MétaCan
Menu
Back to cohort
Record W2550850854 · doi:10.22323/1.282.0118

Global fits of scalar singlet dark matter with GAMBIT

2017· preprint· en· W2550850854 on OpenAlexafffund
Jonathan M. Cornell

Bibliographic record

VenueProceedings of 38th International Conference on High Energy Physics — PoS(ICHEP2016) · 2017
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGambitDark matterScalar (mathematics)PhysicsSinglet stateComputer scienceParticle physicsMathematicsQuantum mechanicsComputational fluid dynamicsMechanicsGeometry

Abstract

fetched live from OpenAlex

The wide range of probes of physics beyond the standard model leads to the need for tools that combine experimental results to make the most robust possible statements about the validity of theories and the preferred regions of their parameter space.Here we introduce a new code for such analyses: GAMBIT, the Global and Modular BSM Inference Tool.GAMBIT is a flexible and extensible framework for global fits of essentially any BSM theory.The code currently incorporates direct and indirect searches for dark matter, limits on production of new particles from the LHC and LEP, complete flavor constraints from LHCb, LHC Higgs production and decay measurements, and various electroweak precision observables.Here we present an overview of the code's capabilities, followed by preliminary results from scans of the scalar singlet dark matter model.

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)0.012
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.021
GPT teacher head0.257
Teacher spread0.236 · 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

Citations5
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of 38th International Conference on High Energy Physics — PoS(ICHEP2016)Same topicDark Matter and Cosmic PhenomenaFrench-language works237,207