Bibliographic record
Abstract
SuperCDMS is a direct-detection dark matter (DM) experiment that uses cryogenically cooled germanium and silicon detectors to search for interac- tions between DM particles and detector nuclei, and in this thesis I describe my contributions to the experiment. I start with a brief review of DM and motivate the possibility of its de- tection in underground laboratories with sensitive detectors, and I review the SuperCDMS detector fundamentals. Then I focus on detector develop- ment for the future generation of the experiment, which will deploy an array of detectors at SNOLAB in Sudbury, Canada. Specifically I describe char- acterization of prototype detectors from surface facility testing, and discuss measurements of critical values that determine the detectors’ sensitivity to DM particles, such as the baseline resolution and the phonon collection ef- ficiency. I also describe analysis techniques developed to measure intrinsic detector noise in a high radiation environment such as a surface test facility. In the final chapters I describe a DM search analysis using four months of data from operation of SuperCDMS detectors in the Soudan Mine in northern Minnesota. I discuss how a particular detector operating mode, called CDMSlite, lowers the energy threshold of the detectors in order to improve the sensitivity to low-mass DM particles. I also present new analysis techniques that optimize the sensitivity to low-mass DM particles, including noise discrimination with multivariate classifiers, instrumental background modeling, and a profile likelihood signal and background fitting approach. In this analysis we set an upper limit on the DM-nucleon scattering cross section in germanium that is a factor of 2.5 improvement over the previous CDMSlite result for a DM mass that is five times the proton rest mass.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".