The various Euclid imaging surveys will become a reference for studies of galaxy morphology by delivering imaging over an unprecedented area of 15 000 square degrees with high spatial resolution. In order to understand the capabilities of measuring morphologies from Euclid-detected galaxies and to help implement measurements in the pipeline of the Organisational Unit MER of the Euclid Science Ground Segment, we have conducted the Euclid Morphology Challenge, which we present in two papers. While the companion paper focusses on the analysis of photometry, this paper assesses the accuracy of the parametric galaxy morphology measurements in imaging predicted from within the Euclid Wide Survey. We evaluate the performance of five state-of-the-art surface-brightness-fitting codes, DeepLeGATo, Galapagos-2, Morfometryka, ProFit and SourceXtractor++, on a sample of about 1.5 million simulated galaxies (350 000 above 5s) resembling reduced observations with the Euclid VIS and NIR instruments. The simulations include analytic Sérsic profiles with one and two components, as well as more realistic galaxies generated with neural networks. We find that, despite some code-specific differences, all methods tend to achieve reliable structural measurements (< 10% scatter on ideal Sérsic simulations) down to an apparent magnitude of about IE = 23 in one component and IE = 21 in two components, which correspond to a signal-to-noise ratio of approximately 1 and 5, respectively. We also show that when tested on non-analytic profiles, the results are typically degraded by a factor of 3, driven by systematics. We conclude that the official Euclid Data Releases will deliver robust structural parameters for at least 400 million galaxies in the Euclid Wide Survey by the end of the mission. We find that a key factor for explaining the different behaviour of the codes at the faint end is the set of adopted priors for the various structural parameters.
Euclid preparation: XXVI. the Euclid Morphology Challenge: Towards structural parameters for billions of galaxies / Bretonniere, H.; Kuchner, U.; Huertas-Company, M.; Merlin, E.; Castellano, M.; Tuccillo, D.; Buitrago, F.; Conselice, C. J.; Boucaud, A.; Hauabler, B.; Kummel, M.; Hartley, W. G.; Alvarez Ayllon, A.; Bertin, E.; Ferrari, F.; Ferreira, L.; Gavazzi, R.; Hernandez-Lang, D.; Lucatelli, G.; Robotham, A. S. G.; Schefer, M.; Wang, L.; Cabanac, R.; Dominguez Sanchez, H.; Duc, P. -A.; Fotopoulou, S.; Kruk, S.; La Marca, A.; Margalef-Bentabol, B.; Marleau, F. R.; Tortora, C.; Aghanim, N.; Amara, A.; Auricchio, N.; Azzollini, R.; Baldi, M.; Bender, R.; Bodendorf, C.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Castander, F. J.; Cavuoti, S.; Cimatti, A.; Cledassou, R.; Congedo, G.; Conversi, L.; Copin, Y.; Corcione, L.; Courbin, F.; Cropper, M.; Da Silva, A.; Degaudenzi, H.; Dinis, J.; Dubath, F.; Duncan, C. A. J.; Dupac, X.; Dusini, S.; Farrens, S.; Ferriol, S.; Frailis, M.; Franceschi, E.; Fumana, M.; Galeotta, S.; Garilli, B.; Gillis, B.; Giocoli, C.; Grazian, A.; Grupp, F.; Haugan, S. V. H.; Hoekstra, H.; Holmes, W.; Hormuth, F.; Hornstrup, A.; Hudelot, P.; Jahnke, K.; Kermiche, S.; Kiessling, A.; Kohley, R.; Kunz, M.; Kurki-Suonio, H.; Ligori, S.; Lilje, P. B.; Lloro, I.; Mansutti, O.; Marggraf, O.; Markovic, K.; Marulli, F.; Massey, R.; Mccracken, H. J.; Medinaceli, E.; Melchior, M.; Meneghetti, M.; Meylan, G.; Moresco, M.; Moscardini, L.; Munari, E.; Niemi, S. M.; Padilla, C.; Paltani, S.; Pasian, F.; Pedersen, K.; Percival, W.; Pettorino, V.; Polenta, G.; Poncet, M.; Pozzetti, L.; Raison, F.; Rebolo, R.; Renzi, A.; Rhodes, J.; Riccio, G.; Romelli, E.; Rosset, C.; Rossetti, E.; Saglia, R.; Sapone, D.; Sartoris, B.; Schneider, P.; Secroun, A.; Seidel, G.; Sirignano, C.; Sirri, G.; Skottfelt, J.; Starck, J. -L.; Tallada-Crespi, P.; Taylor, A. N.; Tereno, I.; Toledo-Moreo, R.; Tutusaus, I.; Valentijn, E. A.; Valenziano, L.; Vassallo, T.; Wang, Y.; Weller, J.; Zamorani, G.; Zoubian, J.; Andreon, S.; Bardelli, S.; Colodro-Conde, C.; Di Ferdinando, D.; Gracia-Carpio, J.; Lindholm, V.; Mauri, N.; Mei, S.; Scottez, V.; Zucca, E.; Baccigalupi, C.; Ballardini, M.; Bernardeau, F.; Biviano, A.; Borgani, S.; Borlaff, A. S.; Burigana, C.; Cappi, A.; Carvalho, C. S.; Casas, S.; Castignani, G.; Cooray, A. R.; Coupon, J.; Courtois, H. M.; Davini, S.; De Lucia, G.; Desprez, G.; Escartin, J. A.; Escoffier, S.; Fabricius, M.; Farina, M.; Fontana, A.; Ganga, K.; Garcia-Bellido, J.; George, K.; Gozaliasl, G.; Hildebrandt, H.; Hook, I.; Ilbert, O.; Ilia, S.; Joachimi, B.; Kansal, V.; Keihanen, E.; Kirkpatrick, C. C.; Loureiro, A.; MacIas-Perez, J.; Magliocchetti, M.; Maoli, R.; Marcin, S.; Martinelli, M.; Martinet, N.; Maturi, M.; Monaco, P.; Morgante, G.; Nadathur, S.; Nucita, A. A.; Patrizii, L.; Popa, V.; Porciani, C.; Potter, D.; Pourtsidou, A.; Pontinen, M.; Reimberg, P.; Sanchez, A. G.; Sakr, Z.; Schirmer, M.; Sefusatti, E.; Sereno, M.; Stadel, J.; Teyssier, R.; Valiviita, J.; Van Mierlo, S. E.; Veropalumbo, A.; Viel, M.; Weaver, J. R.; Scott, D.. - In: ASTRONOMY & ASTROPHYSICS. - ISSN 0004-6361. - 671:(2023). [10.1051/0004-6361/202245042]
Euclid preparation: XXVI. the Euclid Morphology Challenge: Towards structural parameters for billions of galaxies
Polenta G.;Baccigalupi C.;Maoli R.;
2023
Abstract
The various Euclid imaging surveys will become a reference for studies of galaxy morphology by delivering imaging over an unprecedented area of 15 000 square degrees with high spatial resolution. In order to understand the capabilities of measuring morphologies from Euclid-detected galaxies and to help implement measurements in the pipeline of the Organisational Unit MER of the Euclid Science Ground Segment, we have conducted the Euclid Morphology Challenge, which we present in two papers. While the companion paper focusses on the analysis of photometry, this paper assesses the accuracy of the parametric galaxy morphology measurements in imaging predicted from within the Euclid Wide Survey. We evaluate the performance of five state-of-the-art surface-brightness-fitting codes, DeepLeGATo, Galapagos-2, Morfometryka, ProFit and SourceXtractor++, on a sample of about 1.5 million simulated galaxies (350 000 above 5s) resembling reduced observations with the Euclid VIS and NIR instruments. The simulations include analytic Sérsic profiles with one and two components, as well as more realistic galaxies generated with neural networks. We find that, despite some code-specific differences, all methods tend to achieve reliable structural measurements (< 10% scatter on ideal Sérsic simulations) down to an apparent magnitude of about IE = 23 in one component and IE = 21 in two components, which correspond to a signal-to-noise ratio of approximately 1 and 5, respectively. We also show that when tested on non-analytic profiles, the results are typically degraded by a factor of 3, driven by systematics. We conclude that the official Euclid Data Releases will deliver robust structural parameters for at least 400 million galaxies in the Euclid Wide Survey by the end of the mission. We find that a key factor for explaining the different behaviour of the codes at the faint end is the set of adopted priors for the various structural parameters.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
