Extinction Maps Toward The Milky Way Bulge: Two-Dimensional And Three-Dimensional Tests With APOGEE
Bibliographic record
Abstract
Galactic interstellar extinction maps are powerful and necessary tools for Milky Way structure and stellar population analyses, particularly toward the heavily reddened bulge and in the midplane. However, due to the difficulty of obtaining reliable extinction measures and distances for a large number of stars that are independent of these maps, tests of their accuracy and systematics have been limited. Our goal is to assess a variety of photometric stellar extinction estimates, including both two-dimensional and three-dimensional extinction maps, using independent extinction measures based on a large spectroscopic sample of stars toward the Milky Way bulge. We employ stellar atmospheric parameters derived from high-resolution H-band Apache Point Observatory Galactic Evolution Experiment (APOGEE) spectra, combined with theoretical stellar isochrones, to calculate line-of-sight extinction and distances for a sample of more than 2400 giants toward the Milky Way bulge. We compare these extinction values to those predicted by individual near-IR and near+mid-IR stellar colors, two-dimensional bulge extinction maps, and three-dimensional extinction maps. The long baseline, near+mid-IR stellar colors are, on average, the most accurate predictors of the APOGEE extinction estimates, and the two-dimensional and three-dimensional extinction maps derived from different stellar populations along different sightlines show varying degrees of reliability. We present the results of all of the comparisons and discuss reasons for the observed discrepancies. We also demonstrate how the particular stellar atmospheric models adopted can have a strong impact on this type of analysis, and discuss related caveats.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".