A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Euclid preparation: LI. Forecasting the recovery of galaxy physical properties and their relations with template-fitting and machine-learning methods
Tekijät: Enia, A.; Bolzonella, M.; Pozzetti, L.; Humphrey, A.; Cunha, P. A. C.; Hartley, W. G.; Dubath, F.; Paltani, S.; Lopez, X. Lopez; Quai, S.; Bardelli, S.; Bisigello, L.; Cavuoti, S.; De Lucia, G.; Ginolfi, M.; Grazian, A.; Siudek, M.; Tortora, C.; Zamorani, G.; Aghanim, N.; Altieri, B.; Amara, A.; Andreon, S.; Auricchio, N.; Baccigalupi, C.; Baldi, M.; Bender, R.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V; Carbone, C.; Carretero, J.; Casas, S.; Castander, F. J.; Castellano, M.; Castignani, G.; Cimatti, A.; Colodro-Conde, C.; Congedo, G.; Conselice, C. J.; Conversi, L.; Copin, Y.; Corcione, L.; Courbin, F.; Courtois, H. M.; Da Silva, A.; Degaudenzi, H.; Di Giorgio, A. M.; Dinis, J.; Dupac, X.; Dusini, S.; Fabricius, M.; Farina, M.; Farrens, S.; Ferriol, S.; Fosalba, P.; Fotopoulou, S.; Frailis, M.; Franceschi, E.; Fumana, M.; Galeotta, S.; Gillis, B.; Giocoli, C.; Grupp, F.; Haugan, S. V. H.; Holmes, W.; Hook, I; Hormuth, F.; Hornstrup, A.; Jahnke, K.; Joachimi, B.; Keihanen, E.; Kermiche, S.; Kiessling, A.; Kubik, B.; Kuemmel, M.; Kunz, M.; Kurki-Suonio, H.; Ligori, S.; Lilje, P. B.; Lindholm, V; Lloro, I; Maiorano, E.; Mansutti, O.; Marggraf, O.; Markovic, K.; Martinelli, M.; Martinet, N.; Marulli, F.; Massey, R.; McCracken, H. J.; Medinaceli, E.; Mei, S.; Melchior, M.; Mellier, Y.; Meneghetti, M.; Merlin, E.; Meylan, G.; Moresco, M.; Moscardini, L.; Munari, E.; Neissner, C.; Niemi, S-M; Nightingale, J. W.; Padilla, C.; Pasian, F.; Pedersen, K.; Pettorino, V; Polenta, G.; Poncet, M.; Popa, L. A.; Raison, F.; Rebolo, R.; Renzi, A.; Rhodes, J.; Riccio, G.; Romelli, E.; Roncarelli, M.; Rossetti, E.; Saglia, R.; Sakr, Z.; Sapone, D.; Schneider, P.; Schrabback, T.; Scodeggio, M.; Secroun, A.; Sefusatti, E.; Seidel, G.; Serrano, S.; Sirignano, C.; Sirri, G.; Stanco, L.; Steinwagner, J.; Surace, C.; Tallada-Crespi, P.; Tavagnacco, D.; Taylor, A. N.; Teplitz, H., I; Tereno, I; Toledo-Moreo, R.; Torradeflot, F.; Tutusaus, I; Valenziano, L.; Vassallo, T.; Kleijn, G. Verdoes; Veropalumbo, A.; Wang, Y.; Weller, J.; Zucca, E.; Biviano, A.; Boucaud, A.; Burigana, C.; Calabrese, M.; Vigo, J. A. Escartin; Gracia-Carpio, J.; Mauri, N.; Pezzotta, A.; Pontinen, M.; Porciani, C.; Scottez, V; Tenti, M.; Viel, M.; Wiesmann, M.; Akrami, Y.; Allevato, V; Anselmi, S.; Ballardini, M.; Bergamini, P.; Bethermin, M.; Blanchard, A.; Blot, L.; Borgani, S.; Bruton, S.; Cabanac, R.; Calabro, A.; Canas-Herrera, G.; Cappi, A.; Carvalho, C. S.; Castro, T.; Chambers, K. C.; Contarini, S.; Contini, T.; Cooray, A. R.; Cucciati, O.; Davini, S.; De Caro, B.; Desprez, G.; Diaz-Sanchez, A.; Di Domizio, S.; Dole, H.; Escoffier, S.; Ferrari, A. G.; Ferreira, P. G.; Ferrero, I; Finoguenov, A.; Fornari, F.; Gabarra, L.; Ganga, K.; Garcia-Bellido, J.; Gautard, V; Gaztanaga, E.; Giacomini, F.; Gianotti, F.; Gozaliasl, G.; Hall, A.; Hemmati, S.; Hildebrandt, H.; Hjorth, J.; Munoz, A. Jimenez; Joudaki, S.; Kajava, J. J. E.; Kansal, V; Karagiannis, D.; Kirkpatrick, C. C.; Le Graet, J.; Legrand, L.; Loureiro, A.; Macias-Perez, J.; Maggio, G.; Magliocchetti, M.; Mancini, C.; Mannucci, F.; Maoli, R.; Martins, C. J. A. P.; Matthew, S.; Maurin, L.; Metcalf, R. B.; Monaco, P.; Moretti, C.; Morgante, G.; Walton, Nicholas A.; Patrizii, L.; Popa, V; Potter, D.; Risso, I; Rocci, P-F; Sahlen, M.; Schneider, A.; Schultheis, M.; Sereno, M.; Simon, P.; Mancini, A. Spurio; Stanford, S. A.; Tanidis, K.; Tao, C.; Testera, G.; Teyssier, R.; Toft, S.; Tosi, S.; Troja, A.; Tucci, M.; Valieri, C.; Valiviita, J.; Vergani, D.; Verza, G.; Zinchenko, I. A.; Rodighiero, G.; Talia, M.; Euclid Collaboration
Kustantaja: EDP SCIENCES S A
Kustannuspaikka: LES ULIS CEDEX A
Julkaisuvuosi: 2024
Journal: Astronomy and Astrophysics
Tietokannassa oleva lehden nimi: ASTRONOMY & ASTROPHYSICS
Lehden akronyymi: ASTRON ASTROPHYS
Artikkelin numero: A175
Vuosikerta: 691
Sivujen määrä: 26
ISSN: 0004-6361
eISSN: 1432-0746
DOI: https://doi.org/10.1051/0004-6361/202451425
Verkko-osoite: https://doi.org/10.1051/0004-6361/202451425
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/478108801
Euclid will collect an enormous amount of data during the mission's lifetime, observing billions of galaxies in the extragalactic sky. Along with traditional template-fitting methods, numerous machine learning (ML) algorithms have been presented for computing their photometric redshifts and physical parameters (PPs), requiring significantly less computing effort while producing equivalent performance measures. However, their performance is limited by the quality and amount of input information entering the model (the features), to a level where the recovery of some well-established physical relationships between parameters might not be guaranteed - for example, the star-forming main sequence (SFMS). To forecast the reliability of Euclid photo-zs and PPs calculations, we produced two mock catalogs simulating the photometry with the UNIONS ugriz and Euclid filters. We simulated the Euclid Wide Survey (EWS) and Euclid Deep Fields (EDF), alongside two auxiliary fields. We tested the performance of a template-fitting algorithm (Phosphoros) and four ML methods in recovering photo-zs, PPs (stellar masses and star formation rates), and the SFMS on the simulated Euclid fields. To mimic the Euclid processing as closely as possible, the models were trained with Phosphoros-recovered labels and tested on the simulated ground truth. For the EWS, we found that the best results are achieved with a mixed labels approach, training the models with wide survey features and labels from the Phosphoros results on deeper photometry, that is, with the best possible set of labels for a given photometry. This imposes a prior to the input features, helping the models to better discern cases in degenerate regions of feature space, that is, when galaxies have similar magnitudes and colors but different redshifts and PPs, with performance metrics even better than those found with Phosphoros. We found no more than 3% performance degradation using a COSMOS-like reference sample or removing u band data, which will not be available until after data release DR1. The best results are obtained for the EDF, with appropriate recovery of photo-z, PPs, and the SFMS.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
The Euclid Consortium acknowledges the European Space Agency and a number of agencies and institutes that have supported the development of Euclid, in particular the Agenzia Spaziale Italiana, the Austrian Forschungsförderungsgesellschaft funded through BMK, the Belgian Science Policy, the Canadian Euclid Consortium, the Deutsches Zentrum für Luft- und Raumfahrt, the DTU Space and the Niels Bohr Institute in Denmark, the French Centre National d’Etudes Spatiales, the Fundação para a Ciência e a Tecnologia, the Hungarian Academy of Sciences, the Ministerio de Ciencia, Innovación y Universidades, the National Aeronautics and Space Administration, the National Astronomical Observatory of Japan, the Netherlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Research Council of Finland, the Romanian Space Agency, the State Secretariat for Education, Research, and Innovation (SERI) at the Swiss Space Office (SSO), and the United Kingdom Space Agency. A complete and detailed list is available on the Euclid web site (www.euclid-ec.org). AE, MB, LP, SQ, MT, GDL, VA, JB, SF, MS acknowledge support from the ELSA project. “ELSA: Euclid Legacy Science Advanced analysis tools” (Grant Agreement no. 101135203) is funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Innovate UK. Neither the European Union nor the granting authority can be held responsible for them. UK participation is funded through the UK HORIZON guarantee scheme under Innovate UK grant 10093177. We acknowledge the support from grant PRIN MIUR 2017-20173ML3WW_s. We acknowledge the CINECA award under the ISCRA initiative, for the availability of high performance computing resources and support. M.S. acknowledges support by the Polish National Agency for Academic Exchange (Bekker grant BPN/BEK/2021/1/00298/DEC/1), the State Research Agency of the Spanish Ministry of Science and Innovation under the grants ‘Galaxy Evolution with Artificial Intelligence’ (PGC2018-100852-A-I00) and ‘BASALT’ (PID2021-126838NB-I00). This work was partially supported by the European Union’s Horizon 2020 Research and Innovation program under the Maria Sklodowska-Curie grant agreement (No. 754510). Phosphoros filters are taken from the SVO Filter Profile Service (Rodrigo et al. 2012; Rodrigo & Solano 2020). This research has made use of the Spanish Virtual Observatory (https://svo.cab.inta-csic.es) project funded by MCIN/AEI/10.13039/501100011033/ through grant PID2020-112949GB-I00. In preparation for this work, we used the following codes for Python: Numpy (Harris et al. 2020), Scipy (Virtanen et al. 2020), Scikit-Learn (Pedregosa 2011), Pandas (Wes McKinney 2010), CatBoost (Prokhorenkova et al. 2018), Tensorflow (Abadi et al. 2016), nnpz (Tanaka et al. 2018), Phosphoros (Paltani et al., in prep.).
