DIFFUSE INTERSTELLAR BAND AT 8620 Å IN RAVE: A NEW METHOD FOR DETECTING THE DIFFUSE INTERSTELLAR BAND IN SPECTRA OF COOL STARS
Bibliographic record
Abstract
Diffuse interstellar bands (DIBs) are usually observed in spectra of hot stars, where interstellar lines are rarely blended with stellar ones. The need for hot stars is a strong limitation in the number of sightlines we can observe and their distribution in the Galaxy, as hot stars are rare and concentrated in the Galactic plane. We are introducing a new method, where interstellar lines can be observed in spectra of cool stars in large spectroscopic surveys. The method is completely automated and does not require prior knowledge of the stellar parameters. The main step is a construction of the stellar spectrum, which is done by finding other observed spectra that lack interstellar features and are otherwise very similar to the spectrum in question. Such spectra are then combined into a single stellar spectrum template, matching the stellar component of the observed spectrum. We demonstrate the performance of this new method on a sample of 482,430 Radial Velocity Experiment survey spectra. However, many spectra have to be combined (48 on average) in order to achieve a signal-to-noise ratio high enough to measure the profile of the DIB at 8620 Å, hence limiting the spatial information about the interstellar medium. We compare its equivalent width with extinction maps and with Bayesian reddening, calculated for individual stars, and provide a linear relation between the equivalent width and reddening. Separately from the introduced method, we calculate equivalent widths of the DIB in spectra of hot stars with known extinction and compare all three linear relations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".