$^{76}$Ge Detectors of LEGEND experiment: Production, Characterization, Performance
Bibliographic record
Abstract
The LEGEND Collaboration advances an experimental program to search for the neutrinoless double-beta decay of $^{76}$Ge. LEGEND-200, the first stage of this program, recently completed its commissioning process at LNGS in Italy. About 140~kg of $^{76}$Ge-enriched high-purity germanium detectors immersed in liquid argon are now continuously taking low background data. The LEGEND experiment integrates the advanced technology of the germanium detectors used in the GERDA and MAJORANA experiments. They are well suited for $\gamma$-rays measurements at the MeV energy scale, yielding high detection efficiency. The crystal growing procedure results in naturally low internal radioactivity and is a well-established technology. A precise understanding of the behavior of the germanium detectors is fundamental to determine their optimal operational parameters and it necessitates extensive detector characterization. This contribution will present the latest state-of-the-art approach to the production chain, the characterization measurements, and the performance of germanium detectors installed in LEGEND-200 so far.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".