Study of Microdosimetric Energy Deposition Patterns in Tissue-Equivalent Medium Due to Low-Energy Neutron Fields Using a Graphite-Walled Proportional Counter
Bibliographic record
Abstract
To improve radiation protection dosimetry for low-energy neutron fields encountered in nuclear power reactor environments, there is increasing interest in modeling neutron energy deposition in metrological instruments such as tissue-equivalent proportional counters (TEPCs). Along with these computational developments, there is also a need for experimental data with which to benchmark and test the results obtained from the modeling methods developed. The experimental work described in this paper is a study of the energy deposition in tissue-equivalent (TE) medium using an in-house built graphite-walled proportional counter (GPC) filled with TE gas. The GPC is a simple model of a standard TEPC because the response of the counter at these energies is almost entirely due to the neutron interactions in the sensitive volume of the counter. Energy deposition in tissue spheres of diameter 1, 2, 4 and 8 µm was measured in low-energy neutron fields below 500 keV. We have observed a continuously increasing trend in microdosimetric averages with an increase in neutron energy. The values of these averages decrease as we increase the simulated diameter at a given neutron energy. A similar trend for these microdosimetric averages has been observed for standard TEPCs and the Rossi-type, TE, spherical wall-less counter filled with propane-based TE gas in the same energy range. This implies that at the microdosimetric level, in the neutron energy range we employed in this study, the pattern of average energy deposited by starter and insider proton recoil events in the gas is similar to those generated cumulatively by crosser and stopper events originating from the counter wall plus starter and insider recoil events originating in the sensitive volume of a TEPC.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".