LEGEND-1000 Preconceptual Design Report
Bibliographic record
Abstract
Parallel talk presented at the XXI International Workshop on Neutrino Telescopes - Padova 29 September - 3 October 2025 (https://agenda.infn.it/event/44606/) On behalf of the LEGEND Collaboration Abstract: The LEGEND experiment searches for the neutrinoless double-beta (0νββ) decay of Ge-76 using isotopically-enriched high-purity germanium (HPGe) detectors with the ultimate discovery sensitivity beyond a half-life of 10^28 years. The project is conducted in stages. The first one, LEGEND-200, was steadily accumulating physics data at LNGS (Laboratori Nazionali del Gran Sasso, Italy) for more than one year with 140 kg of HPGe detectors. In 2024 the Collaboration unblinded the first data to check the sensitivity of the experiment and study the composition of the LEGEND-200 background, which was slightly higher than predicted based on screening measurements of the components. The detector array was subsequently disassembled to investigate the source of the elevated background, with nearby components undergoing re-cleaning and/or replacement as necessary. LEGEND-200 is scheduled to resume data taking in 2025. In this talk we will present the performance of the ongoing experiment and give an update on the status of its second phase: LEGEND-1000. Funds: This work is supported by the U.S. DOE and the NSF, the LANL, ORNL and LBNL LDRD programs; the European ERC and Horizon programs; the German DFG, BMBF, and MPG; the Italian INFN; the Polish NCN and MNiSW; the Czech MEYS; the Slovak RDA; the Swiss SNF; the UK STFC; the Canadian NSERC and CFI; the LNGS and SURF facilities.
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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 | 0.006 | 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".