Study on the determination of the sensitivity in measuringthe isotope 210-Pb in samples using CCDs
Bibliographic record
Abstract
The search for dark matter using Charge-Coupled Devices (CCDs) requires a careful study of the background, since background signals can easily obscure or imitate the traces left by possible interactions of dark matter particles with the detector. High-resistivity silicon CCD technology has proven to be fundamental for performing precise radiopurity measurements and for building detailed background models, particularly in dark matter search experiments such as DAMIC at SNOLAB (Underground Laboratory, Canada), and it will be in future setups like the LBC and DAMIC-M, both located at the Underground Laboratory of Modane (France). The excellent spatial and energy resolution allows for detailed background identification, which for dark matter search will be used to reject these events. In our case, it is used to do radiopurity measurements. One of the most problematic sources of background in dark matter or neutrino experiments comes from the decay of radioactive isotopes present in the materials surrounding the detector. Among them, lead-210 is one of the most difficult to measure with the precision required in these experiments by current assay techniques. At DAMIC@SNOLAB, an upper limit on the bulk of the detector with ²¹⁰Pb was set at < 160 μBq/kg, which remains the most stringent limit to date. At Canfranc Underground Laboratory, we are working on setting a radiopurity service based on these features. We are planning to build a CCD test stand to perform these measurements. For this purpose, a cryostat with a vacuum chamber and a cryocooler with internal and external shielding will be designed. This project will try to determine the lowest sensitivity to ²¹⁰Pb in different radiative samples using various shielding configurations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".