Bulk and Surface Charge Collection: CDMS Detector Performance and Design Implications
Bibliographic record
Abstract
The Cryogenic Dark Matter Search (CDMS) searches for Weakly Interacting Massive Particles (WIMPs) with cryogenic germanium particle detectors.These detectors discriminate between nuclear-recoil candidate and electron-recoil background events by collecting both phonon and ionization energy from interactions in the crystal.Incomplete ionization collection results in the largest background in the CDMS detectors as this causes electron-recoil background interactions to appear as false candidate events.Two primary causes of incomplete ionization collection are suface and bulk charge trapping.Recent work has been focused on reducing surface trapping through the modification of fabrication methods for future detectors.Analyzing data taken with test devices shows that hydrogen passivation of the amorphous silicon blocking layer does not reduce the effects of surface trapping.Other data shows that the iron-ion implantation used to lower the critical temperature of the tungsten transition-edge sensors increases surface trapping, causing a degradation of the ionization collection.Using selective implantation on future detectors may improve ionization collection for events near the phonon side detector surface.Bulk trapping is minimized by neutralizing ionized lattice impurities.Detector investigations at testing facilities and at the experimental site in Soudan, MN have provided methods to optimize the neutralization process and monitor running conditions to maintain maximal ionization collection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".