Monte Carlo simulation study of the Fano factor, <i>w</i> value, and energy resolution for the absorption of soft x rays in xenon–neon gas mixtures
Bibliographic record
Abstract
Xenon gas proportional-scintillation counters (GPSC) have many applications in the detection of soft x rays where their energy resolution, R, is comparable to solid-state detectors when large window areas are required. However, R is known to deteriorate for energies Exr below 2–3 keV due to electron loss to the entrance window. Since the addition of a lighter noble gas increases the absorption depth, we have investigated the use of Xe–Ne gas mixtures at atmospheric pressure as detector fillings. The results of a Monte Carlo simulation study of the Fano factor, F, the w value, and the intrinsic energy resolution, R=2.36(Fw/Exr)1/2, are presented for Xe–Ne mixtures and pure Xe and Ne. The results show that the addition of Ne to Xe reduces the intrinsic energy resolution ℛ but this never compensates for the reduction in scintillation yield in GPSC applications, implying that the instrumental energy resolution R will only improve with the addition of Ne when electron loss to the window in pure Xe is significant. The simulation reproduces the photoionization process of the Xe and Ne atoms, the vacancy cascade decay of the residual ions, and the elastic and inelastic scattering of electrons by the gas atoms. The contribution of energy and charge transfer mechanisms such as Penning, associative, and transfer ionization is discussed in detail. It is shown that Penning and associative ionization are the crucial indirect ionization processes which determine the behavior of F and w at low concentrations of Xe. The importance of the nonmetastable Ne states is also assessed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".