Background reduction techniques and simulations for the PICASSO and PICO dark matter search experiments.
Bibliographic record
Abstract
Dark matter is one of the most challenging mysteries of cosmology. The detection of Dark Matter is a main objective in modern experimental physics. The PICASSO and then the PICO experiments work towards the direct detection of WIMPs (Weakly Interacting Massive Particles) with superheated liquid detectors. Phase transitions in the superheated liquids are triggered by the nuclear recoil caused by the elastic collision of a dark matter particle. The phase transition in the detectors leads to the formation of a gaseous bubble. The acoustic signal of the nucleation of a bubble is recorded with piezoelectric sensors and pictures of the bubbles are taken with cameras. Many techniques are used to isolate potential dark matter signal from background signals. This thesis presents the work done for the fabrication process and purification techniques of the PICASSO detectors in the clean room at Université de Montréal. The detectors built with these new purification techniques allowed a background reduction of a factor of 10. The techniques and methods will be presented as well as the latest results from the data taken with these detectors. Simulations for the PICO experiment will also be presented. MCNP neutron background simulations from radioactive materials in the PICO detector will be presented. MCNP simulations for neutrons in the SNOLAB drift will also be presented as well as other MCNP simulations for the design and optimisation of the experiment. Finally, Geant4 muon-induced neutron simulations in the SNOLAB drift will be presented as well as Geant4 simulations for the design of a muon veto. These simulations were built entirely from scratch by using the SHIELDING physics list.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".