Search for low-mass dijet resonances using trigger-level jets with the ATLAS detector in $pp$ collisions at sqrt(s)=13 TeV
Bibliographic record
Abstract
Searches for dijet resonances with sub-TeV masses using the ATLAS detector at the Large Hadron Collider can be statistically limited by the bandwidth available to inclusive single-jet triggers, whose data-collection rates at low transverse momentum are much lower than the rate from Standard Model multijet production. This data refers to a search for dijet resonances using a strategy called "Trigger-object Level Analysis" (TLA) in ATLAS, where the limitation on high-rate events that can be used for the search is overcome by recording only the event information calculated by the jet trigger algorithms, thereby allowing much higher event rates with reduced storage needs. There are two event selections leading to two signal regions in the search, one with |y*| < 0.3 and one with |y*| < 0.6. The definition of y* is (y1-y2)/2, where y1 and y2 are the rapidities of the highest- and second-highest-pT trigger-level jets. The event selection for the |y*| < 0.3 region is: - highest-pT-jet > 185 GeV - second-highest-pT-jet > 85 GeV - dijet invariant mass > 400 GeV - |y*| < 0.3 The event selection for the |y*| < 0.6 region is: - highest-pT-jet > 220 GeV - second-highest-pT-jet > 85 GeV - dijet invariant mass > 531 GeV - |y*| < 0.6 The background is calculated using two different fit functions, using a sliding window algorithm described in the paper. The systematic uncertainties on the background account for the choice of the fit function (called "sys, fit function") and for the uncertainties on the fit parameters due to the statistical error of the data (called "sys, fit parameters"). The fit function uncertainty is a one-sided systematic uncertainty. No significant excess with respect to the background prediction is found in the data. The results is interpreted in the paper in terms of a leptophobic mediator of dark matter as in arXiv:1507.00966, here we report the 95% CL limit on Gaussian-shaped resonant processes that can be used to constrain generic resonant processes as described in Appendix A of arXiv:1407.1376.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".