Bibliographic record
Abstract
MoEDAL (Monopole and Exotics Detector at the LHC) is the 7th experiment, specifically dedicated to investigating beyond the Standard Model scenarios by searching for highly ionizing particles, such as magnetic monopoles or massive pseudo-stable charged particles and multiply electrically charged particles as messengers of new physics. Sharing the same interaction point as the LHCb experiment, MoEDAL is complementary to the larger ATLAS and CMS experiments, thereby expanding the discovery reach of the LHC. This largely passive detector is comprised of the following subdetectors: A large array of Nuclear Track Detector (NTD) stacks, a magnetic trapping detector (designed to trap both electrically and magnetically charged highly ionizing particles), and a TimePix chip array that monitors particle backgrounds. The MoEDAL Apparatus for Penetrating Particles (MAPP), a new MoEDAL subdetector, is currently being designed and constructed, while the prototype deployed in 2017 is currently being analyzed. The aim of MAPP is to enable MoEDAL to search for fractionally charged particles as well as new long-lived neutrals. The goal of this poster presentation was to summarize the growing physics program of MoEDAL including MAPP’s search for new fractionally charged particles, introduce the detectors and methodology, as well as present MoEDAL’s latest results on magnetic monopole production at the LHC.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".