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Direct detection constraints on dark photon dark matter

2015· article· en· W1496325958 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhysics Letters B · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsUniversity of VictoriaPerimeter Institute
FundersNatural Sciences and Engineering Research Council of CanadaInstitut Périmètre de physique théoriqueÖsterreichischen Akademie der WissenschaftenWalter Burke Institute for Theoretical PhysicsGovernment of CanadaCalifornia Institute of TechnologyU.S. Department of Energy
KeywordsPhysicsDark matterPhotonDark photonParticle physicsAstrophysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Dark matter detectors built primarily to probe elastic scattering of WIMPs on nuclei are also precise probes of light, weakly coupled, particles that may be absorbed by the detector material. In this paper, we derive constraints on the minimal model of dark matter comprised of long-lived vector states V (dark photons) in the 0.01– 100 keV mass range. The absence of an ionization signal in direct detection experiments such as XENON10 and XENON100 places a very strong constraint on the dark photon mixing angle, down to O ( 10 − 15 ) , assuming that dark photons comprise the dominant fraction of dark matter. This sensitivity to dark photon dark matter exceeds the indirect bounds derived from stellar energy loss considerations over a significant fraction of the available mass range. We also revisit indirect constraints from V → 3 γ decay and show that limits from modifications to the cosmological ionization history are comparable to the updated limits from the diffuse γ -ray flux.

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.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.636
Threshold uncertainty score1.000

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)0.0000.001

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 head0.014
GPT teacher head0.223
Teacher spread0.209 · 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