Calorimeter for Real-Time Dosimetry of Pulsed Ultra-High Dose Rate Electron Beams
Bibliographic record
Abstract
An aluminium calorimeter was investigated as a possible real-time dosimeter for electron beams with ultra-high dose per pulse (DPP) as clinical applied at FLASH radiation therapy (1.5 Gy/pulse). Ion chambers, the most widely used active dosimeter type in conventional external beam radiation therapy, suffer very large ion recombination losses at these conditions. Passive dosimeters, as e.g. alanine, are independent of dose rate but do not provide real-time readout. In this work it is shown that the response of alanine is independent of the DPP in the investigated ultra-high DPP range (up to 2.3 Gy/pulse). Alanine dose measurements were then used to determine the ion recombination correction for an Advanced Markus parallel-plate ion chamber at ultra-high DPP. Ion collection losses larger than 50 % were observed. Therefore, ion chambers are not considered suitable for accurate dosimetry in FLASH radiation therapy. As alternative an aluminium open-to-atmosphere calorimeter, operated in quasi-adiabatic mode was investigated at ultra-high DPP electron radiation. The beam pulse charge, and thus the DPP, was varied to evaluate the linearity of the calorimeter response in the DPP range between 0.3 and 1.8 Gy/pulse. On average, the standard deviation of the calorimeter response was 0.1 %. The response was proportional to the DPP in the investigated range. The average deviation of
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".