285 Search Results

The intelligent distributed dispatch and scheduling (iDDS) service is a versatile workflow orchestration system designed for large-scale, distributed scientific computing. iDDS extends traditional workload and data management by integrating data-aware execution, conditional logic, and programmable workflows, enabling automation of complex and dynamic processing pipelines. Originally developed for the ATLAS experiment at the large hadron collider, iDDS has evolved into an experiment-agnostic platform that supports both template-driven workflows and a Function-as-a-Task model for Python-based orchestration. This paper presents the architecture and core components of iDDS, highlighting its scalability, modular message-driven design, and integration with systems such as PanDA and Rucio. Wemore » demonstrate its versatility through real-world use cases: fine-grained tape resource optimization for ATLAS, orchestration of large Directed Acyclic Graph (DAG) workflows for the Rubin Observatory, distributed hyperparameter optimization for machine learning applications, active learning for physics analyses, and AI-assisted detector design at the electron–ion collider. By unifying workload scheduling, data movement, and adaptive decision-making, iDDS reduces operational overhead and enables reproducible, high-throughput workflows across heterogeneous infrastructures. We conclude with current challenges and future directions, including interactive, cloud-native, and serverless workflow support.« less

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 (𝑝_T) dependence of radial flow fluctuations (𝑣₀⁡(𝑝_T)) over 0.5 < 𝑝_T < 10 GeV and demonstrates its collective nature using a two-particle correlation method in Pb+Pb collisions at $$\sqrt{𝑠_{NN}}$$ = 5.02 TeV. The data revealmore » three key features supporting the collective nature of radial flow: long-range correlation in pseudorapidity, factorization in 𝑝_T, and centrality-independent shape in 𝑝_T. The comparison with a hydrodynamic model demonstrates the sensitivity of 𝑣₀⁡(𝑝_T) 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.« less

The top-quark Yukawa coupling is extracted from the distribution of the top-quark pair ($$t\overline{t}$$) invariant mass in proton-proton collisions using 140 fb⁻¹ of data at $$\sqrt{s}=13$$ TeV collected in 2015–2018 by the ATLAS experiment at the Large Hadron Collider. In the region near the production threshold, the $$t\overline{t}$$ 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 $$t\overline{t}$$ system is reconstructed in the single-lepton final state, requiring exactly onemore » 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.« less

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 inmore » 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.« less

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 additionalmore » 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.« less

A search for R-parity-conserving supersymmetry in events with large missing transverse momentum, jets and at least one hadronically decaying $$\tau$$-lepton is presented. Both gluino and squark pair production are considered, with the cascade decay of each gluino or squark producing either a $$\tau$$-slepton or a $$\tau$$-sneutrino. Three channels are examined, requiring either exactly one hadronically decaying $$\tau$$-lepton and no other leptons, exactly one hadronically decaying $$\tau$$-lepton and at least one other lepton, or two or more hadronically decaying $$\tau$$-leptons. Analyses in the three channels are optimised independently and combined statistically. Two separate analysis strategies, either a cut-and-count or machine-learning approach,more » are used. The search uses 140 and 51.8 of pp collision data recorded by the ATLAS detector at the Large Hadron Collider during 2015–2018 at TeV and 2022–2023 at TeV, respectively. Gluino masses below 2.25 TeV and squark masses up to 1.7 TeV are excluded« less

Measurements of $$t\bar{t}\ell ^{+}\ell ^{-}$$ production in the region of high dilepton invariant mass with effective field theory (EFT) interpretations are presented. They are performed using final states with three isolated leptons (electrons or muons) and are based on $$\sqrt{s} = 13$$ TeV proton–proton collision data with an integrated luminosity of $$140\,\textrm{fb}^{-1}$$, recorded from 2015 to 2018 with the ATLAS detector at the Large Hadron Collider. Measurements of the $$t\bar{t}\ell ^{+}\ell ^{-}$$ signal strength and cross-section upper-limits are performed inclusively in lepton flavour and separately for electrons and muons. The study also aims to probe anomalous four-fermion interactions including tomore » test for possible lepton flavor universality violation. No significant deviations from the Standard Model predictions are observed and the measurements are interpreted through the EFT formalism to provide new constraints on the relevant operators.« less

The ATLAS experiment at the Large Hadron Collider explores the use of modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of topologically connected cells (topo-clusters). The Bayesian neural network (BNN) approach not only yields a continuous and smooth calibration function that improves performance relative to the standard calibration but also provides uncertainties on the calibrated energies for each topo-cluster. The results obtained by using a trained BNN are compared to the standard local hadronic calibration and to a calibration provided by training a deep neural network. The uncertainties predicted by the BNN are interpretedmore » in the context of a fractional contribution to the systematic uncertainties of the trained calibration. They are also compared to uncertainty predictions obtained from an alternative estimator employing repulsive ensembles.« less

Higgs boson production cross-sections via gluon–gluon fusion and vector-boson fusion in proton–proton collisions are measured in the $$H\rightarrow WW^*\rightarrow \ell \nu \ell \nu$$ decay channel. The Large Hadron Collider delivered proton–proton collisions at a centre-of-mass energy of 13 TeV between 2015 and 2018, which were recorded by the ATLAS detector, corresponding to an integrated luminosity of $$140\,\text {fb}^{-1}.$$ The total cross-sections for Higgs boson production by gluon–gluon fusion and vector-boson fusion times the $$H\rightarrow WW^*$$ branching ratio are measured to be $$12.4^{+1.3}_{-1.2}\,\text {pb}$$ and $$0.79^{+0.18}_{-0.16}\,\text {pb},$$ respectively, in agreement with the Standard Model predictions. Higgs boson production is further characterisedmore » through measurements of Simplified Template Cross-Sections in a total of fifteen kinematic fiducial regions. A new scheme of kinematic fiducial regions has been introduced to enhance the sensitivity to CP-violating effects in Higgs boson interactions. Both schemes are used to constrain CP-even and CP-odd dimension-six operators in the Standard Model effective field theory.« less

ATLAS, a general-purpose experiment at the Large Hadron Collider (LHC), makes use of a large internationally-distributed computing infrastructure, including over 10⁶ TB of managed data on disk and tape and almost one million simultaneously running CPU cores. Upgrades for the High-Luminosity LHC (HL-LHC) will increase the required computing resources by a factor of 3–4 by the beginning of the 2030s, and by an order of magnitude before the conclusion of data taking at the beginning of the 2040s. These resources are spread over around 100 computing sites worldwide. Efforts are underway within the experiment to evaluate and mitigate various aspectsmore » of the environmental impact of the sites, with the additional long-term goal of making recommendations to the sites that will significantly reduce the total expected environmental impact in the HL-LHC era. These efforts take several forms: building awareness in the experiment community, adjusting aspects of the computing policy, and modifications of data center configurations, either in ways that take advantage of particular features of ATLAS workloads or in generic ways that reduce the environmental impact of the computing resources. This paper describes the ongoing investigations and approaches that have already provided useful and actionable outcomes.« less