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Record W2983011949 · doi:10.1007/jhep02(2020)039

Invisible and displaced dark matter signatures at Belle II

2020· article· en· W2983011949 on OpenAlexafffund

Bibliographic record

VenueJournal of High Energy Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsInstitute of Particle PhysicsUniversity of British Columbia
FundersScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaDeutsche Forschungsgemeinschaft
KeywordsDark matterParameter spaceVertex (graph theory)Weakly interacting massive particlesSensitivity (control systems)Dark energySignature (topology)Missing energyPhoton

Abstract

fetched live from OpenAlex

Abstract Many dark matter models generically predict invisible and displaced signatures at Belle II, but even striking events may be missed by the currently implemented search programme because of inefficient trigger algorithms. Of particular interest are final states with a single photon accompanied by missing energy and a displaced pair of electrons, muons, or hadrons. We argue that a displaced vertex trigger will be essential to achieve optimal sensitivity at Belle II. To illustrate this point, we study a simple but well-motivated model of thermal inelastic dark matter in which this signature naturally occurs and show that otherwise inaccessible regions of parameter space can be tested with such a search. We also evaluate the sensitivity of single-photon searches at BaBar and Belle II to this model and provide detailed calculations of the relic density target.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.006
GPT teacher head0.195
Teacher spread0.190 · 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

Citations72
Published2020
Admission routes2
Has abstractyes

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