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Abstract Artificial Intelligence is poised to transform the design of complex, large-scale detectors like ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits.This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and usingGeant4simulations, our approach benefits from transparent parameterization and advanced AI features.The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring.Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process. 

Abstract. Processing Earth observation data modelled in a time-series of raster format is critical to solving some of the most complex problems in geospatial science ranging from climate change to public health. Researchers are increasingly working with these large raster datasets that are often terabytes in size. At this scale, traditional GIS methods may fail to handle the processing, and new approaches are needed to analyse these datasets. The objective of this work is to develop methods to interactively analyse big raster datasets with the goal of most efficiently extracting vector data over specific time periods from any set of raster data. In this paper, we describe RINX (Raster INformation eXtraction) which is an end-to-end solution for automatic extraction of information from large raster datasets. RINX heavily utilises open source geospatial techniques for information extraction. It also complements traditional approaches with state-of-the- art high-performance computing techniques. This paper discusses details of achieving big temporal data extraction with RINX, implemented on the use case of air quality and climate data extraction for long term health studies, which includes methods used, code developed, processing time statistics, project conclusions, and next steps. 

This report presents a comprehensive collection of searches for new physics performed by the ATLAS Collaboration during the Run~2 period of data taking at the Large Hadron Collider, from 2015 to 2018, corresponding to about 140~$$^{-1}$$ of $$\sqrt{s}=13$$~TeV proton--proton collision data. These searches cover a variety of beyond-the-standard model topics such as dark matter candidates, new vector bosons, hidden-sector particles, leptoquarks, or vector-like quarks, among others. Searches for supersymmetric particles or extended Higgs sectors are explicitly excluded as these are the subject of separate reports by the Collaboration. For each topic, the most relevant searches are described, focusing on their importance and sensitivity and, when appropriate, highlighting the experimental techniques employed. In addition to the description of each analysis, complementary searches are compared, and the overall sensitivity of the ATLAS experiment to each type of new physics is discussed. Summary plots and statistical combinations of multiple searches are included whenever possible. 

The ATLAS experiment has developed extensive software and distributed computing systems for Run 3 of the LHC. These systems are described in detail, including software infrastructure and workflows, distributed data and workload management, database infrastructure, and validation. The use of these systems to prepare the data for physics analysis and assess its quality are described, along with the software tools used for data analysis itself. An outlook for the development of these projects towards Run 4 is also provided. 

Abstract The ATLAS tile calorimeter (TileCal) is the hadronic sampling calorimeter covering the central region of the ATLAS detector at the Large Hadron Collider (LHC). This paper gives an overview of the calorimeter’s operation and performance during the years 2015–2018 (Run 2). In this period, ATLAS collected proton–proton collision data at a centre-of-mass energy of 13 TeV and the TileCal was 99.65% efficient for data-taking. The signal reconstruction, the calibration procedures, and the detector operational status are presented. The performance of two ATLAS trigger systems making use of TileCal information, the minimum-bias trigger scintillators and the tile muon trigger, is discussed. Studies of radiation effects allow the degradation of the output signals at the end of the LHC and HL-LHC operations to be estimated. Finally, the TileCal response to isolated muons, hadrons and jets from proton–proton collisions is presented. The energy and time calibration methods performed excellently, resulting in good stability and uniformity of the calorimeter response during Run 2. The setting of the energy scale was performed with an uncertainty of 2%. The results demonstrate that the performance is in accordance with specifications defined in the Technical Design Report. 

The associated production of Higgs and $W$bosons via vector-boson fusion is highly sensitive to the relative sign of the Higgs boson couplings to $W$and $Z$bosons. In this Letter, two searches for this process are presented, using $140{\mathrm{fb}}^{-1}$of proton-proton collision data at $\sqrt{s}=13\mathrm{TeV}$recorded by the ATLAS detector at the LHC. The first search targets scenarios with opposite-sign couplings of the $W$and $Z$bosons to the Higgs boson, while the second targets standard model-like scenarios with same-sign couplings. Both analyses consider Higgs boson decays into a pair of $b$quarks and $W$boson decays with an electron or muon. The data exclude the opposite-sign coupling hypothesis with a significance beyond $5\sigma$, and the observed (expected) upper limit set on the cross section for vector-boson fusion $WH$production is 9.0 (8.7) times the standard model value at 95% confidence level. © 2024 CERN, for the ATLAS Collaboration2024CERN 

Abstract A search for leptoquark pair production decaying into$$te^- \bar{t}e^+$$ $t{e}^{-}\overline{t}{e}^{+}$or$$t\mu ^- \bar{t}\mu ^+$$ $t{\mu }^{-}\overline{t}{\mu }^{+}$in final states with multiple leptons is presented. The search is based on a dataset ofppcollisions at$$\sqrt{s}=13~\text {TeV} $$ $\sqrt{s}=13\text{TeV}$recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb$$^{-1}$$ $_{}^{-1}$. Four signal regions, with the requirement of at least three light leptons (electron or muon) and at least two jets out of which at least one jet is identified as coming from ab-hadron, are considered based on the number of leptons of a given flavour. The main background processes are estimated using dedicated control regions in a simultaneous fit with the signal regions to data. No excess above the Standard Model background prediction is observed and 95% confidence level limits on the production cross section times branching ratio are derived as a function of the leptoquark mass. Under the assumption of exclusive decays into$$te^{-}$$ $t{e}^{-}$($$t\mu ^{-}$$ $t{\mu }^{-}$), the corresponding lower limit on the scalar mixed-generation leptoquark mass$$m_{\textrm{LQ}_{\textrm{mix}}^{\textrm{d}}}$$ $m_{{\text{LQ}}_{\text{mix}}^{\text{d}}}$is at 1.58 (1.59) TeV and on the vector leptoquark mass$$m_{{\tilde{U}}_1}$$ $m_{{\tilde{U}}_1}$at 1.67 (1.67) TeV in the minimal coupling scenario and at 1.95 (1.95) TeV in the Yang–Mills scenario. 

Abstract A search is reported for long-lived dark photons with masses between 0.1 GeV and 15 GeV, from exotic decays of Higgs bosons produced via vector-boson-fusion. Events that contain displaced collimated Standard Model fermions reconstructed in the calorimeter or muon spectrometer are probed. This search uses the full LHC Run 2 (2015–2018) data sample collected in proton–proton collisions at$$\sqrt{s}=13$$ $\sqrt{s}=13$TeV, corresponding to an integrated luminosity of 139$$fb^{-1}$$ $f{b}^{-1}$. Dominant backgrounds from Standard Model processes and non-collision sources are estimated using data-driven techniques. The observed event yields in the signal regions are consistent with the expected background. Upper limits on the Higgs boson to dark photon branching fraction are reported as a function of the dark photon mean proper decay length or of the dark photon mass and the coupling between the Standard Model and the potential dark sector. This search is combined with previous ATLAS searches obtained in the gluon–gluon fusion andWHproduction modes. A branching fraction above 10% is excluded at 95% CL for a 125 GeV Higgs boson decaying into two dark photons for dark photon mean proper decay lengths between 173 and 1296 mm and mass of 10 GeV.