On-sky demonstration at Palomar Observatory of the near-IR, high-resolution VIPA spectrometer
Bibliographic record
Abstract
A near-IR high-resolution, R≈80000 spectrometer has been developed at IPAG to directly characterize the atmosphere of exoplanets using adaptive optics (AO) assisted telescopes, and a single-mode fiber-injection unit. A first technical test with the 200’ Hale telescope at Palomar Observatory occurred in March 2022 using the PALM3000 AO system offered by this telescope. Observations have also been made at the same time with the PARVI spectrometer so that a direct comparison can be made between the two instruments. This spectrometer uses a virtually imaged phased array (VIPA) instead of an echelle grating, resulting in a very compact optical layout that fits in a 0.25m3 cryostat. Using a quarter of an H2RG detector, the spectrometer analyses the middle part of the H-band, from 1.57 to 1.7 microns for 2 sources whose light is transferred from the telescope to the spectrometer using single-mode fibers. By design, the transmission of the spectrometer is expected to be 40-50%, which is 2-3 times higher than the transmission of current high-resolution spectrometers such as CRIRES+ and NIRSPEC. A damaged cross-disperser limited it to 21%, however. A replacement grating with a correct, twice as high efficiency has been procured after the on-sky demonstration. In addition to recalling the main specifications of the VIPA spectrometer, this paper presents the control software, the calibration process, and the reduction pipeline that have been developed for the instrument. It also presents the results of the on-sky technical test with the Hale telescope, as well as measurements of the effective resolution and transmission, along with a comparison of a spectrum of the sun obtained with the spectrometer with the BASS2000 reference spectrum. Planned modifications are also discussed. That includes the integration of a new dedicated H2RG detector, and of K-band optics.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".