Stellar Reddening Based Extinction Maps for Cosmological Applications
Bibliographic record
Abstract
Maps of Galactic extinction at a full-width half-maximum of 6.1' and 15', described in Mudur, Park, Finkbeiner '22. These maps were generated from stellar reddenings derived from Pan-STARRS1 and 2MASS photometry and Gaia EDR3 parallaxes using the Bayestar stellar inference pipeline (Green et al '19). The stars are filtered so as to only include objects that lie beyond a certain distance threshold, and pass stellar selections intended to eliminate objects that are possibly of extragalactic origin (stray QSOs and galaxies) from the catalog. Two maps are then generated for the intersection of the sky at Galactic Latitude \(|b|>20^\circ\) and within the PS1 footprint. The fields in the fits files are organized as follows: 'Recon_Mean': Raw map reconstruction mean in Bayestar reddening units. 'Recon_Mean_ZptCorr': Reconstruction mean in units of \(E_{B-V}\). To derive 'Recon_Mean_ZptCorr' from 'Recon_Mean' we multiply 'Recon_Mean' by 0.856 to convert to units of \(E_{B-V}\). We then add an offset of -0.009 to match the mean of the map to the mean of SFD at \(b>60^\circ\). 'Recon_Variance': Raw map reconstruction variance in Bayestar reddening units. 'Recon_VarianceCorr': Reconstruction variance in units of \(E_{B-V}\) with an additional variance term \(\sigma_{sys}^2\). The additional term is set such that the variance of \({Map15-SFD \over \sigma_{Map}^2 + \sigma_{sys}^2 }\) at \(b>60^\circ\) is ~1. \(\sigma_{sys}=0.01\) Figures and analyses in the paper used 'Recon_Mean_ZptCorr' and 'Recon_VarianceCorr' for both maps.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.168 | 0.053 |
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".