Article, 2024

Euclid: Identifying the reddest high-redshift galaxies in the Euclid Deep Fields with gradient-boosted trees⋆

Astronomy & Astrophysics, ISSN 0004-6361, 1432-0746, Volume 685, Page a127, 10.1051/0004-6361/202348737

Contributors

Signor, T 0009-0003-5121-3567 [1] [2] [3] Rodighiero, Giulia 0000-0002-9415-2296 (Corresponding author) [3] [4] Bisigello, Laura 0000-0003-0492-4924 [3] [4] Bolzonella, M. [5] Caputi, Karina I 0000-0001-8183-1460 [6] [7] Daddi, Emanuele 0000-0002-3331-9590 [8] De Lucia, Gabriella [9] Enia, A. [10] [11] Gabarra, L. [12] Gruppioni, Carlotta 0000-0002-5836-4056 [5] Humphrey, Andrew 0000-0002-0510-2351 [13] [14] La Franca, Fabio 0000-0002-1239-2721 [15] Mancini, C [16] Pozzetti, Lucia 0000-0001-7085-0412 [5] Serjeant, Stephen B G 0000-0002-0517-7943 [17] Spinoglio, Luigi 0000-0001-8840-1551 [18] Van Mierlo, Sophie E 0000-0001-8289-2863 [6] Andreon, Stefano 0000-0002-2041-8784 [19] Auricchio, Natalia 0000-0003-4444-8651 [5] Baldi, Marco 0000-0003-4145-1943 [5] [11] [20] Bardelli, Sandro 0000-0002-8900-0298 [5] Battaglia, P [5] Bender, Ralf 0000-0001-7179-0626 [21] [22] Bodendorf, C. [21] Bonino, Donata 0000-0002-3336-9977 [23] Branchini, Enzo 0000-0002-0808-6908 [19] [24] [25] Brescia, Massimo 0000-0001-9506-5680 [26] [27] [28] Brinchmann, Jarle 0000-0003-4359-8797 [14] Camera, Stefano 0000-0003-3399-3574 [23] [29] [30] Capobianco, Vito 0000-0002-3309-7692 [23] Carbone, Carmelita 0000-0003-0125-3563 [16] Carretero, Jorge 0000-0002-3130-0204 [31] [32] Casas, S [33] Castellano, Marco 0000-0001-9875-8263 [34] Cavuoti, Stefano 0000-0002-3787-4196 [27] [28] Cimatti, A [11] Cledassou, R. [35] [36] Congedo, Giuseppe 0000-0003-2508-0046 [37] Conselice, Christopher J 0000-0003-1949-7638 [38] Conversi, Luca 0000-0002-6710-8476 [39] [40] Copin, Yannick 0000-0002-5317-7518 [41] Corcione, Leonardo 0000-0002-6497-5881 [23] Courbin, Frederic Yves Michel 0000-0003-0758-6510 [42] Courtois, H M [43] Da Silva, António José Cunha 0000-0002-6385-1609 [44] Degaudenzi, H. [45] Di Giorgio, Anna Maria [18] Dinis, João 0000-0001-5075-1601 [44] Dubath, F. [45] Dupac, Xavier [40] Dusini, S. [46] Ealet, A. [47] Farina, Maria 0000-0002-3089-7846 [18] Farrens, S. [48] Ferriol, Sylvain [41] Fotopoulou, Sotiria 0000-0002-9686-254X [49] Franceschi, Enrico 0000-0002-0585-6591 [5] Galeotta, Samuele 0000-0002-3748-5115 [9] Garilli, Bianca 0000-0001-7455-8750 [16] Gillard, William 0000-0003-4744-9748 [50] Gillis, Bryan R 0000-0002-4478-1270 [37] Giocoli, Carlo 0000-0002-9590-7961 [5] [20] Grazian, Andrea [4] Grupp, Frank U 0000-0001-7300-9303 [21] [22] Guzzo, Luigi 0000-0001-8264-5192 [19] [51] [52] Haugan, S. V. H. [53] Hook, Isobel Mary 0000-0002-2960-978X [54] Hormuth, Felix [55] Hornstrup, Allan 0000-0002-3363-0936 [7] [56] Jahnke, K. [57] Kümmel, M [22] Kermiche, Sabrina 0000-0002-0302-5735 [50] Kiessling, Alina [58] Kilbinger, Martin 0000-0001-9513-7138 [8] Kitching, Thomas D [59] Kurki-Suonio, Hannu 0000-0002-4618-3063 [60] [61] Ligori, Sebastiano 0000-0003-4172-4606 [23] Lilje, Per B 0000-0003-4324-7794 [53] Lindholm, Valtteri 0000-0003-2317-5471 [60] [61] Lloro, Ivan [62] Maino, Davide 0000-0002-4901-0133 [16] [51] [52] Maiorano, E. [5] Mansutti, Oriana 0000-0001-5758-4658 [9] Marggraf, O. [63] Martinet, Nicolas 0000-0003-2786-7790 [64] Marulli, Federico 0000-0002-8850-0303 [5] [11] [20] Massey, Richard J Massey Richard J 0000-0002-6085-3780 [65] Medinaceli, Eduardo [5] Melchior, Martin [66] Mellier, Yannick [67] [68] Meneghetti, Massimo 0000-0003-1225-7084 [5] [20] Merlin, Emiliano 0000-0001-6870-8900 [34] Moresco, Michele 0000-0002-7616-7136 [5] [11] Moscardini, Lauro 0000-0002-3473-6716 [5] [11] [20] Munari, Emiliano 0000-0002-1751-5946 [9] Nichol, R. C. [69] Niemi, S.-M. [70] Padilla, Cristobal 0000-0001-7951-0166 [32] Paltani, S. [45] Pasian, Fabio 0000-0002-4869-3227 [9] Pedersen, K [71] Pettorino, Valeria 0000-0002-4203-9320 [48] Pires, S. [48] Polenta, G. [72] Poncet, Maurice [36] Popa, L. A. [73] Raison, Frédéric [21] Renzi, Alessandro 0000-0001-9856-1970 [3] [46] Rhodes, Jason D [58] Riccio, Giuseppe 0000-0001-7020-1172 [28] Romelli, Erik 0000-0003-3069-9222 [9] Roncarelli, Mauro 0000-0001-9587-7822 [5] Rossetti, Emanuel 0000-0003-0238-4047 [11] Saglia, Roberto P 0000-0003-0378-7032 [21] [22] Sapone, D. [74] Sartoris, Barbara 0000-0003-1337-5269 [9] [22] Schneider, P. [63] Schrabback, Tim 0000-0002-6987-7834 [75] Secroun, Aurélia 0000-0003-0505-3710 [50] Seidel, G. [57] Serrano, Santiago 0000-0002-0211-2861 [76] [77] [78] Sirignano, Chiara 0000-0002-0995-7146 [3] [46] Sirri, G. [20] Stanco, L. [46] Surace, C [64] Tallada-Crespí, Pau 0000-0002-1336-8328 [31] [79] Teplitz, Harry I 0000-0002-7064-5424 [80] Tereno, Ismael Alexandre Borges 0000-0002-4537-6218 [44] Toledo-Moreo, Rafael 0000-0002-2997-4859 [81] Torradeflot, Fra Ncesc 0000-0003-1160-1517 [31] [79] Tutusaus, Isaac 0000-0002-3199-0399 [82] Valentijn, Edwin A 0000-0003-1032-6680 [6] Vassallo, T 0000-0001-6512-6358 [9] [22] Veropalumbo, A [19] [25] Wang, Yun 0000-0002-4749-2984 [80] Weller, Jochen 0000-0002-8282-2010 [21] [22] Williams, O. R. [6] Zoubian, J. [50] Zucca, Elena 0000-0002-5845-8132 [5] Burigana, Carlo 0000-0002-3005-5796 [20] [83] Scottez, Vivien [68] [84]

Affiliations

  1. [1] Diego Portales University
    2. [NORA names: Chile; America, South; OECD];
  2. [2] Inria Chile
    3. [NORA names: Chile; America, South; OECD];
  3. [3] University of Padua
    4. [NORA names: Italy; Europe, EU; OECD];
  4. [4] Osservatorio Astronomico di Padova
    5. [NORA names: Italy; Europe, EU; OECD];
  5. [5] Osservatorio astronomico di Bologna
    6. [NORA names: Italy; Europe, EU; OECD];

Abstract

Context. ALMA observations show that dusty, distant, massive ( M * ≳ 10 11 M ⊙ ) galaxies usually have a remarkable star-formation activity, contributing of the order of 25% of the cosmic star-formation rate density at z ≈ 3–5, and up to 30% at z ∼ 7. Nonetheless, they are elusive in classical optical surveys, and current near-IR 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 potentially be capable of delivering the required information, but, given the lack of spectroscopic features at these distances within its bands, it is still unclear if Euclid will be able to identify and characterise these objects. Aims. The goal of this work is to assess the capability of Euclid , together with ancillary optical and near-IR data, to identify these distant, dusty, and massive galaxies based on broadband photometry. Methods. 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 made use of simulated photometric observations that mimic the Euclid Deep Survey, derived using the state-of-the-art Spectro-Photometric Realizations of Infrared-selected Targets at all- z ( SPRITZ ) software. Results. The gradient-boosting algorithm was found to be accurate in predicting both the redshift and spectral type of objects within the simulated Euclid Deep Survey catalogue at z > 2, while drastically decreasing the runtime with respect to spectral-energy-distribution-fitting methods. In particular, we studied the analogue of HIEROs (i.e. sources selected on the basis of a red H − [4.5]> 2.25), combining Euclid and Spitzer data at the depth of the Deep Fields. These sources include the bulk of obscured and massive galaxies in a broad redshift range, 3 < z < 7. We find that the dusty population at 3 ≲ z ≲ 7 is well identified, with a redshift root mean squared error and catastrophic outlier fraction of only 0.55 and 8.5% ( H E ≤ 26), respectively. Our findings suggest that with Euclid we will obtain meaningful insights into the impact of massive and dusty galaxies on the cosmic star-formation rate over time.

Keywords

ALMA, ALMA observations, Deep, Deep Field, Deep Survey, Euclid, Euclid Deep Survey, Spitzer, Spitzer data, Spritz, Survey catalogue, activity, algorithm, analogues, analysis, band, broadband photometry, capability, catalogue, cosmic star-formation rate, cosmic star-formation rate density, data, density, depth, distance, dusty galaxies, dusty population, error, features, field, findings, fraction, galaxies, goal, gradient boosting algorithm, high-redshift galaxies, impact, information, lack, low space density, massive galaxies, mean square error, method, near-IR data, near-IR surveys, objective, observations, optical surveys, photometric observations, photometry, population, range, rate, rate density, realization, redshift, redshift range, relevant results, results, root mean square error, runtime, software, source, space density, spectral type, spectroscopic features, star-formation, star-formation activity, star-formation rate, star-formation rate density, statistically, statistically relevant results, survey, target, type

Funders

  • Ministry of Education
  • National Aeronautics and Space Administration
  • Academy of Finland
  • Ministry of Economy, Industry and Competitiveness
  • Belgian Federal Science Policy Office
  • Fundação para a Ciência e Tecnologia
  • Agenzia Spaziale Italiana
  • United Kingdom Space Agency

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