<i>Euclid</i>: the selection of quiescent and star-forming galaxies using observed colours
Bibliographic record
Abstract
ABSTRACT The Euclid mission will observe well over a billion galaxies out to z ∼ 6 and beyond. This will offer an unrivalled opportunity to investigate several key questions for understanding galaxy formation and evolution. The first step for many of these studies will be the selection of a sample of quiescent and star-forming galaxies, as is often done in the literature by using well-known colour techniques such as the ‘UVJ’ diagram. However, given the limited number of filters available for the Euclid telescope, the recovery of such rest-frame colours will be challenging. We therefore investigate the use of observed Euclid colours, on their own and together with ground-based u-band observations, for selecting quiescent and star-forming galaxies. The most efficient colour combination, among the ones tested in this work, consists of the (u − VIS) and (VIS − J) colours. We find that this combination allows users to select a sample of quiescent galaxies complete to above $\sim 70{{\ \rm per\ cent}}$ and with less than 15${{\ \rm per\ cent}}$ contamination at redshifts in the range 0.75 < z < 1. For galaxies at high-z or without the u-band complementary observations, the (VIS − Y) and (J − H) colours represent a valid alternative, with $\gt 65{{\ \rm per\ cent}}$ completeness level and contamination below 20${{\ \rm per\ cent}}$ at 1 < z < 2 for finding quiescent galaxies. In comparison, the sample of quiescent galaxies selected with the traditional UVJ technique is only $\sim 20{{\ \rm per\ cent}}$ complete at z < 3, when recovering the rest-frame colours using mock Euclid observations. This shows that our new methodology is the most suitable one when only Euclid bands, along with u-band imaging, are available.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".