Infrared spectroscopy of asymptotic giant branch stars in the Galactic bulge
Bibliographic record
Abstract
We have selected a homogeneous sample of asymptotic giant branch (AGB) stars in the Galactic bulge population from the ISOGAL survey. Our target stars cover a wide range of mass-loss rates (∼10−8-10−4 M⊙ yr−1) and differ primarily by their age on the AGB. This homogeneous sample is thus ideally suited to study the dust formation process as a function of age on the AGB. We observed our sample with Spitzer-Infrared Spectrograph, and studied the overall properties of the infrared spectra of these targets. The analysis is complicated by the presence of strong and variable background emission, and the extracted infrared AGB star spectra are affected by interstellar extinction. Several stars in our sample have no detectable dust emission, and we used these ‘naked stars’ to characterize the stellar and molecular contributions to the infrared spectra of our target stars. The resulting dust spectra of our targets do indeed show significant variety in their spectral appearance, pointing to differing dust compositions for the targets. We classify the spectra based on the shape of their 10-μm emission following the scheme by Sloan & Price. We find that the early silicate emission classes associated with oxide dust are generally under-represented in our sample due to extinction effects. We also find a weak 13-μm dust feature in two of our otherwise naked star spectra, suggesting that the carrier of this feature could potentially be the first condensate in the sequence of dust condensation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".