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Euclid preparation LXVIII: Extracting physical parameters from galaxies with machine learning
Tekijät: Kovačić, I.; Baes, M.; Nersesian, A.; Andreadis, N.; Nemani, L.; Abdurro'Uf, ; Bisigello, L.; Bolzonella, M.; Tortora, C.; Van, Der Wel A.; Cavuoti, S.; Conselice, C.J.; Enia, A.; Hunt, L.K.; Iglesias-Navarro, P.; Iodice, E.; Knapen, J.H.; Marleau, F.R.; Müller, O.; Peletier, R.F.; Román, J.; Ragusa, R.; Salucci, P.; Saifollahi, T.; Scodeggio, M.; Siudek, M.; De, Waele T.; Amara, A.; Andreon, S.; Auricchio, N.; Baccigalupi, C.; Baldi, M.; Bardelli, S.; Battaglia, P.; Bender, R.; Bodendorf, C.; Bonino, D.; Bon, W.; 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.; Conversi, L.; Copin, Y.; Courbin, F.; Courtois, H.M.; Da Silva, A.; Degaudenzi, H.; De Lucia, G.; Di Giorgio, A.M.; Dinis, J.; Douspis, M.; Dubath, F.; Dupac, X.; Dusini, S.; Ealet, A.; Farina, M.; Farrens, S.; Faustini, F.; Ferriol, S.; Fosalba, P.; Frailis, M.; Franceschi, E.; Galeotta, S.; Gillis, B.; Giocoli, C.; Grazian, A.; Grupp, F.; Guzzo, L.; Haugan, S.V.H.; Holmes, W.; Hook, I.; Hormuth, F.; Hornstrup, A.; Jahnke, K.; Jhabvala, M.; Joachimi, B.; Keihänen, E.; Kermiche, S.; Kiessling, A.; Kilbinger, M.; Kubik, B.; Kuijken, K.; Kümmel, M.; Kunz, M.; Kurki-Suonio, H.; Ligori, S.; Lilje, P.B.; Lindholm, V.; Lloro, I.; Maino, D.; Maiorano, E.; Mansutti, O.; Marcin, S.; Marggraf, O.; Markovic, K.; Martinelli, M.; Martinet, N.; Marulli, F.; Massey, R.; Medinaceli, E.; Mei, S.; Melchior, M.; Mellier, Y.; Meneghetti, M.; Merlin, E.; Meylan, G.; Moresco, M.; Moscardini, L.; Niemi, S.-M.; Nightingale, J.W.; Padilla, C.; Paltani, S.; Pasian, F.; Pedersen, K.; Pettorino, V.; Pires, S.; Polenta, G.; Poncet, M.; Popa, L.A.; Pozzetti, L.; Raison, F.; Rebolo, R.; Renzi, A.; Rhodes, J.; Riccio, G.; Romelli, E.; Roncarelli, M.; Rossetti, E.; Saglia, R.; Sakr, Z.; Sánchez, A.G.; Sapone, D.; Sartoris, B.; Schirmer, M.; Schneider, P.; Schrabback, T.; Secroun, A.; Seidel, G.; Serrano, S.; Sirignano, C.; Sirri, G.; Stanco, L.; Steinwagner, J.; Tallada-Crespí, P.; Tavagnacco, D.; Taylor, A.N.; Teplitz, H.I.; Tereno, I.; Toledo-Moreo, R.; Torradeflot, F.; Tutusaus, I.; Valenziano, L.; Vassallo, T.; Verdoes, Kleijn G.; Veropalumbo, A.; Wang, Y.; Weller, J.; Zacchei, A.; Zamorani, G.; Zucca, E.; Biviano, A.; Bozzo, E.; Burigana, C.; Calabrese, M.; Di Ferdinando, D.; Escartin, Vigo J.A.; Finelli, F.; Gracia-Carpio, J.; Matthew, S.; Mauri, N.; Pöntinen, M.; Scottez, V.; Tenti, M.; Viel, M.; Wiesmann, M.; Akrami, Y.; Allevato, V.; Alvi, S.; Anselmi, S.; Archidiacono, M.; Atrio-Barandela, F.; Ballardini, M.; Bethermin, M.; Blot, L.; Borgani, S.; Bruton, S.; Cabanac, R.; Calabro, A.; Camacho, Quevedo B.; Cañas-Herrera, G.; Cappi, A.; Caro, F.; Carvalho, C.S.; Castro, T.; Chambers, K.C.; Contini, T.; Cooray, A.R.; Cucciati, O.; Desprez, G.; Díaz-Sánchez, A.; Diaz, J.J.; Di Domizio, S.; Dole, H.; Escoffier, S.; Ferrari, A.G.; Ferreira, P.G.; Ferrero, I.; Finoguenov, A.; Fontana, A.; Fornari, F.; Gabarra, L.; Ganga, K.; García-Bellido, J.; Gasparetto, T.; Gautard, V.; Gaztanaga, E.; Giacomini, F.; Gianotti, F.; Gozaliasl, G.; Gutierrez, C.M.; Hall, A.; Hemmati, S.; Hildebrandt, H.; Hjorth, J.; Jimenez, Muñoz A.; Kajava, J.J.E.; Kansal, V.; Karagiannis, D.; Kirkpatrick, C.C.; Le Brun, A.M.C.; Le Graet, J.; Lesgourgues, J.; Liaudat, T.I.; Loureiro, A.; Macias-Perez, J.; Maggio, G.; Magliocchetti, M.; Mannucci, F.; Maoli, R.; Martín-Fleitas, J.; Martins, C.J.A.P.; Maurin, L.; Metcalf, R.B.; Miluzio, M.; Monaco, P.; Montoro, A.; Mora, A.; Moretti, C.; Morgante, G.; Walton, N.A.; Patrizii, L.; Popa, V.; Potter, D.; Risso, I.; Rocci, P.-F.; Sahlén, M.; Sarpa, E.; Scarlata, C.; Schneider, A.; Sereno, M.; Shankar, F.; Simon, P.; Spurio, Mancini A.; Stadel, J.; 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.; Vielzeuf, P.; Euclid Collaboration
Kustantaja: EDP Sciences
Julkaisuvuosi: 2025
Journal: Astronomy and Astrophysics
Tietokannassa oleva lehden nimi: Astronomy & Astrophysics
Artikkelin numero: A284
Vuosikerta: 695
ISSN: 0004-6361
eISSN: 1432-0746
DOI: https://doi.org/10.1051/0004-6361/202453111
Verkko-osoite: https://doi.org/10.1051/0004-6361/202453111
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/491952456
The Euclid mission is generating a vast amount of imaging data in four broadband filters at a high angular resolution. This data will allow for the detailed study of mass, metallicity, and stellar populations across galaxies that will constrain their formation and evolutionary pathways. Transforming the Euclid imaging for large samples of galaxies into maps of physical parameters in an efficient and reliable manner is an outstanding challenge. Here, we investigate the power and reliability of machine learning techniques to extract the distribution of physical parameters within well-resolved galaxies. We focus on estimating stellar mass surface density, mass-averaged stellar metallicity, and age. We generated noise-free synthetic high-resolution (100 pc × 100 pc) imaging data in the Euclid photometric bands for a set of 1154 galaxies from the TNG50 cosmological simulation. The images were generated with the SKIRT radiative transfer code, taking into account the complex 3D distribution of stellar populations and interstellar dust attenuation. We used a machine learning framework to map the idealised mock observational data to the physical parameters on a pixel-by-pixel basis. We find that stellar mass surface density can be accurately recovered with a ≤0.130 dex scatter. Conversely, stellar metallicity and age estimates are, as expected, less robust, but they still contain significant information that originates from underlying correlations at a sub-kiloparsec scales between stellar mass surface density and stellar population properties. As a corollary, we show that TNG50 follows a spatially resolved mass-metallicity relation that is consistent with observations. Due to its relatively low computational and time requirements, which has a time-frame of minutes without dedicated high performance computing infrastructure once it has been trained, our method allows for fast and robust estimates of the stellar mass surface density distributions of nearby galaxies from four-filter Euclid imaging data. Equivalent estimates of stellar population properties (stellar metallicity and age) are less robust but still hold value as first-order approximations across large samples.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
Co-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. Neither the European Union nor the granting authority can be held responsible for them. IK, MB and AN acknowledge support from the Belgian Science Policy Office (BELSPO) through the PRODEX project “Belgian Euclid Science Exploitation (BESE)” (No. 4000143202). JHK acknowledges grant PID2022-136505NB-I00 funded by MCIN/AEI/10.13039/501100011033 and EU, ERDF. We wish to thank the “Summer School for Astrostatistics in Crete” for providing training on the statistical methods adopted in this work. 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 Academy of Finland, the Agenzia Spaziale Italiana, the Belgian Science Policy, the Canadian Euclid Consortium, the French Centre National d’Etudes Spatiales, the Deutsches Zentrum für Luft-und Raumfahrt, the Danish Space Research Institute, the Fundação para a Ciência e a Tecnologia, the Ministerio de Ciencia e Innovación, the National Aeronautics and Space Administration, the National Astronomical Observatory of Japan, the Netherlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, 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 (http://www.euclid-ec.org).
