standard Model prediction. An upper limit on the top-quark Yukawa coupling strength of Y_t < 2.1 relative to the Standard Model prediction is observed at 95% confidence level, consistent with the expected sensitivity.

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.

This Letter reports the observation of W+W−γ triboson production in 140 fb−1 of data collected by the ATLAS detector from proton–proton collisions at a centre-of-mass energy of s√ = 13 TeV at the LHC. Events with an opposite-charge eμ pair, a high transverse-momentum photon, and significant missing transverse momentum are considered. The observed (expected) significance of the signal is 5.9 (6.0) standard deviations. The measured fiducial cross-section, defined for the W+W−γ→e±μ∓νν¯γ final state is 6.2  ±  0.8 (stat.)  ±  0.6 (sys.) fb, in good agreement with the Standard Model prediction of 6.1+1.0−0.7 fb. Constraints on the Wilson coefficients of 13 dimension-8 operators describing physics beyond the Standard Model through anomalous quartic gauge-boson couplings are derived using the effective field theory framework.

The existence of right-handed neutrinos with Majorana masses below the electroweak scale could help address the origins of neutrino masses, the matter-antimatter asymmetry, and dark matter. In this paper, leptonic decays of W bosons from 140 fb(-1) of 13 TeV proton-proton collisions at the LHC, reconstructed in the ATLAS experiment, are used to search for heavy neutral leptons produced through their mixing with muon or electron neutrinos in a scenario with lepton number violation. The search is conducted using prompt leptonic decay signatures. The considered final states require two same-charge leptons or three leptons, while vetoing three-lepton same-flavour topologies. No significant excess over the expected Standard Model backgrounds is found, leading to constraints on the heavy neutral lepton's mixing with muon and electron neutrinos for heavy-neutral-lepton masses. The analysis excludes vertical bar U-e vertical bar(2) values above 8x10(-5) and vertical bar U-mu vertical bar(2) values above 5.0x10(-5) in the full mass range of 8-65 GeV. The strongest constraints are placed on heavy-neutral-lepton masses in the range 15-30 GeV of vertical bar U-e vertical bar(2) < 1.1 x 10(-5) and vertical bar U-mu vertical bar(2) < 5 x 10(-6).

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.

The HIBEAM-NNBAR program is a proposed two-stage experiment at the European Spallation Source focusing on searches for baryon number violation processes as well as ultralight dark matter. This paper presents recent advancements in computing and simulation, including machine learning for event selection, fast parametric simulations for detector studies, and detailed modeling of the time projection chamber and readout electronics.

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.