Data-driven Discovery of Diffuse Interstellar Bands with APOGEE Spectra
Bibliographic record
Abstract
Abstract Data-driven models of stellar spectra are useful tools to study nonstellar information, such as the diffuse interstellar bands (DIBs) caused by intervening interstellar material. Using ∼55,000 spectra of ∼17,000 red clump stars from the APOGEE DR16 data set, we create second-order polynomial models of the continuum-normalized flux as a function of stellar parameters ( T eff , log g , [Fe/H], [ α /Fe], and age). The model and data show good agreement within uncertainties across the APOGEE wavelength range, although many regions reveal residuals that are not in the stellar rest-frame. We show that many of these residual features—having average extrema at the level of ∼3% in stellar flux on average—can be attributed to incompletely removed spectral lines from the Earth’s atmosphere and DIBs from the interstellar medium (ISM). After removing most of the remaining contamination from Earth’s sky, we identify 84 absorption features not seen in unreddened sightlights that have <50% probability of being noise artifacts—with 25 of these features having <5% probability of being noise artifacts—including all 10 previously known DIBs in the APOGEE wavelength range. Because many of these features occur in the wavelength windows that APOGEE uses to measure chemical abundances, note that characterization and removal of this nonstellar contamination establish an important step in reaching the precision required for chemical tagging experiments. Proper characterization of these features will benefit Galactic ISM science and the currently ongoing Milky Way Mapper program of Sloan Digital Sky Survey V, which relies on the APOGEE spectrograph.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.000 |
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".