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Record W6887635392 · doi:10.17182/hepdata.149530

A statistical combination of ATLAS Run 2 searches for charginos and neutralinos at the LHC

2024· other· en· W6887635392 on OpenAlexfundno aff

Bibliographic record

VenueArchive ouverte UNIGE (University of Geneva) · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersHORIZON EUROPE Marie Sklodowska-Curie ActionsNatural Sciences and Engineering Research Council of CanadaAgencia Nacional de Promoción Científica y TecnológicaInstitut National de Physique Nucléaire et de Physique des ParticulesHorizon 2020 Framework ProgrammeEuropean Social FundGeneralitat de CatalunyaAgencia Nacional de Investigación y DesarrolloNarodowa Agencja Wymiany AkademickiejBundesministerium für Wissenschaft, Forschung und WirtschaftGeneralitat ValencianaAustrian Science FundConselho Nacional de Desenvolvimento Científico e TecnológicoNational Natural Science Foundation of ChinaMinisterstvo Školství, Mládeže a TělovýchovyCHIST-ERAAgence Nationale de la RechercheEuropean CommissionNational Science FoundationTRIUMFRoyal SocietyFundação de Amparo à Pesquisa do Estado de São PauloCERNCentres de Recerca de CatalunyaLeverhulme Trust
KeywordsCharginoLarge Hadron ColliderAtlas (anatomy)ATLAS experimentAtlas detectorSupersymmetryBosonMissing energy

Abstract

fetched live from OpenAlex

Statistical combinations of searches for charginos and neutralinos using various decay channels are performed using $139\,$fb$^{-1}$ of pp collision data at $\sqrt{s}$=13 TeV with the ATLAS detector at the Large Hadron Collider. Searches targeting pure-wino chargino pair production, pure-wino chargino-neutralino production, or higgsino production decaying via Standard Model W, Z, or h bosons are combined to extend the mass reach to the produced SUSY particles by 30-100 GeV. The depth of the sensitivity of the original searches is also improved by the combinations, lowering the 95% CL cross-section upper limits by 15%-40%.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.002

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 head0.020
GPT teacher head0.235
Teacher spread0.215 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

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