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Record W2589299402 · doi:10.1093/mnras/stx560

Dynamical friction in the primordial neutrino sea

2017· article· en· W2589299402 on OpenAlexaff
Chiamaka Okoli, Morag I. Scrimgeour, Niayesh Afshordi, Michael J. Hudson

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of WaterlooPerimeter Institute
Fundersnot available
KeywordsPhysicsNeutrinoAstrophysicsNeutrino oscillationCosmologyGalaxyDark matterContext (archaeology)Galaxy formation and evolutionCosmic neutrino backgroundRedshiftAstronomyHaloParticle physicsSolar neutrino

Abstract

fetched live from OpenAlex

Standard big bang cosmology predicts a cosmic neutrino background at Tν ≃ 1.95 K. Given the current neutrino oscillation measurements, we know most neutrinos move at large, but non-relativistic, velocities. Therefore, dark matter haloes moving in the sea of primordial neutrinos form a neutrino wake behind them, which would slow them down, due to the effect of dynamical friction. In this paper, we quantify this effect for realistic haloes, in the context of the halo model of structure formation, and show that it scales as |$m_\nu ^4$|× relative velocity and monotonically grows with the halo mass. Galaxy redshift surveys can be sensitive to this effect (at >3σ confidence level, depending on survey properties, neutrino mass and hierarchy) through redshift space distortions of distinct galaxy populations.

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)0.009
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.209
Teacher spread0.201 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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