Euclid Quick Data Release (Q1). The average far-infrared properties of Euclid-selected star-forming galaxies
Bibliographic record
Abstract
The first Euclid Quick Data Release contains millions of galaxies with excellent optical and near-infrared (IR) coverage. To complement this dataset, we investigate the average far-IR properties of Euclid-selected main sequence (MS) galaxies using existing Herschel and SCUBA-2 data. We use 17.6deg$^2$ (2.4deg$^2$) of overlapping Herschel (SCUBA-2) data, containing 2.6 million (240000) MS galaxies. We bin the Euclid catalogue by stellar mass and photometric redshift and perform a stacking analysis following SimStack, which takes into account galaxy clustering and bin-to-bin correlations. We detect stacked far-IR flux densities across a significant fraction of the bins. We fit modified blackbody spectral energy distributions in each bin and derive mean dust temperatures, dust masses, and star-formation rates (SFRs). We find similar mean SFRs compared to the Euclid catalogue, and we show that the average dust-to-stellar mass ratios decreased from z$\simeq$1 to the present day. Average dust temperatures are largely independent of stellar mass and are well-described by the function $T_2+(T_1-T_2){\rm e}^{-t/τ}$, where $t$ is the age of the Universe, $T_1=79.7\pm7.4$K, $T_2=23.2\pm0.1$K, and $τ=1.6\pm0.1$Gyr. We argue that since the dust temperatures are converging to a non-zero value below $z=1$, the dust is now primarily heated by the existing cooler and older stellar population, as opposed to hot young stars in star-forming regions at higher redshift. We show that since the dust temperatures are independent of stellar mass, the correlation between dust temperature and SFR depends on stellar mass. Lastly, we estimate the contribution of the Euclid catalogue to the cosmic IR background (CIB), finding that it accounts for >60% of the CIB at 250, 350, and 500$μ$m. Forthcoming Euclid data will extend these results to higher redshifts, lower stellar masses, and recover more of the CIB.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.029 |
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".