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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 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) | 0.017 | 0.001 |
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; both teacher heads agree on what is shown here.
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".