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Euclid Quick Data Release (Q1). The average far-infrared properties of Euclid-selected star-forming galaxies

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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 $T_2+(T_1-T_2){\rm e}^{-t/\tau}$, where $t$ is the age of the Universe, $T_1=79.7\pm7.4$K, $T_2=23.2\pm0.1$K, and $\tau=1.6\pm0.1$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$\mu$m. Forthcoming Euclid data will extend these results to higher redshifts, lower stellar masses, and recover more of the CIB.

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2026 1

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How to count clustered galaxies

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An empirical method corrects clustering bias in P(D) galaxy number counts, showing that clustering inflates 500μm counts by a factor of 1.6 near 10 mJy in Herschel-SPIRE GOODS-N data.

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