Tesseract – a high-stability, low-noise fluxgate sensor designed for constellation applications
Bibliographic record
Abstract
Abstract. Accurate high-precision magnetic field measurements are a significant challenge for many applications, including constellation missions studying space plasmas. Instrument stability and orthogonality are essential to enable meaningful comparison between disparate satellites in a constellation without extensive cross-calibration efforts. Here we describe the design and characterization of Tesseract – a fluxgate magnetometer sensor designed for low-noise, high-stability constellation applications. Tesseract's design takes advantage of recent developments in the manufacturing of custom low-noise fluxgate cores. Six of these custom racetrack fluxgate cores are securely and compactly mounted within a single solid three-axis symmetric base. Tesseract's feedback windings are configured as a four-square Merritt coil to create a large homogenous magnetic null inside the sensor where the fluxgate cores are held in a near-zero field, regardless of the ambient magnetic field, to improve the reliability of the core magnetization cycle. A Biot–Savart simulation is used to optimize the homogeneity of the field generated by the feedback Merritt coils and was verified experimentally to be homogeneous within 0.42 % along the racetrack cores' axes. The thermal stability of the sensor's feedback windings is measured using an insulated container filled with dry ice inside a coil system. The sensitivity over temperature of the feedback windings is found to be between 13 and 17 ppm ∘C−1. The sensor's three axes maintain orthogonality to within at most 0.015∘ over a temperature range of −45 to 20 ∘C. Tesseract's cores achieve a magnetic noise floor of 5 pT √Hz−1 at 1 Hz. Tesseract will be flight demonstrated on the ACES-II sounding rockets, currently scheduled to launch in late 2022 and again aboard the TRACERS satellite mission as part of the MAGIC technology demonstration which is currently scheduled to launch in 2023.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".