On-chip monolithic Fourier transform spectrometers assisted by cGAN spectral prediction
Bibliographic record
Abstract
Silicon photonic spatial heterodyne Fourier transform spectrometers (SH-FTSs) are attractive with chip-scale monolithic arrays of imbalanced Mach-Zehnder interferometers; however, there exist optical path difference (OPD) errors from the inevitable fabrication imperfection, which will severely distort the retrieved spectra. In this Letter, we propose that a predictive model can be created for rapid and accurate spectral recovery based on the conditional generative adversarial network (cGAN) featuring strong input-on-output supervision, instead of both complicated physical OPD modification and time-consuming iterative spectral calculation. As a demonstration, cGAN spectral prediction was performed for our previously presented dual-polarized SH-FTS with large OPD errors [Opt. Lett.44, 2923 (2019)OPLEDP0146-959210.1364/OL.44.002923]. Due to the strong noise-resistant capability, the cGAN-predicted spectra can stay reliable, even though the signal-to-noise ratio of acquired interferograms dramatically drops from 1000 to 100, implying a lower limit of detection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".