The EDIBLES Survey
Bibliographic record
Abstract
Context. There have been many attempts to identify families of diffuse interstellar bands (DIBs) with perfectly correlating band strengths. Although major efforts have been made to classify broadly based DIB families and important insights have been gained, no family has been identified with sufficient accuracy or statistical significance to prove that a series of selected DIBs originates from the same carrier. This can be attributed in part to the exclusive use of equivalent widths to establish DIB families. Aims. In a change of strategy, we search for DIBs that are highly correlated in both band strength and profile shape. This approach increases the chance of correlating DIBs being members of one family and originating from the same carrier molecule. We also search for correlations between DIB profile families and atomic interstellar lines, with the goal of further chemically constraining possible DIB carriers. Methods. We adapted the well-known method of time-series alignment to perform a spectral alignment; that is, DIB alignment. In a second step, we analysed the alignment results using a clustering analysis. This method required a statistically significant data set of DIB sight lines. The ESO Diffuse Interstellar Bands Large Exploration Survey (EDIBLES) data were perfectly suited for this application. Results. We report eight DIB families with correlating strengths and profiles, as well as four previously unreported DIBs in the visual range, found using DIB alignment. All profile family members show Pearson correlation coefficients in band strength higher than 0.9. In particular, we report the 6614–6521 Å DIB pair, in which both DIBs show the same triple-peak substructure and an unprecedented band strength Pearson correlation coefficient of 0.9935. The presented approach opens up new perspectives that can guide the laboratory search for DIB carriers.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".