Research and development of particle detectors for muon tomography and the CERN ALICE experiment
Bibliographic record
Abstract
The Standard Model of particle physics describes successfully the building blocks of the material and their interactions, which are confirmed by most of the experimental observations. However, there are several open questions, e. g. how the Universe was created or where the missing antimatter is? We can answer these questions by new or upgraded experiments. In these experiments, we apply particle detectors to measure the particles originated from colliders or high-energy cosmic rays. This Ph.D. thesis focuses mainly on the research and development of particle detectors. The ALICE experiment at the Large Hadron Collider (LHC) of the European Laboratory for Particle Physics (CERN), investigates the quark gluon plasma (QGP), which is produced in heavy-ion collisions. ALICE has a complex apparatus consists of tracking, identification and calorimeter detectors. To measure more precisely the properties of QPG, the increase of energy and luminosity of collisions is necessary. This implies the better understanding of the operation of subdetectors of ALICE and their upgrades. The development of the instrumentation and the methods of particle physics led to the appearance of new applications. The muon radiography or muon tomography is an imaging method which is based on cosmic muon tracking. This is applicable to image largesize and high-density bjects. With an appropriate instrument, we can measure the change of the density in volcanoes in real time and predict even their eruptions. If the imaging of low-Z materials can be realised, that led to a non-invasive medical imaging procedure.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".