Robust constraints on Lorentz Invariance Violation from H.E.S.S., MAGIC\n and VERITAS data combination
Bibliographic record
Abstract
Gamma-Ray bursts, flaring active galactic nuclei and pulsars are distant and\nenergetic astrophysical sources, detected up to tens of TeV with Imaging\nAtmospheric Cherenkov Telescopes (IACTs). Due to their high variability, they\nare the most suitable sources for energy-dependent time-delay searches related\nto Lorentz Invariance Violation (LIV) predicted by some Quantum Gravity (QG)\nmodels. However, these studies require large datasets. A working group between\nthe three major IACTs ground experiments - H.E.S.S., MAGIC and VERITAS - has\nbeen formed to address this issue and combine for the first time all the\nrelevant data collected by the three experiments in a joint analysis. This\nproceeding will review the new standard combination method. The likelihood\ntechnique used to deal with data from different source types and instruments\nwill be presented, as well as the way systematic uncertainties are taken into\naccount. The method has been developed and tested using simulations based on\npublished source observations from the three experiments. From these\nsimulations, the performance of the method will be assessed and new light will\nbe shed on time delays dependencies with redshift.\n
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".