Long-lived particle decays at the proposed MATHUSLA experiment
Bibliographic record
Abstract
We carefully study the decay and reconstruction of long-lived particle (LLP) decays in the proposed MATHUSLA LLP detector for the HL-LHC. Our investigations are focused on three LLP benchmark models. MATHUSLA’s primary physics target is represented by hadronically decaying LLPs with mass above ∼ 10 GeV , produced in exotic Higgs decays. We also investigate GeV-scale scalar and right-handed neutrino LLPs, which are the target of many other proposed experiments. We first introduce a public astim code to allow for efficient signal-only studies of LLP decays in MATHUSLA and general external LLP detectors. For each of our benchmark scenarios, we carefully simulate LLP production and decay, and make our simulation library publicly accessible for future investigations and comparisons with other experiments. We then systematically study the geometric acceptance of MATHUSLA for LLP decays in these scenarios, and present updated sensitivity projections that include these acceptances. Our results show that the idealized reach of MATHUSLA computed in earlier studies is mostly realized. We also investigate possible ways of increasing the signal acceptance using the inherent geometric flexibility of the FastSim, which will provide useful inputs for realistic experimental and engineering optimization of the detector in the future. Published by the American Physical Society 2024
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".