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The LHC diphoton resonance and dark matter

2016· article· en· W2216498375 on OpenAlexfundno aff
Yann Mambrini, Giorgio Arcadi, A. Djouadi

Bibliographic record

VenuePhysics Letters B · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsnot available
FundersSeventh Framework ProgrammeEuropean Research CouncilMinisterio de Ciencia e InnovaciónPacific Institute for Climate Solutions
KeywordsPhysicsParticle physicsLarge Hadron ColliderPseudoscalarHiggs bosonResonance (particle physics)Dark matterCosmologyScalar (mathematics)Physics beyond the Standard ModelCoupling (piping)Nuclear physicsAstrophysicsMeson

Abstract

fetched live from OpenAlex

A resonance with a mass of approximately 750 GeV has recently been “observed” at the LHC in its diphoton decay. If this state is not simply a statistical fluctuation which will disappear with more data, it could have important implications not only for particle physics but also for cosmology. In this note, we analyze the implications of such a resonance for the dark matter (DM). Assuming a spin- 1 2 DM particle, we first verify that indeed the correct relic density can be obtained for a wide range of the particle mass and weak scale coupling, that are compatible with present data. We then show that the combination of near future direct and indirect detection experiments will allow to probe the CP-nature of the mediator resonance, i.e. discriminate whether it is a scalar or a pseudoscalar like particle.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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 designObservational
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

Citations167
Published2016
Admission routes1
Has abstractyes

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