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Dark Matter Detectors as Dark Photon Helioscopes

2013· article· en· W1996937737 on OpenAlexaff
Haipeng An, Maxim Pospelov, Josef Pradler

Bibliographic record

VenuePhysical Review Letters · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsUniversity of VictoriaPerimeter Institute
Fundersnot available
KeywordsPhysicsDark matterDark photonPhotonStandard Model (mathematical formulation)Light dark matterLimit (mathematics)Weakly interacting massive particlesVector bosonIonizationBosonParticle physicsAstrophysicsAtomic physicsNuclear physicsScalar field dark matterDark energyCosmologyQuantum mechanicsIon

Abstract

fetched live from OpenAlex

Light new particles with masses below 10 keV, often considered as a plausible extension of the standard model, will be emitted from the solar interior and can be detected on Earth with a variety of experimental tools. Here, we analyze the new "dark" vector state V, a massive vector boson mixed with the photon via an angle κ, that in the limit of the small mass mV has its emission spectrum strongly peaked at low energies. Thus, we utilize the constraints on the atomic ionization rate imposed by the results of the XENON10 experiment to set the limit on the parameters of this model: κ×mV<3×10(-12) eV. This makes low-threshold dark matter experiments the most sensitive dark vector helioscopes, as our result not only improves current experimental bounds from other searches by several orders of magnitude but also surpasses even the most stringent astrophysical and cosmological limits in a seven-decade-wide interval of mV. We generalize this approach to other light exotic particles and set the most stringent direct constraints on "minicharged" particles.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.999

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)0.0020.013

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 head0.248
Teacher spread0.241 · 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; both teacher heads agree on what is shown here.

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

Citations170
Published2013
Admission routes1
Has abstractyes

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