Radiopurity of a kg-scale PbWO$$_4$$ cryogenic detector produced from archaeological Pb for the RES-NOVA experiment
Bibliographic record
Abstract
Abstract RES-NOVA is a newly proposed experiment for detecting neutrinos from astrophysical sources, mainly Supernovae, using an array of cryogenic detectors made of PbWO $$_4$$ 4 crystals produced from archaeological Pb. This unconventional material, characterized by intrinsic high radiopurity, enables low-background levels in the region of interest for the neutrino detection via Coherent Elastic neutrino-Nucleus Scattering (CE $$\nu $$ ν NS). This signal lies at the detector energy threshold, O(1 keV), and it is expected to be hidden by naturally occurring radioactive contaminants of the crystal absorber. Here, we present the results of a radiopurity assay on a 0.84 kg PbWO $$_4$$ 4 crystal produced from archaeological Pb operated as a cryogenic detector. The crystal internal radioactive contaminations are: $$^{232}$$ 232 Th <40 $$\upmu $$ μ Bq/kg, $$^{238}$$ 238 U <30 $$\upmu $$ μ Bq/kg, $$^{226}$$ 226 Ra 1.3 mBq/kg and $$^{210}$$ 210 Pb 22.5 mBq/kg. We also present a background projection for the final experiment and possible mitigation strategies for further background suppression. The achieved results demonstrate the feasibility of realizing this new class of detectors.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".