Euclid: Identifying the reddest high-redshift galaxies in the Euclid Deep Fields with gradient-boosted trees
Creators: name: Signor, T.
name: Rodighiero, G.
name: Bisigello, L.
name: Bolzonella, M.
name: Caputi, K. I.
name: Daddi, E.
name: De Lucia, G.
name: Enia, A.
name: Gabarra, L.
name: Gruppioni, C.
name: Humphrey, A.
name: La Franca, F.
name: Mancini, C.
name: Pozzetti, L.
name: Serjeant, S.
name: Spinoglio, L.
name: van Mierlo, S. E.
name: Andreon, S.
name: Auricchio, N.
name: Baldi, M.
name: Bardelli, S.
name: Battaglia, P.
name: Bender, R.
name: Bodendorf, C.
name: Bonino, D.
name: Branchini, E.
name: Brescia, M.
name: Brinchmann, J.
name: Camera, S.
name: Capobianco, V.
name: Carbone, C.
name: Carretero, J.
name: Casas, S.
name: Castellano, M.
name: Cavuoti, S.
name: Cimatti, A.
name: Cledassou, R.
name: Congedo, G.
name: Conselice, C. J.
name: Conversi, L.
name: Copin, Y.
name: Corcione, L.
name: Courbin, F.
name: Courtois, H. M.
name: Da Silva, A.
name: Degaudenzi, H.
name: Di Giorgio, A. M.
name: Dinis, J.
name: Dubath, F.
name: Dupac, X.
name: Dusini, S.
name: Ealet, A.
name: Farina, M.
name: Farrens, S.
name: Ferriol, S.
name: Fotopoulou, S.
name: Franceschi, E.
name: Galeotta, S.
name: Garilli, B.
name: Gillard, W.
name: Gillis, B.
name: Giocoli, C.
name: Grazian, A.
name: Grupp, F.
name: Guzzo, L.
name: Haugan, S. V. H.
name: Hook, I.
name: Hormuth, F.
name: Hornstrup, A.
name: Jahnke, K.
name: Kümmel, M.
name: Kermiche, S.
name: Kiessling, A.
name: Kilbinger, M.
name: Kitching, T.
name: Kurki-Suonio, H.
name: Ligori, S.
name: Lilje, P. B.
name: Lindholm, V.
name: Lloro, I.
name: Maino, D.
name: Maiorano, E.
name: Mansutti, O.
name: Marggraf, O.
name: Martinet, N.
name: Marulli, F.
name: Massey, R.
name: Medinaceli, E.
name: Melchior, M.
name: Mellier, Y.
name: Meneghetti, M.
name: Merlin, E.
name: Moresco, M.
name: Moscardini, L.
name: Munari, E.
name: Nichol, R. C.
name: Niemi, S. -M.
name: Padilla, C.
name: Paltani, S.
name: Pasian, F.
name: Pedersen, K.
name: Pettorino, V.
name: Pires, S.
name: Polenta, G.
name: Poncet, M.
name: Popa, L. A.
name: Raison, F.
name: Renzi, A.
name: Rhodes, J.
name: Riccio, G.
name: Romelli, E.
name: Roncarelli, M.
name: Rossetti, E.
name: Saglia, R.
name: Sapone, D.
name: Sartoris, B.
name: Schneider, P.
name: Schrabback, T.
name: Secroun, A.
name: Seidel, G.
name: Serrano, S.
name: Sirignano, C.
name: Sirri, G.
name: Stanco, L.
name: Surace, C.
name: Tallada-Crespí, P.
name: Teplitz, H. I.
name: Tereno, I.
name: Toledo-Moreo, R.
name: Torradeflot, F.
name: Tutusaus, I.
name: Valentijn, E. A.
name: Vassallo, T.
name: Veropalumbo, A.
name: Wang, Y.
name: Weller, J.
name: Williams, O. R.
name: Zoubian, J.
name: Zucca, E.
name: Burigana, C.
name: Scottez, V.
Publisher: name: arXiv
Year: 2024
Subjects: subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: FOS: Physical sciences
subject: FOS: Physical sciences

description: Dusty, distant, massive ($M_*\gtrsim 10^{11}\,\rm M_\odot$) galaxies are usually found to show a remarkable star-formation activity, contributing on the order of $25\%$ of the cosmic star-formation rate density at $z\approx3$--$5$, and up to $30\%$ at $z\sim7$ from ALMA observations. Nonetheless, they are elusive in classical optical surveys, and current near-infrared surveys are able to detect them only in very small sky areas. Since these objects have low space densities, deep and wide surveys are necessary to obtain statistically relevant results about them. Euclid will be potentially capable of delivering the required information, but, given the lack of spectroscopic features at these distances within its bands, it is still unclear if it will be possible to identify and characterize these objects. The goal of this work is to assess the capability of Euclid, together with ancillary optical and near-infrared data, to identify these distant, dusty and massive galaxies, based on broadband photometry. We used a gradient-boosting algorithm to predict both the redshift and spectral type of objects at high $z$. To perform such an analysis we make use of simulated photometric observations derived using the SPRITZ software. The gradient-boosting algorithm was found to be accurate in predicting both the redshift and spectral type of objects within the Euclid Deep Survey simulated catalog at $z&gt;2$. In particular, we study the analog of HIEROs (i.e. sources with $H-[4.5]&gt;2.25$), combining Euclid and Spitzer data at the depth of the Deep Fields. We found that the dusty population at $3\lesssim z\lesssim 7$ is well identified, with a redshift RMS and OLF of only $0.55$ and $8.5\%$ ($H_E\leq26$), respectively. Our findings suggest that with Euclid we will obtain meaningful insights into the role of massive and dusty galaxies in the cosmic star-formation rate over time.
description: 18 pages, 13 figures, accepted in A&amp;A