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Improving Identification of Dijet Resonances at Hadron Colliders

2015· article· en· W1507894344 on OpenAlexafffund
Eder Izaguirre, Brian Shuve, Itay Yavin

Bibliographic record

VenuePhysical Review Letters · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsMcMaster UniversityPerimeter Institute
FundersMainz Institute for Theoretical Physics, Johannes Gutenberg University MainzOntario Ministry of Research, Innovation and ScienceInstitut Périmètre de physique théoriqueIndustry Canada
KeywordsPhysicsParticle physicsLarge Hadron ColliderElectroweak interactionHiggs bosonHadronPartonNuclear physicsPhysics beyond the Standard ModelStandard Model (mathematical formulation)ObservableQuarkResonance (particle physics)Vector bosonGauge (firearms)

Abstract

fetched live from OpenAlex

The experimental detection of resonances has played a vital role in the development of subatomic physics. The overwhelming multijet backgrounds at the Large Hadron Collider (LHC) necessitate the invention of new techniques to identify resonances decaying into a pair of partons. In this Letter we introduce an observable that achieves a significant improvement in several key measurements at the LHC: the Higgs boson decay to a pair of b quarks; W±/Z0 vector-boson hadronic decay; and extensions of the standard model (SM) with a new hadronic resonance. Measuring the Higgs decay to b quarks is a central test of the fermion mass generation mechanism in the SM, whereas the W±/Z0 production rates are important observables of the electroweak sector. Our technique is effective in large parts of phase space where the resonance is mildly boosted and is particularly well suited for experimental searches dominated by systematic uncertainties, which is true of many analyses in the high-luminosity running of the LHC.

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: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.304
Teacher spread0.281 · 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
GenreMethods

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

Citations5
Published2015
Admission routes2
Has abstractyes

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