LEGEND-1000 Preconceptual Design Report
Bibliographic record
Abstract
We propose the construction of LEGEND-1000, the ton-scale Large Enriched Germanium Experiment for Neutrinoless $ββ$ Decay. This international experiment is designed to answer one of the highest priority questions in fundamental physics. It consists of 1000 kg of Ge detectors enriched to more than 90% in the $^{76}$Ge isotope operated in a liquid argon active shield at a deep underground laboratory. By combining the lowest background levels with the best energy resolution in the field, LEGEND-1000 will perform a quasi-background-free search and can make an unambiguous discovery of neutrinoless double-beta decay with just a handful of counts at the decay $Q$ value. The experiment is designed to probe this decay with a 99.7%-CL discovery sensitivity in the $^{76}$Ge half-life of $1.3\times10^{28}$ years, corresponding to an effective Majorana mass upper limit in the range of 9-21 meV, to cover the inverted-ordering neutrino mass scale with 10 yr of live time.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.478 | 0.357 |
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".