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Record W2765757925 · doi:10.22323/1.301.0646

First combined studies on Lorentz Invariance Violation from observations of astrophysical sources

2017· preprint· en· W2765757925 on OpenAlexaff
Leyre Nogués, Tony T.Y. Lin, C. Perennes, A. Gent, J. Bolmont, M. Gaug, A. Jachołkowska, Manel Martinez, A. N. Otte, Robert Wagner, J. E. Ward, B. Zitzer

Bibliographic record

VenueProceedings of 35th International Cosmic Ray Conference — PoS(ICRC2017) · 2017
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsCherenkov radiationPhysicsMAGIC (telescope)RedshiftCherenkov Telescope ArrayAstrophysicsLorentz covarianceAstronomyLorentz factorLorentz transformationGalaxyOptics

Abstract

fetched live from OpenAlex

Imaging Atmospheric Cherenkov Telescopes study the highest energy (up to tens of TeV) photon emission coming from nearby and distant astrophysical sources, thus providing valuable results from searches for Lorentz Invariance Violation (LIV) effects. Highly variable, energetic and distant sources such as Pulsars and AGNs are the best targets for the Time-of-Flight LIV studies. However, the limited number of observations of AGN flares or of high-energy pulsed emission greatly restricts the potential of such studies, especially any potential LIV effects as a function of redshift. To address these issues, an inter-experiment working group has been established by the three major collaborations taking data with Imaging Atmospheric Cherenkov Telescopes (H.E.S.S., MAGIC and VERITAS) with the aim to increase sensitivity to any effects of LIV, together with an improved control of systematic uncertainties, by sharing data samples and developing joint analysis methods. This will allow an increase in the number of available sources and to perform a sensitive search for redshift dependencies. This presentation reviews the first combined maximum likelihood method analyses using simulations of published source observations done in the past with H.E.S.S., MAGIC and VERITAS. The results from analyses based on combined maximum likelihood methods, the strategies to deal with data from different types of sources and instruments, as well as future plans will be presented.

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 score0.335
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 head0.111
GPT teacher head0.343
Teacher spread0.231 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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