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Record W2963780271 · doi:10.1007/jhep07(2017)097

Precision probes of QCD at high energies

2017· article· en· W2963780271 on OpenAlexaff
Simone Alioli, Marco Farina, Duccio Pappadopulo, Joshua T. Ruderman

Bibliographic record

VenueJournal of High Energy Physics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsInstitute of Particle Physics
FundersU.S. Department of EnergyNational Science Foundation
KeywordsPhysicsParticle physicsQuantum chromodynamicsLarge Hadron ColliderEffective field theoryPhysics beyond the Standard ModelJet (fluid)Operator product expansionInvariant massGluonNuclear physics

Abstract

fetched live from OpenAlex

New physics, that is too heavy to be produced directly, can leave measurable imprints on the tails of kinematic distributions at the LHC. We use energetic QCD processes to perform novel measurements of the Standard Model (SM) Effective Field Theory. We show that the dijet invariant mass spectrum, and the inclusive jet transverse momentum spectrum, are sensitive to a dimension 6 operator that modifies the gluon propagator at high energies. The dominant effect is constructive or destructive interference with SM jet production. We compare differential next-to-leading order predictions from POWHEG to public 7 TeV jet data, including scale, PDF, and experimental uncertainties and their respective correlations. We constrain a New Physics (NP) scale of 3.5 TeV with current data. We project the reach of future 13 and 100 TeV measurements, which we estimate to be sensitive to NP scales of 8 and 60 TeV, respectively. As an application, we apply our bounds to constrain heavy vector octet colorons that couple to the QCD current. We project that effective operators will surpass bump hunts, in terms of coloron mass reach, even for sequential couplings.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.255
Teacher spread0.243 · 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 designTheoretical or conceptual
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

Citations49
Published2017
Admission routes1
Has abstractyes

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