Simulating barium ion motion in liquid xenon for a future barium tagging upgrade of nEXO
Bibliographic record
Abstract
The neutrino is one of the most abundant elementary particles in the Universe, and plays important roles in proposed answers to many frontier scientific questions, such as matter-antimatter asymmetry, and by what mechanism and processes do massive stars end their lives in supernova explosions. Nevertheless, some of the neutrino’s most basic properties remain poorly understood, in particular, the neutrino’s mass. One avenue to determine the mass of the neutrino and explore its origin is the study of a hypothetical nuclear decay process known as neutrinoless double beta decay (0νββ). The nEXO experiment is a tonne-scale liquid xenon (LXe) time projection chamber that aims to uncover properties of neutrinos via the 0νββ in the isotope Xe-136. The observation of 0νββ would point to new physics beyond the Standard Model and imply lepton number violation, indicating that neutrinos are their own antiparticle and thus change our understanding of the universe. The collaboration has been pursuing the development of Ba-tagging as a potential technique to further improve upon the detection sensitivity of nEXO by detecting the daughter isotope Ba-136, produced from the ββ of Xe-136. This technique aims to extract single daughter Ba ions from a LXe volume. Ba-tagging would allow for an unambiguous identification of true ββ decay events. Various Ba-ion extraction approaches are under investigation by the nEXO collaboration. The groups at TRIUMF and McGill University are developing an accelerator-driven ion source to implant radioactive beam ions inside a LXe volume, for subsequent ion extraction and identification via methods under development by other nEXO collaborators. In the first phase, radioactive ions will be extracted using an electrostatic probe for subsequent identification by γ-spectroscopy. This thesis describes the setup, fluid dynamics and particle ray tracing simulations to study the motion of barium ions in liquid xenon, and ion extraction efficiency simulations using an electrostatic probe.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".