A Holistic Exploration of the Potentially Recoverable Redshift Information of Stage IV Galaxy Surveys
Bibliographic record
Abstract
Abstract Extragalactic science and cosmology with Stage IV galaxy surveys will rely almost exclusively on redshift measurements derived solely from photometry, which are subject to systematic and statistical uncertainties with numerous analysis choices. Single-survey photometric redshift estimates ought to be improved by combining data from multiple surveys, with common wisdom asserting that optical data benefits from additional infrared (IR) but not ultraviolet (UV) coverage. The degree of improvement for either is not well characterized, and attempts necessitate assumptions of a chosen estimator and its prior information. We apply an information-theoretic metric of potentially recoverable redshift information to assess the impact of multi-survey photometry without assuming an estimator or priors in the context of the Vera C. Rubin Observatory Legacy Survey of Space and Time (lsst) in the optical, Roman and Euclid (roman and euclid) in the IR, and Cosmological Advanced Survey Telescope for Optical-UV Research (c astor) in the UV. Our approach uses mock catalogs to approximate conditional relationships between color and redshift from real samples, but is otherwise independent of estimator and prior information. We conclude that adding UV photometry can benefit redshift determination of certain galaxy populations, but that gain is tempered by their decreased chance of meeting detection criteria at higher wavelengths. We explore the spectral energy distributions of galaxies whose potentially recoverable redshift information is most impacted by additional photometry. The holistic assessment approach we develop here is generic and may be applied to quantify the impact of combining photometric data sets, changing experimental design, optimizing observing strategy, and mitigating systematics.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| 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".