Molecular Dynamics Simulations of Bubble Nucleation in a Liquid Scintillator
Bibliographic record
Abstract
The Scintillating Bubble Chamber is a detector experiment searching for Weakly Interacting Massive Particles, with estimated sensitivities as low as 100 eV. Its use of a liquid scintillator causes electron events to deposit most of their energy electronically, rather than into nuclear recoils, which contributes to this low sensitivity. However, existing molecular dynamics simulations of bubble formation do not incorporate the energy losses to photons or time-delayed releases that occur in liquid argon. In this study, we use the HOOMD-blue molecular dynamics framework to simulate an idealized model of electron events in liquid argon, including energy deposited via photon creation, ionization, and direct nuclear recoils. A multi-stage bubble growth process similar to that reported in the literature was observed. When comparing simulated thresholds under this model to those under the standard Seitz ``heat spike" model, we found that scintillation raises the average energy required to form a bubble by a factor of 2.16. This indicates that more energy is lost than is accounted for by photon creation and that thermal energy deposited after the bubble's rapid growth phase does not contribute to nucleation. This conclusion was further strengthened by simulations showing increased thresholds with slower scintillation processes, even at identical thermodynamic conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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) | 0.003 | 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".