Bibliographic record
Abstract
DEAP-3600 is a direct dark matter detector using ∼ 3300 kg of liquid argon, located 2 km underground at SNOLAB.It is a single phase liquid argon scintillator using pulse-shape discrimination techniques to remove backgrounds.This allows for the sensitivity required to measure spin-independent interactions with Weakly Interacting Massive Particles (WIMPs), a leading dark matter candidate.Particles entering the detector produce scintillation light which is guided into one of the detector's 255 photomultiplier tubes.Calibration sources for gammas, betas and neutrons are used to obtain the light yield of different event types.The energy of an event is then determined by counting the number of photoelectrons while knowing the associated light yield.In order to properly calibrate the energy measured in the detector, a new corrected energy variable has been developed for the DEAP-3600 analysis using a three year data set.The new energy variable corrects for the number of working photomultiplier tubes and time drift with a RMS uncertainty of 0.096%.The uncertainty of the energy due to position dependence is constrained to be < 3.5% between the center and the edge of the detector.The effect of the energy scale non-linearity has been determined up to ∼ 10 MeV, with the uncertainty constrained to be < 7.3%.The overall residual uncertainty of the measured energy is constrained to be < 8.1%, assuming no correlations between the dependencies.Comparisons between data and Monte Carlo generated events were also done to validate the simulation model.i you are proud of me and I really wish you could see how much I have grown thanks to you.Lastly, Mon Amour, without you I would not have the life that I have now.You mean the world to me and I cannot express enough how much you have changed
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.007 | 0.004 |
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".