LEGEND-1000 - a next generation detector for searches of neutrino-less double beta decay
Bibliographic record
Abstract
The LEGEND experiment has been designed to search for neutrino-less double-beta decay in 76Ge. Its discovery would have profound implications for neutrino physics and cosmology providing unambiguous evidence for the Majorana nature of neutrinos, lepton number non-conservation and the absolute neutrino mass scale. The LEGEND-1000 detector represents the ton-scale phase of the LEGEND program, following the current intermediate stage, LEGEND-200, carried out at LNGS in Italy. The LEGEND-1000 will be based on p-type, inverted-coaxial, point-contact germanium detectors enriched in 76Ge up to about 90 %. The detectors will be operated in an active shield based on underground argon. This gas is extracted from an underground source and it is depleted in 39Ar and 42Ar. This approach proved to guarantee the lowest background levels and the best energy resolution at the decay Q value as established by the GERDA and MAJORANA DEMONSTRATOR experiments. The anticipated quasi background-free operation will allow the search for neutrino-less double-beta decay in 76Ge at a half-life beyond 1028 yr and a discovery sensitivity spanning the inverted-ordering neutrino mass scale. The LEGEND Collaboration is successfully seeking funding from US and European agencies. The construction of the detector in Hall C of the underground laboratory of LNGS in Italy should start early 2027 and will take about 8 years. Start of data taking is foreseen for 2031.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".