Spectral shape corrections for SDSS BOSS quasars
Bibliographic record
Abstract
Modifications were made to the Sloan Digital Sky Survey’s Baryonic Oscillations Spectroscopic Survey (SDSS/BOSS) optical fibres assigned to quasar targets in order to improve the signal-to-noise ratio in the Ly-α forest. However, the consequence of these modifications is that quasars observed in this way require additional flux correction procedures in order to recover the correct spectral shapes. In this paper we describe such a procedure, based on the geometry of the problem, and other observational parameters. Applying several correction methods to four SDSS quasars with multiple observations permits a detailed verification of the relative performances of the different flux correction procedures. We contrast our method (which takes into account a wavelength-dependent seeing profile) with the BOSS pipeline approach (which does not). Our results provide independent confirmation that the geometric approach employed in the SDSS pipeline works well, although with room for improvement. By separating the contributions from four effects, we are able to quantify their relative importance. Most importantly, we demonstrate that wavelength dependence has a significant impact on the derived spectral shapes and thus should not be ignored.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".