Scientific Data

Total Cost of Ownership and Evaluation of Google Cloud Resources for the ATLAS Experiment at the LHC

(2025)

Abstract: The ATLAS Google Project was established as part of an ongoing evaluation of the use of commercial clouds by the ATLAS Collaboration, in anticipation of the potential future adoption of such resources by WLCG grid sites to fulfil or complement their computing pledges. Seamless integration of Google cloud resources into the worldwide ATLAS distributed computing infrastructure was achieved at large scale and for an extended period of time, and hence cloud resources are shown to be an effective mechanism to provide additional, flexible computing capacity to ATLAS. For the first time a total cost of ownership analysis has been performed, to identify the dominant cost drivers and explore effective mechanisms for cost control. Network usage significantly impacts the costs of certain ATLAS workflows, underscoring the importance of implementing such mechanisms. Resource bursting has been successfully demonstrated, whilst exposing the true cost of this type of activity. A follow-up to the project is underway to investigate methods for improving the integration of cloud resources in data-intensive distributed computing environments and reducing costs related to network connectivity, which represents the primary expense when extensively utilising cloud resources.

Data Readiness for AI: A 360-Degree Survey

(2025)

Artificial Intelligence (AI) applications critically depend on data. Poor-quality data produces inaccurate and ineffective AI models that may lead to incorrect or unsafe use. Evaluation of data readiness is a crucial step in improving the quality and appropriateness of data usage for AI. R&D efforts have been spent on improving data quality. However, standardized metrics for evaluating data readiness for use in AI training are still evolving. In this study, we perform a comprehensive survey of metrics used to verify data readiness for AI training. This survey examines more than 140 papers published by ACM Digital Library, IEEE Xplore, journals such as Nature, Springer, and Science Direct, and online articles published by prominent AI experts. This survey aims to propose a taxonomy of data readiness for AI (DRAI) metrics for structured and unstructured datasets. We anticipate that this taxonomy will lead to new standards for DRAI metrics that would be used for enhancing the quality, accuracy, and fairness of AI training and inference.

Measurement of the Lund jet plane in hadronic decays of top quarks and W bosons with the ATLAS detector

(2025)

Abstract: The Lund jet plane (LJP) is measured for the first time in $$t\bar{t}$$ t t ¯ events, using 140 $$\textrm{fb}^{-1}$$ fb - 1 of $$\sqrt{s} = 13$$ s = 13 TeV pp collision data collected with the ATLAS detector at the LHC. The LJP is a two-dimensional observable of the sub-structure of hadronic jets that acts as a proxy for the kinematics of parton showers and hadron formation. The observable is constructed from charged particles and is measured for $$R=1.0$$ R = 1.0 anti- $$k_t$$ k t jets with transverse momentum above 350 GeV containing the full decay products of either a top quark or a daughter W boson. The other top quark in the event is identified from its decay into a b-quark, an electron or a muon and a neutrino. The measurement is corrected for detector effects and compared with a range of Monte Carlo predictions sensitive to different aspects of the hadronic decays of the heavy particles. In the W-boson-initiated jets, all the predictions are incompatible with the measurement. In the top quark initiated jets, disagreement with all predictions is observed in smaller subregions of the plane, and with a subset of the predictions across the fiducial plane. The measurement could be used to improve the tuning of Monte Carlo generators, for better modelling of hadronic decays of heavy quarks and bosons, or to improve the performance of jet taggers.

Observation of tt¯ Production in Pb+Pb Collisions at sNN=5.02 TeV with the ATLAS Detector

(2025)

Top-quark pair production is observed in lead–lead (Pb+Pb) collisions at sNN=5.02 TeV at the Large Hadron Collider with the ATLAS detector. The data sample was recorded in 2015 and 2018, amounting to an integrated luminosity of 1.9 nb−1. Events with exactly one electron and one muon and at least two jets are selected. Top-quark pair production is measured with an observed (expected) significance of 5.0 (4.1) standard deviations. The measured top-quark pair production cross section is σtt¯=3.6 −0.9+1.0(stat) −0.5+0.8(syst) μb, with a total relative uncertainty of 31%, and is consistent with theoretical predictions using a range of different nuclear parton distribution functions. The observation of this process consolidates the evidence of the existence of all quark flavors in the preequilibrium stage of the quark-gluon plasma at very high energy densities, similar to the conditions present in the early Universe. © 2025 CERN, for the ATLAS Collaboration 2025 CERN

Search for Dark Matter Produced in Association with a Dark Higgs Boson in the bb¯ Final State Using pp Collisions at s=13 TeV with the ATLAS Detector

(2025)

A search is performed for dark matter particles produced in association with a resonantly produced pair of (Formula presented)-quarks with (Formula presented) using (Formula presented) of proton-proton collisions at a center-of-mass energy of 13 TeV recorded by the ATLAS detector at the LHC. This signature is expected in extensions of the standard model predicting the production of dark matter particles, in particular those containing a dark Higgs boson (Formula presented) that decays into (Formula presented). The highly boosted (Formula presented) topology is reconstructed using jet reclustering and a new identification algorithm. This search places stringent constraints across regions of the dark Higgs model parameter space that satisfy the observed relic density, excluding dark Higgs bosons with masses between 30 and 150 GeV in benchmark scenarios with (Formula presented) mediator masses up to 4.8 TeV at 95% confidence level.

Intelligent IoT Attack Detection Design via ODLLM with Feature Ranking-based Knowledge Base

(2025)

The widespread adoption of Internet of Things (IoT) devices has introduced significant cybersecurity challenges, particularly with the increasing frequency and sophistication of Distributed Denial of Service (DDoS) attacks. Traditional machine learning (ML) techniques often fall short in detecting such attacks due to the complexity of blended and evolving patterns. To address this, we propose a novel framework leveraging On-Device Large Language Models (ODLLMs) augmented with fine-tuning and knowledge base (KB) integration for intelligent IoT network attack detection. By implementing feature ranking techniques and constructing both long and short KBs tailored to model capacities, the proposed framework ensures efficient and accurate detection of DDoS attacks while overcoming computational and privacy limitations. Simulation results demonstrate that the optimized framework achieves superior accuracy across diverse attack types, especially when using compact models in edge computing environments. This work provides a scalable and secure solution for real-time IoT security, advancing the applicability of edge intelligence in cybersecurity.

I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey

(2025)

Growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. This demand for speed has prompted the use of high performance computing (HPC) systems that excel in managing distributed workloads. Because data is the main fuel for AI applications, the performance of the storage and I/O subsystem of HPC systems is critical. In the past, HPC applications accessed large portions of data written by simulations or experiments or ingested data for visualizations or analysis tasks. ML workloads perform small reads spread across a large number of random files. This shift of I/O access patterns poses several challenges to modern parallel storage systems. In this paper, we survey I/O in ML applications on HPC systems, and target literature within a 6-year time window from 2019 to 2024. We define the scope of the survey, provide an overview of the common phases of ML, review available profilers and benchmarks, examine the I/O patterns encountered during offline data preparation, training, and inference, and explore I/O optimizations utilized in modern ML frameworks and proposed in recent literature. Lastly, we seek to expose research gaps that could spawn further R&D.

Software and computing for Run 3 of the ATLAS experiment at the LHC

(2025)

Abstract: 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.

Configuration, Performance, and Commissioning of the ATLAS b-jet Triggers for the 2022 and 2023 LHC data-taking periods

(2025)

Abstract: In 2022 and 2023, the Large Hadron Collider produced approximately two billion hadronic interactions each second from bunches of protons that collide at a rate of 40 MHz. The ATLAS trigger system is used to reduce this rate to a few kHz for recording. Selections based on hadronic jets, their energy, and event topology reduce the rate to 𝒪(10) kHz while maintaining high efficiencies for important signatures resulting in b-quarks, but to reach the desired recording rate of hundreds of Hz, additional real-time selections based on the identification of jets containing b-hadrons (b-jets) are employed to achieve low thresholds on the jet transverse momentum at the High-Level Trigger. The configuration, commissioning, and performance of the real-time ATLAS b-jet identification algorithms for the early LHC Run 3 collision data are presented. These recent developments provide substantial gains in signal efficiency for critical signatures; for the Standard Model production of Higgs boson pairs, a 50% improvement in selection efficiency is observed in final states with four b-quarks or two b-quarks and two hadronically decaying τ-leptons.

Studying baryon acoustic oscillations using photometric redshifts from the DESI Legacy Imaging survey DR9

(2025)

Context. The Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Survey DR9 (DR9 hereafter), with its extensive dataset of galaxy locations and photometric redshifts, presents an opportunity to study baryon acoustic oscillations (BAOs) in the region covered by the ongoing spectroscopic survey with DESI. Aims. We aim to investigate differences between different parts of the DR9 footprint. Furthermore, we want to measure the BAO scale for luminous red galaxies within them. Our selected redshift range of 0.6-0.8 corresponds to the bin in which a tension between DESI Y1 and eBOSS was found. Methods. We calculated the anisotropic two-point correlation function in a modified binning scheme to detect the BAOs in DR9 data. We then used template fits based on simulations to measure the BAO scale in the imaging data. Results. Our analysis reveals the expected correlation function shape in most of the footprint areas, showing a BAO scale consistent with Planck's observations. Aside from identified mask-related data issues in the southern region of the South Galactic Cap, we find a notable variance between the different footprints. Conclusions. We find that this variance is consistent with the difference between the DESI Y1 and eBOSS data, and it supports the argument that that tension is caused by sample variance. Additionally, we also uncovered systematic biases not previously accounted for in photometric BAO studies. We emphasize the necessity of adjusting for the systematic shift in the BAO scale associated with typical photometric redshift uncertainties to ensure accurate measurements.

Computing Sciences

Scientific Data