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Record W2171640151 · doi:10.1103/physrevd.93.112015

Search for metastable heavy charged particles with large ionization energy loss in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>p</mml:mi><mml:mi>p</mml:mi></mml:math>collisions at<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msqrt><mml:mi>s</mml:mi></mml:msqrt><mml:mo>=</mml:mo><mml:mn>13</mml:mn><mml:mtext> </mml:mtext><mml:mtext> </mml:mtext><mml:mi>TeV</mml:mi></mml:math>using the ATLAS experiment

2016· article· lv· W2171640151 on OpenAlexafffund
M. Aaboud, G. Aad, B. Abbott, J. Abdallah, O. Abdinov, B. Abeloos, R. Aben, O. S. AbouZeid, H. Abramowicz, J. Adelman

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2016
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsInstitute of Particle Physics
FundersH2020 Marie Skłodowska-Curie ActionsInstitut National de Physique Nucléaire et de Physique des ParticulesSeventh Framework ProgrammeInstituto Nazionale di Fisica NucleareAgencia Nacional de Promoción Científica y TecnológicaFundação para a Ciência e a TecnologiaAustralian Research CouncilJapan Society for the Promotion of ScienceNational Research Center "Kurchatov Institute"Services Fédéraux des Affaires Scientifiques, Techniques et CulturellesBergens ForskningsstiftelseGeorgian National Science FoundationMinistarstvo Prosvete, Nauke i Tehnološkog RazvojaVetenskapsrådetJavna Agencija za Raziskovalno Dejavnost RSBritish Columbia Knowledge Development FundEuropean Research CouncilCentre National de la Recherche ScientifiqueMax-Planck-GesellschaftKnut och Alice Wallenbergs StiftelseIsrael Science FoundationMinisterstwo Edukacji i NaukiNarodowe Centrum NaukiCanada Foundation for InnovationConselho Nacional de Desenvolvimento Científico e TecnológicoMinistry of Education and Science of the Russian FederationBundesministerium für Wissenschaft, Forschung und WirtschaftNational Research Council CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesAustrian Science FundDeutsche ForschungsgemeinschaftNederlandse Organisatie voor Wetenschappelijk OnderzoekChinese Academy of SciencesFondation Partager le SavoirNational Science CouncilEuropean CommissionLeverhulme TrustFundação de Amparo à Pesquisa do Estado de São PauloDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)German-Israeli Foundation for Scientific Research and DevelopmentMinisterstvo školstva, vedy, výskumu a športu Slovenskej republikyDanmarks GrundforskningsfondNatural Sciences and Engineering Research Council of CanadaMinisterio de Economía y CompetitividadMinistry of Education, Culture, Sports, Science and TechnologyBundesministerium für Bildung und ForschungResearch Grants Council, University Grants CommitteeMinisterstvo Školství, Mládeže a TělovýchovyDepartment of Science and Technology, Ministry of Science and Technology, IndiaStichting voor Fundamenteel Onderzoek der MaterieScience and Technology Facilities CouncilMinisterstvo Průmyslu a ObchoduSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungMinistry of Science and Technology of the People's Republic of ChinaMinistrstvo za Izobraževanje, Znanost in ŠportAgence Nationale de la RechercheMinerva FoundationComisión Nacional de Investigación Científica y TecnológicaTürkiye Atom Enerjisi KurumuJoint Institute for Nuclear ResearchDepartment of Science and Technology, Republic of South AfricaGeneralitat ValencianaGeneralitat de CatalunyaGeneral Secretariat for Research and TechnologyEuropean Social FundCentre National pour la Recherche Scientifique et TechniqueRoyal SocietyNational Science FoundationLundbeckfondenCompute CanadaAlexander von Humboldt-StiftungTRIUMFNational Natural Science Foundation of ChinaIsraeli Centers for Research ExcellenceU.S. Department of EnergyOntario Innovation TrustCERNCanarieHelmholtz-GemeinschaftNorges ForskningsrådNatur og Univers, Det Frie Forskningsråd
KeywordsPhysicsHadronIonizationCharged particleLarge Hadron ColliderNuclear physicsParticle physicsATLAS experimentGluinoLuminosityAtlas (anatomy)NeutralinoIonAstrophysics

Abstract

fetched live from OpenAlex

This paper presents a search for massive charged long-lived particles produced in $pp$ collisions at $\sqrt{s}=13\text{ }\text{ }\mathrm{TeV}$ at the LHC using the ATLAS experiment. The data set used corresponds to an integrated luminosity of $3.2\text{ }\text{ }{\mathrm{fb}}^{\ensuremath{-}1}$. Many extensions of the Standard Model predict the existence of massive charged long-lived particles, such as $R$-hadrons. These massive particles are expected to be produced with a velocity significantly below the speed of light, and therefore to have a specific ionization higher than any Standard Model particle of unit charge at high momenta. The Pixel subsystem of the ATLAS detector is used to measure the ionization energy loss of reconstructed charged particles and to search for such highly ionizing particles. The search presented here has much greater sensitivity than a similar search performed using the ATLAS detector in the $\sqrt{s}=8\text{ }\text{ }\mathrm{TeV}$ data set, thanks to the increase in expected signal cross section due to the higher center-of-mass energy of collisions, to an upgraded detector with a new silicon layer close to the interaction point, and to analysis improvements. No significant deviation from Standard Model background expectations is observed, and lifetime-dependent upper limits on $R$-hadron production cross sections and masses are set. Gluino $R$-hadrons with lifetimes above 0.4 ns and decaying to $q\overline{q}$ plus a 100 GeV neutralino are excluded at the 95% confidence level, with lower mass limit ranging between 740 and 1590 GeV. In the case of stable $R$-hadrons the lower mass limit at the 95% confidence level is 1570 GeV.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.021
GPT teacher head0.307
Teacher spread0.286 · 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".

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Citations68
Published2016
Admission routes2
Has abstractyes

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