Pushing into the Unknown: A Search for light long-lived particles with the ATLAS detector
Bibliographic record
Abstract
New long-lived particles (LLPs) are predicted by various extensions of the Standard Model that address unresolved questions such as dark matter, neutrino masses, and the matter-antimatter asymmetry in the universe. These LLPs can be investigated at the Large Hadron Collider (LHC). LLPs exhibit distinctive detector signatures, and require novel search strategies and analysis techniques to aid in their discovery. This thesis investigates a search for light, pseudoscalar LLPs using 140, $fb^{−1}$ of proton-proton collision data collected by the ATLAS detector from 2015 to 2018 at $\sqrt{s} = 13 TeV$. The search focuses on hadronically decaying LLPs with masses between 5 and 55 GeV, with bench- mark models including both Higgs boson decays to LLP pairs ($H \to ss$) and axion-like particle (ALP) models. No significant excess above the expected background is observed. Upper limits areplaced on the branching ratio of Higgs boson to pairs of LLPs, the cross-section for ALPs produced in association with a vector boson, and, for the first time, on the branching ratio of the top quark to an ALP and a u/c quark. The obtained limits for the $H \to ss$ model are the most stringent so far for LLP masses ms < 40, GeV and lifetimes of $10^{−1} –10^{-3}$ m. This thesis also presents the performance of the large-radius tracking algorithm, which was optimized for Run 3 data-taking period. This algorithm is an additional pass of the track reconstruction process with relaxed collision vertex pointing requirements to improve sensitivity to LLPs in the ATLAS inner detector. Additionally, a transformer-based graph neural network algorithm is also studied in this thesis for hard-scatter vertex selection and is found to improve selection efficiency by approximately 5–10% compared to the existing approach for various physics processes.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".