Performance of bare high-purity germanium detectors in liquid argon for the GERDA experiment
Bibliographic record
Abstract
The GERmanium Detector Array, GERDA, will search for neutrinoless double beta decay in76Ge at the National Gran Sasso Laboratory of the INFN. Bare high-purity germanium detectors enriched in76Ge will be submerged in liquid argon serving simultaneously as a shield against external radioactivity and as a cooling medium. In GERDA Phase-I, reprocessed enriched-Ge detectors, which were previously operated by the Heidelberg-Moscow and IGEX collaborations, will be redeployed. Before operating the enriched detectors, tests are performed with non-enriched bare HPGe detectors in the GERDA underground Detector Laboratory to test the Phase-I detector assembly, the detector handling protocols, the refurbishment technology and to study the long-term stability in liquid argon. The leakage currents in liquid argon and liquid nitrogen have been extensively studied under varying gamma irradiation conditions. In total three non-enriched high-purity p-type prototype germanium detectors have been operated successfully. The detector performance is stable over the long-term measurements. For the first time, performance of bare high-purity germanium detectors in liquid argon is reported.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".