The Application of Photometric Redshifts to the SDSS Early Data Release
Bibliographic record
Abstract
The Early Data Release (EDR) from the Sloan Digital Sky Survey provides one of the largest multicolor photometric catalogs currently available to the astronomical community. In this paper we present the first application of photometric redshifts to the ∼6 million extended sources in these data (with 1.8 million sources having r ' < 21). Utilizing a range of photometric redshift techniques, from empirical to template and hybrid techniques, we investigate the statistical and systematic uncertainties present in the redshift estimates for the EDR data. For r ' < 21, we find that the redshift estimates provide realistic redshift histograms with an rms uncertainty in the photometric redshift relation of 0.035 at r ' < 18 and rising to 0.1 at r ' < 21. We conclude by describing how these photometric redshifts and derived quantities, such as spectral type, rest-frame colors, and absolute magnitudes, are stored in the SDSS database. We provide sample queries for searching on photometric redshifts and list the current caveats and issues that should be understood before using these photometric redshifts in statistical analyses of the SDSS galaxies.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".