Study of bandwidth effects in monochromator-based spectral responsivity measurements
Bibliographic record
Abstract
The errors caused by monochromator bandwidth in spectral responsivity measurements with a monochromator-based apparatus are discussed. Bandwidth effects are not negligible in high-accuracy cryogenic radiometer-based calibrations. A simple numerical method is used to calculate bandwidth effects for different types of detectors, monochromator slit scattering functions, and monochromator output spectral distributions. The method uses low-order Lagrange polynomials fitted segmentwise to measured spectral responsivity and monochromator spectral distribution data in order to make the calculations. It is shown that the shape of the slit function has only a small influence on the bandwidth errors, whereas the output spectral distribution of the monochromator can strongly affect bandwidth errors. It is also shown that in most cases the magnitude of bandwidth effects will vary as the square of the bandwidth. Bandwidth error calculations are presented for various types of detectors (silicon, silicon trap, germanium, InGaAs), for a V(X) detector, and for a typical filter radiometer. A comparison is made between calculated and measured bandwidth effects to validate the method used. In general, calculations of bandwidth effects will be mostly useful for determining uncertainties associated with monochromator bandwidth in spectral responsivity measurements; however, in certain cases the calculations can be used to apply corrections for such effects.
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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 | 0.003 | 0.014 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".