Fiber Fabry–Perot astrophotonic correlation spectroscopy for remote gas identification and radial velocity measurements
Bibliographic record
Abstract
We present a novel, to the best of our knowledge, remote gas detection and identification technique based on correlation spectroscopy with a piezoelectric tunable fiber-optic Fabry–Perot filter. We show that the spectral correlation amplitude between the filter transmission window and gas absorption features is related to the gas absorption optical depth, and that different gases can be distinguished from one another using their correlation signal phase. Using a previously captured telluric-corrected high-resolution near-infrared spectrum of Venus, we show that the radial velocity of Venus can be extracted from the phase of higher order harmonic lock-in signals. This correlation spectroscopy technique has applications in the detection and radial velocity determination of weak spectral features in astronomy and remote sensing. We experimentally demonstrate a remote <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">C</mml:mi> <mml:mi mathvariant="normal">O</mml:mi> </mml:mrow> </mml:mrow> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> </mml:math> detection system using a lock-in amplifier, fiber-optic Fabry–Perot filter, and single channel avalanche photodiode.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".