Euclid Quick Data Release (Q1). A probabilistic classification of quenched galaxies
Bibliographic record
Abstract
Investigating what drives the quenching of star formation in galaxies is key to understanding their evolution. The Euclid mission will provide rich data from optical to infrared wavelengths for millions of galaxies, and enable precise measurements of their star formation histories. Using the first Euclid Quick Data Release (Q1), we developed a probabilistic classification framework that combines the average specific star-formation rate inferred over two timescales ($10^8,10^9$ yr) to categorise galaxies as `ageing' (secularly evolving), `quenched' (recently halted star formation), or `retired' (dominated by old stars). Two classification methods were employed: a probabilistic approach, which integrates posterior distributions, and a model-driven method, which optimises sample purity and completeness using IllustrisTNG. At $z<0.1$ and $M_\ast \gtrsim 3\times10^{8}\,M_\odot$, we obtain Euclid class fractions of 68-72\%, 8-17\%, and 14-19\% for ageing, quenched, and retired populations, respectively. Ageing and retired galaxies dominate at the low- and high-mass end, respectively, while quenched galaxies surpass the retired fraction for $M_\ast \lesssim 10^{10}\,\rm M_\odot$. The evolution with redshift shows increasing and decreasing fractions of ageing and retired galaxies, respectively. More massive galaxies usually undergo quenching episodes at earlier times than to their low-mass counterparts. In terms of the mass-size-metallicity relation, ageing galaxies generally exhibit disc morphologies and low metallicities. Retired galaxies show compact structures and enhanced chemical enrichment, while quenched galaxies form an intermediate population that is more compact and chemically evolved than ageing systems. This work demonstrates Euclid's great potential for elucidating the physical nature of the quenching mechanisms that govern galaxy evolution.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.026 |
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".