ABCD and Template Fitting for Background Estimation Using 90 Signal Models in the Search for Emerging Jets at the ATLAS Experiment at the Large Hadron Collider
Bibliographic record
Abstract
This thesis presents an analysis as part of the search for the physical signature known as an emerging jet in the ATLAS detector at the Large Hadron Collider.This hypothetical signature is predicted by a proposed hidden valley sector, called the dark sector, which interacts with the Standard Model via a dark mediator particle, X d .In this sector, the dark particles produce collimated sprays of particles, leading to eventual decay into Standard Model particles and the production of jet objects.This may occur at displaced points from the original interaction point, resulting in the sudden emergence of these jets.The analysis uses Monte-Carlo simulated events created at an integrated luminosity of 139 fb -1 and a centre-of-mass energy of s = 13 TeV.This thesis shows the background estimation and sensitivity to the theoretical cross-section of 90 emerging jets signal models found by a data-driven background estimation method.The systematic uncertainties from all sources are also shown.This is done for a model independent approach and a model dependent approach that uses a machine learning technique to create the input signal and background planes.Also presented is a similar study using an MC-driven template fitting method for background estimation and sensitivity calculations.This is done as a direct comparison to the data-driven method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".