First combined studies on Lorentz Invariance Violation from observations\n of astrophysical sources
Bibliographic record
Abstract
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 simu- lations 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".