Low Frequency Excess Noise Source Investigation of Off-Diagonal GMI-Based Magnetometers
Bibliographic record
Abstract
The equivalent magnetic noise spectral densities of off-diagonal giant magnetoimpedance (GMI)-based magnetometers exhibit significant low-frequency excess noise, proportional to 1/f noise. As it represents a serious limitation to the ultimate sensing performances of high sensitivity magnetometers, possible sources of this 1/f noise are under investigation. Low-frequency magnetization fluctuations have been proposed as the noise source in the case of classical GMI-based sensors. Here, we apply this model to off-diagonal GMI-based magnetometers. This requires the inclusion of magnetization fluctuation noise sources, in addition to white noise sources from electronic conditioning in the GMI effect equations. A pessimistic scenario is presented, predicting the upper limit of low-frequency excess noise from material characteristics. The equivalent magnetic noise level is then computed from the sensitivity of each term of the sensing element impedance matrix to the magnetization angle at the static working point (for both axial and circumferential static magnetic field) and to conditioning circuitry. Based on this, it appears that magnetization fluctuations similarly affect all modes of operation of the two-port network sensing element, inducing identical impedance fluctuations. It also appears that this noise depends only upon the static equilibrium condition. This condition is governed by the effective anisotropy of the magnetic wire and by both axial and circumferential static components of the working point.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".