Euclid preparation. XXV. The Euclid Morphology Challenge -- Towards model-fitting photometry for billions of galaxies
Auteurs : S. Davini, N. Mauri, L. Patrizii, G. Sirri, Y. Wang, A. A. Nucita, O. Ilbert, M. Meneghetti, Euclid Collaboration, G. Desprez, S. Paltani, J. Coupon, M. Brescia, S. Cavuoti, W. G. Hartley, A. Tramacere, M. Castellano, F. Dubath, E. Merlin, S. Andreon, N. Auricchio, C. Baccigalupi, A. Balaguera-Antolínez, M. Baldi, S. Bardelli, R. Bender, A. Biviano, C. Bodendorf, E. Branchini, C. Burigana, R. Cabanac, S. Camera, V. Capobianco, A. Cappi, C. Carbone, J. Carretero, C. S. Carvalho, S. Casas, F. J. Castander, G. Castignani, A. Cimatti, R. Cledassou, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, L. Corcione, H. M. Courtois, A. Da Silva, H. Degaudenzi, D. Di Ferdinando, M. Douspis, C. A. J. Duncan, X. Dupac, S. Farrens, M. Frailis, E. Franceschi, S. Galeotta, B. Garilli, B. Gillis, C. Giocoli, G. Gozaliasl, J. Graciá-Carpio, F. Grupp, S. V. H. Haugan, W. Holmes, F. Hormuth, K. Jahnke, E. Keihanen, S. Kermiche, C. C. Kirkpatrick, R. Kohley, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, I. Lloro, O. Marggraf, K. Markovic, N. Martinet, F. Marulli, R. Massey, M. Maturi, E. Medinaceli, S. Mei, G. Meylan, M. Moresco, L. Moscardini, E. Munari, C. Padilla, F. Pasian, G. Polenta, M. Poncet, L. Popa, D. Potter, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Rossetti, R. Saglia, D. Sapone, P. Schneider, V. Scottez, A. Secroun, C. Sirignano, A. N. Taylor, I. Tereno, R. Toledo-Moreo, L. Valenziano, J. Valiviita, T. Vassallo, M. Viel, A. Zacchei, G. Zamorani, J. Zoubian, E. Zucca, F. Courbin, H. Bretonnière, M. Huertas-Company, U. Kuchner, D. Tuccillo, F. Buitrago, J. R. Peterson, F. Caro, P. Dimauro, L. Nemani, A. Fontana, M. Kümmel, B. Häußler, A. Alvarez Ayllon, E. Bertin, P. Dubath, F. Ferrari, L. Ferreira, R. Gavazzi, D. Hernández-Lang, G. Lucatelli, A. S. G. Robotham, M. Schefer, C. Tortora, N. Aghanim, A. Amara, L. Amendola, M. Cropper, J. Dinis, S. Dusini, S. Ferriol, P. Franzetti, A. Grazian, H. Hoekstra, A. Hornstrup, P. Hudelot, A. Kiessling, T. Kitching, O. Mansutti, H. J McCracken, M. Melchior, S. M. Niemi, K. Pedersen, W. J. Percival, R. Rebolo, E. Romelli, B. Sartoris, G. Seidel, J. Skottfelt, J. -L. Starck, P. Tallada-Crespí, I. Tutusaus, J. Weller, A. Boucaud, V. Lindholm, C. Neissner, M. Ballardini, F. Bernardeau, S. Borgani, A. S. Borlaff, A. R. Cooray, O. Cucciati, G. De Lucia, J. A. Escartin, S. Escoffier, M. Farina, K. Ganga, J. Garcia-Bellido, K. George, H. Hildebrandt, I. Hook, S. Ilic, B. Joachimi, V. Kansal, A. Loureiro, J. Macias-Perez, M. Magliocchetti, G. Mainetti, R. Maoli, S. Marcin, M. Martinelli, S. Matthew, R. B. Metcalf, P. Monaco, G. Morgante, S. Nadathur, V. Popa, C. Porciani, A. Pourtsidou, M. Pöntinen, P. Reimberg, A. G. Sánchez, Z. Sakr, M. Schirmer, M. Sereno, J. Stadel, R. Teyssier, C. Valieri, S. E. van Mierlo, A. Veropalumbo, J. R. Weaver, D. Scott
Résumé : The ESA Euclid mission will provide high-quality imaging for about 1.5 billion galaxies. A software pipeline to automatically process and analyse such a huge amount of data in real time is being developed by the Science Ground Segment of the Euclid Consortium; this pipeline will include a model-fitting algorithm, which will provide photometric and morphological estimates of paramount importance for the core science goals of the mission and for legacy science. The Euclid Morphology Challenge is a comparative investigation of the performance of five model-fitting software packages on simulated Euclid data, aimed at providing the baseline to identify the best suited algorithm to be implemented in the pipeline. In this paper we describe the simulated data set, and we discuss the photometry results. A companion paper (Euclid Collaboration: Bretonni\`ere et al. 2022) is focused on the structural and morphological estimates. We created mock Euclid images simulating five fields of view of 0.48 deg2 each in the $I_E$ band of the VIS instrument, each with three realisations of galaxy profiles (single and double S\'ersic, and 'realistic' profiles obtained with a neural network); for one of the fields in the double S\'ersic realisation, we also simulated images for the three near-infrared $Y_E$, $J_E$ and $H_E$ bands of the NISP-P instrument, and five Rubin/LSST optical complementary bands ($u$, $g$, $r$, $i$, and $z$). To analyse the results we created diagnostic plots and defined ad-hoc metrics. Five model-fitting software packages (DeepLeGATo, Galapagos-2, Morfometryka, ProFit, and SourceXtractor++) were compared, all typically providing good results. (cut)
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