Searching for Dark Matter with the ATLAS Detector in Events with an Energetic Jet and Large Missing Transverse Momentum
Bibliographic record
Abstract
Hadron colliders, such as the Large Hadron Collider (LHC), principally produce events involving hadronic activity. Such activity is typically modelled by jets, which provide a useful representation of the underlying physics. Given the ubiquity of jets in LHC events, it becomes important to ensure that their properties and performance are well understood. The full approach to jet reconstruction and calibration, as used by the ATLAS Experiment, is detailed with a focus on recent improvements. The systematic uncertainties associated with jets are quantified, with the procedures and resulting reductions in uncertainties thoroughly detailed. Extra attention is placed on the treatment of high energy jets, and particularly the impact of inactive calorimeter regions and calorimeter non-containment (punch-through). The mono-jet topology is presented as an analysis where high energy jets are particularly relevant. This search makes use of very high missing transverse momentum balanced purely by jets, enabling measurements of the production cross-section of new physics processes producing weakly interacting particles. The full Standard Model background determination is shown, and the data are seen to be consistent with the Standard Model expectations. Limits are set on the visible cross-section for new physics processes. The results are interpreted as a search for the pair-production of Dark Matter (DM), both through Effective Field Theories (EFTs) and simplified models. Scenarios where the DM is either a scalar or fermionic particle are both considered. The validity of the EFT approach is thoroughly investigated, providing the first complete collider study into all relevant interaction types. Limits are then set on the EFT suppression scale, the WIMP-nucleon scattering cross-section, and the WIMP annihilation cross-section in order to compare with other types of DM experiments. The simplified model is used to conduct a full parameter-space scan of the mono-jet sensitivity to a wide range of conditions. First projections for the mono-jet analysis at an upgraded LHC are presented, demonstrating the significant gain in both discovery potential and limit sensitivity that accompanies higher collision energies. The analysis is projected to double in sensitivity in the coming year, hinting at the exciting times to come.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".