Analytic description and optimization of magneto-optical Kerr setups with photoelastic modulation
Bibliographic record
Abstract
Instruments based on the magneto-optical Kerr effect are routinely used to probe surface magnetic properties. These tools rely on the characterization of the polarization state of reflected light from the sample to collect information on its magnetization. Here, we present a theoretical optimization of common setups based on the magneto-optical Kerr effect. A detection scheme based on a simple analyzer and photodetector and one made from a polarizing beam splitter and balanced photodetectors are considered. The effect of including a photoelastic modulator (PEM) and a lock-in amplifier to detect the signal at harmonics of the modulating frequency is studied. Jones formalism is used to derive general expressions that link the intensity of the measured signal to the magneto-optical Fresnel reflection coefficients for any orientation of the polarizing optical components. Optimal configurations are then defined as those that allow measuring the Kerr rotation and ellipticity while minimizing nonmagnetic contributions from the diagonal Fresnel coefficients in order to improve the signal-to-noise ratio (SNR). The expressions show that with the PEM, setups based on polarizing beam splitters inherently offer a twofold higher signal than commonly used analyzers, and the experimental results confirm that the SNR is improved by more than 150%. Furthermore, we find that while all proposed detection schemes measure Kerr effects, only those with polarizing beam splitters allow measuring the Kerr rotation directly when no modulator is included. This accommodates, for instance, time-resolved measurements at relatively low laser pulse repetition rates. Ultrafast demagnetization measurements are presented as an example of such applications.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".