Flux calibration of medium-resolution spectra from 300 nm to 2500 nm: Model reference spectra and telluric correction
Bibliographic record
Abstract
While the near-infrared wavelength regime is becoming more and more important for astrophysics there is a marked lack of spectrophotometric standard star data that would allow the flux calibration of such data. Furthermore, flux calibrating medium- to high-resolution échelle spectroscopy data is challenging even in the optical wavelength range, because the available flux standard data are often too coarsely sampled. We will provide standard star reference data that allow users to derive response curves from 300nm to 2500nm for spectroscopic data of medium to high resolution, including those taken with échelle spectrographs. In addition we describe a method to correct for moderate telluric absorption without the need of observing telluric standard stars. As reference data for the flux standard stars we use theoretical spectra derived from stellar model atmospheres. We verify that they provide an appropriate description of the observed standard star spectra by checking for residuals in line cores and line overlap regions in the ratios of observed (X-shooter) spectra to model spectra. The finally selected model spectra are then corrected for remaining mismatches and photometrically calibrated using independent observations. The correction of telluric absorption is performed with the help of telluric model spectra.We provide new, finely sampled reference spectra without telluric absorption for six southern flux standard stars that allow the users to flux calibrate their data from 300 nm to 2500 nm, and a method to correct for telluric absorption using atmospheric models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".