The EDIBLES survey
Bibliographic record
Abstract
Context. Interstellar absorption observed toward stellar targets changes slowly over long timescales, mainly due to the proper motion of the background target relative to the intervening clouds, such that over time, different parts of the intervening cloud are probed. On longer timescales, the slowly changing physical and chemical conditions in the cloud can also cause variation. Detecting such time variations thus provides an opportunity to study cloud structure. Aims. We searched for systematic variations in the absorption profiles of the diffuse interstellar bands (DIBs) and interstellar atomic and molecular lines by comparing the high-quality data set from the recent ESO diffuse interstellar bands large exploration survey (EDIBLES) to older archival observations, bridging typical timescales of ~10 yr with a maximum timescale of 22 yr. Methods. For 64 EDIBLES targets, we found adequate archival observations. We selected 31 strong DIBs, seven atomic lines, and five molecular lines to focus our search on. We carefully considered various systematic effects and used a robust Bayesian quantitative test to establish which of these absorption features could display significant variations. Results. While systematic effects greatly complicate our search, we find evidence for variations in the profiles of the λλ4727 and 5780 DIBs in a few sightlines. Toward HD 167264, we find a new Ca I cloud component that appears and becomes stronger after 2008. The same sightline furthermore displays marginal, but systematic changes in the column densities of the atomic lines originating from the main cloud component in the sightline. Similar variations are seen toward HD 147933. Conclusions. Our high-quality spectroscopic observations in combination with archival data show that it is possible to probe interstellar time variations on time scales of typically a decade. Despite the fact that systematic uncertainties as well as the generally somewhat lower quality of older data complicate matters, we can conclude that time variations can be made visible, both in atomic lines and DIB profiles for a few targets, but that generally, these features are stable along many lines of sight. We present this study as an archival baseline for future comparisons, bridging longer periods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".