<i>Euclid</i> preparation

S. E. van Mierlo, K. I. Caputi, M. L. N. Ashby, Hakim Atek, M. Bolzonella, R. A. A. Bowler, Gabriel Brammer, Christopher J. Conselice, Jean-Gabriel Cuby, Pratika Dayal,

Astronomy and Astrophysics · 2022 · 18 citations · 69 references

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Abstract

Context. The Euclid mission is expected to discover thousands of z &gt; 6 galaxies in three deep fields, which together will cover a ∼50 deg 2 area. However, the limited number of Euclid bands (four) and the low availability of ancillary data could make the identification of z &gt; 6 galaxies challenging. Aims. In this work we assess the degree of contamination by intermediate-redshift galaxies ( z = 1–5.8) expected for z &gt; 6 galaxies within the Euclid Deep Survey. Methods. This study is based on ∼176 000 real galaxies at z = 1–8 in a ∼0.7 deg 2 area selected from the UltraVISTA ultra-deep survey and ∼96 000 mock galaxies with 25.3 ≤ H &lt; 27.0, which altogether cover the range of magnitudes to be probed in the Euclid Deep Survey. We simulate Euclid and ancillary photometry from fiducial 28-band photometry and fit spectral energy distributions to various combinations of these simulated data. Results. We demonstrate that identifying z &gt; 6 galaxies with Euclid data alone will be very effective, with a z &gt; 6 recovery of 91% (88%) for bright (faint) galaxies. For the UltraVISTA-like bright sample, the percentage of z = 1–5.8 contaminants amongst apparent z &gt; 6 galaxies as observed with Euclid alone is 18%, which is reduced to 4% (13%) by including ultra-deep Rubin ( Spitzer ) photometry. Conversely, for the faint mock sample, the contamination fraction with Euclid alone is considerably higher at 39%, and minimised to 7% when including ultra-deep Rubin data. For UltraVISTA-like bright galaxies, we find that Euclid ( I E − Y E ) &gt; 2.8 and ( Y E − J E ) &lt; 1.4 colour criteria can separate contaminants from true z &gt; 6 galaxies, although these are applicable to only 54% of the contaminants as many have unconstrained ( I E − Y E ) colours. In the best scenario, these cuts reduce the contamination fraction to 1% whilst preserving 81% of the fiducial z &gt; 6 sample. For the faint mock sample, colour cuts are infeasible; we find instead that a 5 σ detection threshold requirement in at least one of the Euclid near-infrared bands reduces the contamination fraction to 25%.

References

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