SEARCHING FOR NEW HIGH MASS PHENOMENA DECAYING TO MUON PAIRS USING PROTON-PROTON COLLISIONS AT sqrt(s) = 13 TEV WITH THE ATLAS DETECTOR AT THE LHC
Bibliographic record
Abstract
The Standard Model (SM) of particle physics is a very successful predictive theory which explains the fundamental interactions of elementary particles in the universe, except for gravity. However, the SM is known to be an effective theory that is valid only in a low energy regime, called the electroweak scale, and does not account for many observed experimental results. For example, it does not offer a satisfying explanation for neutrino masses or dark matter. Hence, it is clear that to fully understand and explain nature, a theoretical framework that goes beyond the Standard Model (BSM) is required. While high mass resonances do not offer a complete solution to the problems mentioned above, many BSM theories predict their existence. To name a few, extra dimensional models, grand unified theories, and supersymmetric models all have the common goal of reconciling the very different scales of electroweak symmetry breaking and high mass scales, and predict the existence of high mass resonances. Thus, finding high mass resonances would help validate these theories, which do offer solutions to the aforementioned problems. This analysis focuses on searching for new high mass phenomena using the latest data collected by the ATLAS detector at the LHC, which has an unprecedented centre-of-mass energy of 13 TeV and corresponds to 36.1 fb$^{-1}$ at $\\sqrt{s}$ = 13 TeV. The search is conducted for both resonant and non-resonant new phenomena in dimuon final states. The dimuon invariant mass spectrum is the discriminating variable used in this search. No significant deviations from the Standard Model expectation are observed. Lower limits are set on the signal parameters of interest at 95% credibility level, using a Bayesian interpretation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".