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Record W4390892277 · doi:10.3204/pubdb-2024-00385

The ATLAS Trigger System for LHC Run 3 and Trigger performance in 2022

2023· preprint· en· W4390892277 on OpenAlexfundno aff
G. Aad

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
FundersCHIST-ERAH2020 Marie Skłodowska-Curie ActionsFundação para a Ciência e a TecnologiaJapan Society for the Promotion of ScienceAgencia Nacional de Investigación y DesarrolloNarodowa Agencja Wymiany AkademickiejIstituto Nazionale di Fisica NucleareState Key Laboratory of Particle Detection and ElectronicsUniversität InnsbruckUniversity of Science and Technology of ChinaUniversidade Federal do Rio de JaneiroUniversidade Federal de Juiz de ForaUniversité Paris-SaclayUniversidade do Estado do Rio de JaneiroInstitut National de Physique Nucléaire et de Physique des ParticulesUniversité de GenèveUniversité de FribourgUniversidade de São PauloBanco Bilbao Vizcaya ArgentariaNational Tsing Hua UniversityShanghai Jiao Tong UniversityMinisterio de Ciencia e InnovaciónUniversità degli Studi di PaviaMinistry of Education, IndiaAgence Nationale de la RechercheSapienza Università di RomaIsrael Science FoundationConselho Nacional de Desenvolvimento Científico e TecnológicoBundesministerium für Wissenschaft, Forschung und WirtschaftGeneralitat ValencianaGeneralitat de CatalunyaAustrian Science FundEuropean CommissionLeverhulme TrustFundação de Amparo à Pesquisa do Estado de São PauloUniversità di PisaDanmarks GrundforskningsfondJavna Agencija za Raziskovalno Dejavnost RSScience and Technology Facilities CouncilSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungUniversity of GlasgowNatural Sciences and Engineering Research Council of CanadaMinistry of Education, Culture, Sports, Science and TechnologyBundesministerium für Bildung und ForschungUniversité Grenoble AlpesMinisterstvo Školství, Mládeže a TělovýchovyIowa State UniversityNational Natural Science Foundation of ChinaScottish Universities Physics AllianceAlbert-Ludwigs-Universität FreiburgAgencia Nacional de Promoción Científica y TecnológicaGeorg-August-Universität GöttingenNederlandse Organisatie voor Wetenschappelijk OnderzoekDeutsche ForschungsgemeinschaftHORIZON EUROPE Marie Sklodowska-Curie ActionsCentres de Recerca de CatalunyaZhengzhou UniversityCERNU.S. Department of EnergyShandong UniversityJustus Liebig Universität GießenEuropean Regional Development FundBritish Columbia Knowledge Development FundCentre National de la Recherche ScientifiqueMax-Planck-GesellschaftEuropean Social FundCentre National pour la Recherche Scientifique et TechniqueRoyal SocietyAlexander von Humboldt-StiftungTRIUMFNational Science FoundationUniversità degli Studi di TrentoBaden-Württemberg StiftungUniversità degli Studi di Napoli Federico IIH2020 European Research CouncilNorges ForskningsrådFundación BBVAShanghai Key Laboratory for Particle Physics and CosmologyKnut och Alice Wallenbergs StiftelseUniversität HeidelbergAbdus Salam International Centre for Theoretical PhysicsTürkiye Enerji, Nükleer ve Maden Araştırma KurumuHorizon 2020 Framework ProgrammeVetenskapsrådetCanarieHarvard University
KeywordsLarge Hadron ColliderAtlas (anatomy)ATLAS experimentPhysicsProtonComputer scienceNuclear physics

Abstract

fetched live from OpenAlex

The ATLAS trigger system is a crucial component of the ATLAS experiment at the LHC.It is responsible for selecting events in line with the ATLAS physics programme. This paper presentsan overview of the changes to the trigger and data acquisition system during the second longshutdown of the LHC,and shows the performance of the trigger system and its components in the proton-proton collisionsduring the 2022 commissioning period as well as its expected performance inproton-proton and heavy-ion collisions for the remainder of the third LHC data-taking period (2022–2025).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.008

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.054
GPT teacher head0.187
Teacher spread0.133 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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