Measuring partial fluorescence yield using filtered detectors
Bibliographic record
Abstract
Typically, X-ray absorption near-edge structure measurements aim to probe the linear attenuation coefficient. These measurements are often carried out using partial fluorescence yield techniques that rely on detectors having photon energy discrimination improving the sensitivity and the signal-to-background ratio of the measured spectra. However, measuring the partial fluorescence yield in the soft X-ray regime with reasonable efficiency requires solid-state detectors, which have limitations due to the inherent dead-time while measuring. Alternatively, many of the available detectors that are not energy dispersive do not suffer from photon count rate limitations. A filter placed in front of one of these detectors will make the energy-dependent efficiency non-linear, thereby changing the responsivity of the detector. It is shown that using an array of filtered X-ray detectors is a viable method for measuring soft X-ray partial fluorescence yield spectra without dead-time. The feasibility of this technique is further demonstrated using α-Fe2O3 as an example and it is shown that this detector technology could vastly improve the photon collection efficiency at synchrotrons and that these detectors will allow experiments to be completed with a much lower photon flux reducing X-ray-induced damage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".