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Record W2750195634 · doi:10.22323/1.301.0595

Multi-TeV Energy Resolution Studies with VERITAS

2017· preprint· en· W2750195634 on OpenAlexfundno aff
R. M. Wells

Bibliographic record

VenueProceedings of 35th International Cosmic Ray Conference — PoS(ICRC2017) · 2017
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
KeywordsPhysicsSaturation (graph theory)Energy (signal processing)Resolution (logic)Gamma rayBrightnessSpectral lineAstrophysicsOpticsAstronomyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This work aims to investigate the systematic uncertainty in gamma-ray spectra arising from saturation effects from bright images reconstructed by VERITAS. The goal of the work is to improve or validate the energy resolution used for deriving gamma-ray spectra in the sub-TeV to multi-TeV energy regime with VERITAS. Saturation from multi-TeV gamma-ray events affects the image brightness used to reconstruct the energy of the primary gamma ray, and potentially biases the energy estimate. We discuss a method for investigating the energy resolution bias in these multi-TeV gamma-ray events by looking at showers with large core distances that do not saturate the cameras.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.048
GPT teacher head0.297
Teacher spread0.249 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2017
Admission routes1
Has abstractyes

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