Bulk and Surface Charge Collection: CDMS Detector Performance and Design Implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it