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Record W1138335524 · doi:10.22323/1.236.0750

The VERITAS Survey of the Cygnus Region of the Galaxy

2016· preprint· en· W1138335524 on OpenAlexfundno aff
A. Popkow

Bibliographic record

VenueProceedings of The 34th International Cosmic Ray Conference — PoS(ICRC2015) · 2016
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaU.S. Department of EnergyOffice of ScienceSmithsonian InstitutionNational Science Foundation
KeywordsPhysicsAstronomyAstrophysicsGalaxyPulsarFermi Gamma-ray Space TelescopeCherenkov radiationObservatorySupernovaTelescopeActive galactic nucleusCherenkov Telescope ArrayAstroparticle physicsCosmic ray

Abstract

fetched live from OpenAlex

The Cygnus region is a very active region of our Galaxy with many sources of GeV and TeV gamma-ray emission, such as supernova remnants, pulsar wind nebulae, and massive star clusters. A detailed study of the Cygnus region at these energies can give insight into the processes of particle acceleration in astrophysical sources. VERITAS (Very Energetic Radiation Imaging Telescope Array System) is an array of four 12 meter diameter imaging atmospheric Cherenkov telescopes located at the Fred Lawrence Whipple Observatory (FLWO) in southern Arizona. From 2007 through 2012 VERITAS observed the Cygnus region for nearly 300 hours, from 67 to 82 degrees Galactic longitude and from -1 to 4 degrees in Galactic latitude. We have reanalyzed the VERITAS data with updated more sensitive analysis techniques and will be cross correlating that data with the results of an analysis of nearly six years of Fermi-LAT data in the region. Using this cross correlation we can motivate continued observations in this active region of the Galaxy.

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 score0.140
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.027
GPT teacher head0.245
Teacher spread0.219 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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