Low-Energy Radiative Backgrounds in CCD-Based Dark-Matter Detectors
Bibliographic record
Abstract
The reach of sub-GeV dark-matter detectors is at present severely affected by low-energy events from various origins. We present the theoretical methods to compute the single- and few-electron events that arise from secondary radiation emitted by high-energy particles passing through detector materials and perform simulations to quantify them at (Skipper) CCD-based experiments, focusing on the SENSEI data collected in the MINOS cavern at Fermilab. The simulations account for the generation of secondaries from Cherenkov and luminescent recombination; photo-absorption, reflection, refraction and thin-film interference in detector materials; roughness of the interfaces and the dynamics of charges and partial charge collection (PCC) in the doped CCD-backside. We consider several systematic uncertainties, notably those stemming from the backside charge-diffusion modeling, which we estimate with a "fiducial'' and an "extreme'' model, with the former model presenting better agreement with PCC data. We find that Cherenkov photons constitute about 40% of the observed single-electron events for both models; radiative recombination rates are negligible for the fiducial model, but can dominate over the Cherenkov rates for the extreme model. We also estimate the fraction of 2-electron events from 1-electron event same-pixel coincidences, finding that the entire 2-electron rate can be explained by coincidences of radiative events and spurious charge. Accounting for backgrounds, we project the sensitivity of future Skipper-CCD-based experiments to different dark-matter models. For light-mediator models with dark-matter masses of 1, 5, and 10 MeV, we find that future experiments with 10-kg-year exposures and successful background mitigation could have a sensitivity that is larger by 9, 3, and 2 orders of magnitude, respectively, when compared to an experiment without background improvements. (abridged)
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".