Bibliographic record
Abstract
As liquid argon (LAr) detectors are made at progressively larger sizes, accurate models of LAr optical properties become increasingly important for simulating light transport, understanding signals, and developing analyses. The refractive index, group velocity, and Rayleigh scattering length are particularly important for vacuum ultraviolet (VUV) and visible photons in detectors with diameters much greater than one meter. While optical measurements in the VUV are sparse, recent measurements of the group velocity of 128 nm photons in LAr provide valuable constraints on these parameters. These calculations are further complicated by the dependence of optical parameters on thermodynamic properties that might fluctuate or vary throughout the argon volume. This manuscript presents the model used by DEAP-3600, a dark matter direct detection experiment at SNOLAB using a 3.3 tonne LAr scintillation counter. Existing data and thermodynamic models are synthesized to estimate the wavelength-dependent refractive index, group velocity, and Rayleigh scattering length within the detector, and parameters' uncertainties are estimated. This model, along with in situ measurements of LAr scintillation properties, is benchmarked against data collected in DEAP-3600, providing a method for modeling optical properties in large LAr detectors and for propagating their uncertainties through downstream simulations. Updates are also presented of the Noble Element Simulation Technique (NEST) software, widely used to model scintillation and ionization signals in argon- and xenon-based detectors.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".