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Record W2883486330 · doi:10.22323/1.301.0500

Measurement of the Iron Spectrum in Cosmic Rays with VERITAS

2017· article· en· W2883486330 on OpenAlexfundno aff
Henrike Fleischhack

Bibliographic record

VenueProceedings of 35th International Cosmic Ray Conference — PoS(ICRC2017) · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaHelmholtz Alliance for Astroparticle PhysicsU.S. Department of EnergyOffice of ScienceSmithsonian InstitutionNational Science Foundation
KeywordsCherenkov radiationCosmic rayPhysicsAir showerCherenkov detectorRange (aeronautics)Charged particleParticle accelerationDetectorUltra-high-energy cosmic rayAstrophysicsOpticsNuclear physicsPlasmaIon

Abstract

fetched live from OpenAlex

The elemental energy spectra of cosmic rays play an important role in understanding their acceleration and propagation. Most current results are obtained either from direct measurements by balloon or satellite detectors, or from indirect measurements by air shower detector arrays on the Earth’s surface. Imaging Air Cherenkov Telescopes (IACTs), used primarily for gamma-ray astronomy, can also be used for cosmic-ray physics. They are able to measure Cherenkov light emitted both by heavy nuclei and by secondary particles produced in their air showers, and are thus sensitive to the charge and energy of cosmic ray particles with energies of tens to hundreds of TeV. A measurement of the energy spectrum of iron nuclei, based on 71 hours of data taken by the VERITAS array of IACTs between 2009 and 2012, will be presented. The energy and other properties of the primary particle are reconstructed using a template-based likelihood fit. The event selection makes use of direct Cherenkov light, which is emitted by the primary particle before starting the air shower. A multivariate method is used to estimate the remaining background. Using these methods, the iron spectrum was measured in the energy range from 20 TeV to 500 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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.798

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 science0.0020.000
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 head0.019
GPT teacher head0.235
Teacher spread0.216 · 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 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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