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Record W2799918778 · doi:10.22323/1.282.0084

VERITAS Observations of the Galactic Center

2017· article· en· W2799918778 on OpenAlexfundno aff
A. J. Weinstein

Bibliographic record

VenueProceedings of 38th International Conference on High Energy Physics — PoS(ICHEP2016) · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaScience Foundation IrelandOffice of ScienceSmithsonian InstitutionNational Science Foundation
KeywordsPhysicsWIMPWeakly interacting massive particlesGalactic CenterDark matterAstrophysicsAstronomyCherenkov radiationAnnihilationCherenkov Telescope ArrayLight dark matterCenter (category theory)Gamma rayUniverseMilky WayScalar field dark matterParticle physicsCosmologyGalaxyDark energy

Abstract

fetched live from OpenAlex

Weakly-interacting massive particles (WIMPS) are among the wide range of candidates for the dark matter (DM) that dominates the mass content of the universe. In some scenarios these WIMPs are self-annihilating. In regions of high dark matter density (such as our own Galactic Center) this self-annihilation is expected to produce a characteristic gamma-ray radiation signature that cuts off at the WIMP mass. Observations of these regions of high dark matter density with very-high-energy (VHE) gamma-ray telescopes can constrain a unique phase space of heavy WIMP masses. However, in the case of the Galactic Center gamma-ray emission arising from more conventional astrophysical sources complicates the interpretation of such observations. We provide an update on deep observations of the Galactic Center with VERITAS, an array of atmospheric Cherenkov telescopes sensitive to VHE gamma rays with energies between 85 GeV and 30 TeV.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.039
GPT teacher head0.262
Teacher spread0.223 · 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 designObservational
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

Citations1
Published2017
Admission routes1
Has abstractyes

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