Characterization of the background spectrum in DAMIC at SNOLAB
Bibliographic record
Abstract
We construct the first comprehensive radioactive background model for a dark matter search with charge-coupled devices (CCDs). We leverage the well-characterized depth and energy resolution of the DAMIC at SNOLAB detector and a detailed geant4-based particle-transport simulation to model both bulk and surface backgrounds from natural radioactivity down to $50\text{ }\text{ }{\mathrm{eV}}_{\mathrm{ee}}$. We fit to the energy and depth distributions of the observed ionization events to differentiate and constrain possible background sources, for example, bulk $^{3}\mathrm{H}$ from silicon cosmogenic activation and surface $^{210}\mathrm{Pb}$ from radon plate-out. We observe the bulk background rate of the DAMIC at SNOLAB CCDs to be as low as $3.1\ifmmode\pm\else\textpm\fi{}0.6\text{ }\text{ }\mathrm{counts}\text{ }{\mathrm{kg}}^{\ensuremath{-}1}\text{ }{\mathrm{day}}^{\ensuremath{-}1}\text{ }{\mathrm{keV}}_{\mathrm{ee}}^{\ensuremath{-}1}$, making it the most sensitive silicon dark matter detector. Finally, we discuss the properties of a statistically significant excess of events over the background model with energies below $200\text{ }\text{ }{\mathrm{eV}}_{\mathrm{ee}}$.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".