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Update on 750 GeV diphotons from closed string states

2016· article· en· W2327227684 on OpenAlexfundno aff
Luis A. Anchordoqui, Ignatios Antoniadis, Haim Goldberg, X. T. Huang, Dieter Lüst, Tomasz R. Taylor

Bibliographic record

VenuePhysics Letters B · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsnot available
FundersMinistry of Science and Technology, TaiwanDeutsche ForschungsgemeinschaftYork UniversityNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsPhysicsString (physics)Particle physicsTheoretical physicsNuclear physics

Abstract

fetched live from OpenAlex

Motivated by the recent update on LHC searches for narrow and broad resonances decaying into diphotons we reconsider the possibility that the observed peak in the invariant mass spectrum at M γ γ = 750 GeV originates from a closed string (possibly axionic) excitation φ (associated with low mass scale string theory) that has a coupling with gauge kinetic terms. We reevaluate the production of φ by photon fusion to accommodate recent developments on additional contributions to relativistic light–light scattering. We also study the production of φ via gluon fusion. We show that for both a narrow and a broad resonance these two initial topologies can accommodate the excess of events, spanning a wide range of string mass scales 7 ≲ M s / TeV ≲ 30 that are consistent with the experimental lower bound: M s > 7 TeV , at 95% CL. We demonstrate that for the two production processes the LHC13 data is compatible with the lack of a diphoton excess in LHC8 data within ∼ 1 σ . We also show that if the resonance production is dominated by gluon fusion the null results on dijet searches at LHC8 further constrain the coupling strengths of φ , but without altering the range of possible string mass scales.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.005

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 head0.010
GPT teacher head0.235
Teacher spread0.225 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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