A frequency-modulated laser interferometer for nanometer-scale position sensing at cryogenic temperatures
Bibliographic record
Abstract
The continually increasing sensitivity required for advancement of far-infrared astronomy dictates that the next generation of space-based observatories must employ cryogenically cooled telescopes and instruments. Cryogenic operation of interferometers such as those proposed for future space missions poses particular challenges, including the need for robust low power dissipation cryogenic position metrology. Instrumentation must be cooled to <4 K to avoid a noise contribution from self-emission and often contain moving components whose position must be measured precisely at cryogenic temperatures. In 2018, we reported on the development of a three-phase fiber-fed laser homodyne interferometer for optical position metrology that achieved a displacement uncertainty of 2.3 nm RMS at 4 K. In that design, one arm of the interferometer had an additional 2 m of optical fiber to carry the probe signal to the 4 K work space. Subsequently, a 2 m, armored, differential fiber pair was developed to balance the lengths of the probe and reference interferometric beams that were subject to thermal gradients. Although this led to an improved dynamic performance in the measurement of an oscillating target, low velocity performance was limited by 1/f noise in the photodetector circuit. Building on that work, we present the design and review the performance of a new frequency-modulated laser interferometer system we have developed that improves upon the three-phase system by eliminating the need for a differential fiber pair in cryogenic applications and achieves 29 nm RMS uncertainty for mechanical displacement velocities from 0 to ~4 mm/s.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".