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Tensor Interpretation of BICEP2 Results Severely Constrains Axion Dark Matter

2014· article· en· W1546860581 on OpenAlexaff
David J. E. Marsh, Daniel Grin, Renée Hložek, Pedro G. Ferreira

Bibliographic record

VenuePhysical Review Letters · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsPerimeter Institute
FundersScience and Technology Facilities CouncilNational Science Foundation
KeywordsPhysicsAxionDark matterGravitational wavePolarization (electrochemistry)Cold dark matterHot dark matterParticle physicsInterpretation (philosophy)Tensor (intrinsic definition)AstrophysicsCosmologyDark energyPhilosophy

Abstract

fetched live from OpenAlex

The recent detection of B modes by the BICEP2 experiment has nontrivial implications for axion dark matter implied by combining the tensor interpretation with isocurvature constraints from Planck observations. In this Letter the measurement is taken as fact, and its implications considered, though further experimental verification is required. In the simplest inflation models, r=0.2 implies HI=1.1×10(14) GeV. If the axion decay constant fa<HI/2π, constraints on the dark matter (DM) abundance alone rule out the QCD axion as DM for ma≲52χ6/7 μeV (where χ>1 accounts for theoretical uncertainty). If fa>HI/2π then vacuum fluctuations of the axion field place conflicting demands on axion DM: isocurvature constraints require a DM abundance which is too small to be reached when the backreaction of fluctuations is included. High-fa QCD axions are thus ruled out. Constraints on axionlike particles, as a function of their mass and DM fraction, are also considered. For heavy axions with ma≳10(-22) eV we find Ωa/Ωd≲10(-3), with stronger constraints on heavier axions. Lighter axions, however, are allowed and (inflationary) model-independent constraints from the CMB temperature power spectrum and large scale structure are stronger than those implied by tensor modes.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.561

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 head0.253
Teacher spread0.245 · 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 designNot applicable
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

Citations76
Published2014
Admission routes1
Has abstractyes

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