Astrophotonic solutions for spectral cross-correlation techniques
Bibliographic record
Abstract
Using photonic devices, we developed a new approach to traditional spectroscopy where the spectral cross-correlation with a template spectrum can be done entirely on-device. By creating photonic devices with a carefully designed, modulated transmission spectrum, the cross-correlation can be carried out optically without requiring any dispersion, vastly simplifying the instrument and reducing its cost. The measured correlation lag can be used for detecting atomic/molecular species within and determining the radial velocity of a particular astrophysical object. We present an overview of two design approaches that are currently being developed that use different photonic platforms: silicon and fibre-based photonics. The silicon photonic approach utilizes ring resonators that can be thermo-optically modulated to carry out the cross-correlation. The fibre approach uses customized fibre Bragg gratings (FBGs) with transmission spectra that can be strain-modulated. Both approaches have been able to detect molecular gas in a lab setting, and we are now in the process of on-sky testing. Lastly, we discuss the future for these types of devices as their simplicity opens up the possibility of developing low-cost, purpose-built multi-object or integral field spectroscopic instruments that could make significant contributions to scientific programs requiring stellar RV measurements and exoplanet detections.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".