The DEEP Groth Strip Survey. II. <i>Hubble Space Telescope</i> Structural Parameters of Galaxies in the Groth Strip
Bibliographic record
Abstract
The quantitative morphological classification of distant galaxies is essential to the understanding of the evolution of galaxies over the history of the universe. This paper presents Hubble Space Telescope WFPC2 F606W and F814W photometric structural parameters for 7450 galaxies in the "Groth Strip." These parameters are based on a two-dimensional bulge + disk surface brightness model and were obtained using an automated reduction and analysis pipeline described in detail here. A first set of fits was performed separately in each bandpass, and a second set of fits was performed simultaneously on both bandpasses. The information produced by these two types of fits can be used to explore different science goals. Systematic and random fitting errors in all structural parameters as well as bulge and disk colors are carefully characterized through extensive sets of simulations. The results of these simulations are given in catalogs similar to the real science catalogs so that both real and simulated measurements can be sampled according to the same selection criteria to show biases and errors in the science data subset of interest. The effects of asymmetric structures on the recovered bulge+disk fitting parameters are also explored through simulations. The full multidimensional photometric survey selection function of the Groth Strip is also computed. This selection function, coupled to bias maps from simulations, provides a complete and objective reproduction of the observational limits, and these limits can be applied to theoretical predictions from galaxy evolution models for direct comparisons with the data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".