Assay-based background projection for the Majorana Demonstrator using Monte Carlo uncertainty propagation
Bibliographic record
Abstract
The background index (BI) is an important quantity to project and calculate the half-life sensitivity of neutrinoless double-$\ensuremath{\beta}$ decay $(0\ensuremath{\nu}\ensuremath{\beta}\ensuremath{\beta})$ experiments. An analysis framework is presented to calculate the BI using the specific activities, masses, and simulated efficiencies of an experiments components as distributions. This Bayesian framework includes a unified approach to combine specific activities from assay. Monte Carlo uncertainty propagation is used to build a BI distribution from the specific activity, mass, and efficiency distributions. This method is applied to the Majorana Demonstrator, which deployed arrays of high-purity Ge detectors enriched in $^{76}\mathrm{Ge}$ to search for $0\ensuremath{\nu}\ensuremath{\beta}\ensuremath{\beta}$. The original assay-based projection is requantified in the new framework, using the as-built geometry of the Demonstrator and additional assay information. While 47% higher than the original projection, the resulting BI of $[8.95\ifmmode\pm\else\textpm\fi{}0.36]\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}4}\phantom{\rule{3.33333pt}{0ex}}\text{cts/(keV}\phantom{\rule{0.16em}{0ex}}\text{kg}\phantom{\rule{0.16em}{0ex}}\text{yr)}$ from the $^{232}\mathrm{Th}$ and $^{238}\mathrm{U}$ decay chains does not account for the higher-than-expected BI observed by the Demonstrator. This method enables us to demonstrate the statistical incompatibility between the Demonstrator's observed background and the assay results.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".