Searching for Beyond the Standard Model Phenomena in Dijet Events with at Least One Lepton with the ATLAS Detector
Bibliographic record
Abstract
A search for new resonances in events with at least one isolated charged lepton ($e$ or $\mu$) and two jets is performed using 139 fb$^{−1}$ of $\sqrt{s}$=13 TeV proton-proton collision data recorded by the ATLAS detector at the LHC. Deviations from a smoothly falling background hypothesis are tested in three- and four-body invariant mass distributions constructed from leptons and jets. The inclusion of leptons in dijet invariant masses provides two main benefits. Firstly, lepton triggers can be used to search for resonance masses starting from 500 GeV, as the transverse momentum thresholds on lepton triggers can be as low as 24 GeV, while inclusive dijet searches are limited to resonance masses greater than 1 TeV due to jet-based triggers which have a minimum jet transverse momentum of about 400 GeV. Secondly, this search in new final states provides sensitivity to new signal models. The search presented in this thesis considers four different search channels in invariant mass distributions composed of two jets and two leptons, as well as two jets plus one lepton where either zero, one or both jets are identified as originating from bottom quarks. The largest deviation between the observed data and the estimated Standard Model background was found in the two jet plus one lepton channel at a invariant mass of 1.3 TeV with a global statistical significance of 1.5 standard deviations. In the absence of a statistically significant deviation from the background estimate, upper exclusion limits are set at a 95\% confidence level in each signal channel on generic Gaussian-shaped resonances with different widths and masses, as well as in the context of four new signal models.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".