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Record W2339587169 · doi:10.1088/1475-7516/2016/07/027

Renormalization group study of the minimal Majoronic dark radiation and dark matter model

2016· article· en· W2339587169 on OpenAlexaff

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsTRIUMF
Fundersnot available
KeywordsDark matterDark radiationNeutrinoScalar field dark matterLight dark matterPhysics beyond the Standard ModelRenormalization groupDecoupling (probability)

Abstract

fetched live from OpenAlex

We study the 1-loop renormalization group equation running in the simplest singlet Majoron model constructed by us earlier to accommodate the dark radiation and dark matter content in the universe. A comprehensive numerical study was performed to explore the whole model parameter space. A smaller effective number of neutrinos $\triangle N_{eff}\sim 0.05$, or a Majoron decoupling temperature higher than the charm quark mass, is preferred. We found that a heavy scalar dark matter, $\rho$, of mass $1.5-4$ TeV is required by the stability of the scalar potential and an operational type-I see-saw mechanism for neutrino masses. A neutral scalar, $S$, of mass in the $10-100$ GeV range and its mixing with the standard model Higgs as large as $0.1$ is also predicted. The dominant decay modes are $S$ into $b\bar{b}$ and/or $\omega\omega$. A sensitive search will come from rare $Z$ decays via the chain $Z\rightarrow S+ f\bar{f}$, where $f$ is a Standard Model fermion, followed by $S$ into a pair of Majoron and/or b-quarks. The interesting consequences of dark matter bound state due to the sizable $S\rho \rho$-coupling are discussed as well. In particular, shower-like events with an apparent neutrino energy at $M_\rho$ could contribute to the observed effective neutrino flux in underground neutrino detectors such as IceCube.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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 teacher head, 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

Citations10
Published2016
Admission routes1
Has abstractyes

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