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Record W4386307976 · doi:10.3847/2041-8213/acf049

Can Neutrino Self-interactions Save Sterile Neutrino Dark Matter?

2023· article· en· W4386307976 on OpenAlexaff
Rui An, Vera Gluscevic, Ethan O. Nadler, Yue Zhang

Bibliographic record

VenueThe Astrophysical Journal Letters · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsCarleton University
FundersNational Aeronautics and Space Administration
KeywordsSterile neutrinoPhysicsNeutrinoParticle physicsDark matterWarm dark matterAstrophysicsStandard Model (mathematical formulation)Measurements of neutrino speedCosmic neutrino backgroundNeutrino oscillationNeutrino detectorHot dark matterSolar neutrinoCosmologyDark energy

Abstract

fetched live from OpenAlex

Abstract Sterile neutrinos only interact with the standard model through the neutrino sector, and thus represent a simple dark matter (DM) candidate with many potential astrophysical and cosmological signatures. Recently, sterile neutrinos produced through self-interactions of active neutrinos have received attention as a particle candidate that can yield the entire observed DM relic abundance without violating the most stringent constraints from X-ray observations. We examine consistency of this production mechanism with the abundance of small-scale structure in the universe, as captured by the population of ultrafaint dwarf galaxies orbiting the Milky Way, and derive a lower bound on the sterile-neutrino particle mass of 37 keV. Combining these results with previous collider and X-ray limits excludes 100% sterile-neutrino DM produced by strong neutrino self-coupling, mediated by a heavy (≳1 GeV) scalar; however, data permits sterile-neutrino DM production via a light mediator.

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)0.001
metaresearch head score (Gemma)0.002
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.000
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 head0.009
GPT teacher head0.227
Teacher spread0.218 · 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

Citations20
Published2023
Admission routes1
Has abstractyes

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