Hot model of muonium formation in liquids
Bibliographic record
Abstract
The mechanism of formation of muonium atoms from positive muons was studied here through measurements of the yield of diamagnetic muon states in dipolar aprotic solvents and for scavenger solutions in hexane and methanol. The results are compared with published data on common solvents covering a full range of the physicochemical properties of liquids that affect an ionic formation mechanism, namely their static dielectric constants, electron mobilities, and radiolysis yields of electrons. It is concluded that muonium is not formed by a thermal charge-neutralization reaction in these chemically-active media, though that mechanism does contribute to muonium formation in inert media like liquefied noble gases. It is clear that muonium materializes on a much shorter timescale than the recently proposed "delayed" mechanism (microseconds) and the earlier "spur" model (nanoseconds). In contrast, the data referring to all these liquids are consistent with the intra-track "hot" model. This is the only Mu-formation model proposed so far in which the immediate precursors of Mu (Mu(hot)) are neither scavengable nor ionic.Key words: muonium atoms, formation mechanism, hot model, spur model, delayed-muonium-formation model, diamagnetic yields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".