Low-threshold response of a scintillating xenon bubble chamber to nuclear and electronic recoils
Bibliographic record
Abstract
A device filled with pure xenon first demonstrated the ability to operate simultaneously as a bubble chamber and scintillation detector in 2017. Initial results from data taken at thermodynamic thresholds down to $\ensuremath{\sim}4\text{ }\text{ }\mathrm{keV}$ showed sensitivity to $\ensuremath{\sim}20\text{ }\text{ }\mathrm{keV}$ nuclear recoils with no observable bubble nucleation by $\ensuremath{\gamma}$-ray interactions. This paper presents results from further operation of the same device at thermodynamic thresholds as low as 0.50 keV, hardware limited. The bubble chamber has now been shown to have sensitivity to $\ensuremath{\sim}1\text{ }\text{ }\mathrm{keV}$ nuclear recoils while remaining insensitive to bubble nucleation by $\ensuremath{\gamma}$-rays. A data-driven calibration of the chamber's nuclear recoil nucleation response, as a function of nuclear recoil energy and thermodynamic state, is presented. Stringent upper limits are established for the probability of bubble nucleation by $\ensuremath{\gamma}$-ray-induced Auger cascades, with a limit of $<1.1\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}6}$ set at 0.50 keV, the lowest thermodynamic threshold explored.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".