<i>Euclid</i> preparation

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.

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Publication Details

Journal
Astronomy and Astrophysics
Published
2025-02-10
DOI
https://doi.org/10.1051/0004-6361/202453111
Citations
4
Primary Topic
Astronomy and Astrophysical Research
Type
article
Field-Weighted Citation Impact
4.77

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article

Euclid preparation

M Viel, G. Seidel, J. Brinchmann, Laura Bisigello et al.
4 citations
Astronomy and Astrophysics
Astronomy and Astrophysical Research
4.77
article

Euclid preparation

M Viel, G. Seidel, J. Brinchmann, Laura Bisigello, J. Graciá‐Carpio, M. Ballardini, P. Tallada-Crespí, S Matthew, G. Riccio, F. Gianotti, William Bon, C. Sirignano, M. Brescia, E. Merlin, I. Hook, I. Ferrero, M. Bolzonella, Patricia Iglesias-Navarro, M. Schirmer, S. Borgani, N. Mauri, C. Giocoli, O. Marggraf, S Dusini, Patrice Simon, J. F. Macías–Pérez, F. Courbin, Y. Copin, Alex D. Hall, M Baldi, A. Troja, Joachim Stadel, A. Grazian, A. Díaz‐Sánchez, G. Sirri, G. Gozaliasl, S. Bardelli, L Conversi, G. Desprez, T. Contini, F Faustini, A Montoro, Harry I. Teplitz, Oliver Müller, R. F. Peletier, M. Magliocchetti, G Verza, J Steinwagner, G Castignani, Claudio Moretti, Javier Román, O. Cucciati, V. Capobianco, L. Moscardini, J Hjorth, S.-M Niemi, T. Schrabback, M Miluzio, S. Farrens, T. Saifollahi, J Le Graet, S. Pires, D. Vergani, Chunhui Tao, R. Massey, L. Gabarra, T. Vassallo, A Enia, Benjamin Joachimi, S. Paltani, D. Tavagnacco, Valérie Gautard, R. Toledo-Moreo, B. Camacho Quevedo, S Bruton, M. Moresco, M. Baes, L Stanco, D. Sapone, T. Castro, M. Meneghetti, D. Bonino, F Fornari, L. Patrizii, F Grupp, A. Veropalumbo, M. Poncet, G. Congedo, L. Valenziano, A Kiessling, K. Ganga, H. Dole, H. Hildebrandt, L.K Hunt, F.R Marleau, E Gaztanaga, E. Romelli, C. Burigana, S Andreon, A. Biviano, I. Tutusaus, O. Mansutti, G. Zamorani, N. Martinet, I Risso, V. Allevato, J. Väliviita, S. Escoffier, C. Padilla Aranda, L Blot, E. Maiorano, A Finoguenov, B. Gillis, C Bodendorf, M Sahlén, M. Roncarelli, F. Dubath, S Casas, A. Hornstrup, E. Medinaceli, E. Iodice, A. Amara, M. Pöntinen, S. Di Domizio, Francesco Shankar, J Weller, M. Béthermin, K. Jahnkę, A. Secroun, M. Farina, F. Mannucci, M Jhabvala, R. Bender, F. Pasian, L. Guzzo, V. Kansal, S Anselmi, Nick Andreadis, Sameer Alvi, S. Ligori, M Melchior, F. Giacomini, Angelos Nersesian, A. Zacchei, S. Galeotta, F. Raison, E. Franceschi, M Calabrese, J Martín-Fleitas, Konrad H. Kuijken, M. Siudek, N. A. Walton, S. Marcin, G Cañas-Herrera, K. Markovič, S. Camera, Z. Sakr, K Tanidis, E. Bozzo, Romain Teyssier, M. Kilbinger, F. Finelli⋆, T Gasparetto, J. Dinis, V. Lindholm, R. Rébolo, P.-F Rocci, M. Sereno, E. Keihänen, Anthony Calabro, B. Sartoris, P Vielzeuf, P. C. Schneider, S. Cavuoti, N. Auricchio, M. Frailis, V Scottez, A. Spurio Mancini, E Zucca, C. Carbone, Shoubaneh Hemmati, Y Mellier, S. Kermiche, Arthur Loureiro, Arjen van der Wel, F Caro, E Sarpa, Sune Toft, Claudia Scarlata, M. Scodeggio, L. Nemani, A. Fontana, S. Serrano, G. Polenta, E. Rossetti, A.M.C Le Brun, Aurel Schneider, D. Potter, P. Salucci, M. Douspis, M. G. Castellano, E. Branchini, L. Pozzetti, Y. Akrami, F. Atrio‐Barandela, I Kovačić, M Abdurro'Uf, Yun Wang, S. Tosi, A. Da Silva, Pedro G. Ferreira, L. Maurin, D Karagiannis, A. Jiménez Muñoz, G de Lucia, M Kunz, F Torradeflot, J Rhodes, D. Maino, X. Dupac, M. Tenti, D. Di Ferdinando, J García-Bellido, I. Tereno, P Battaglia, G. Meylan, H. Degaudenzi, H. Kurki‐Suonio, M. Wiesmann, A. Ealet, W. A. Holmes, G. Testera, G. Maggio, V. Popa, F. Hormuth, S Mei, V Pettorino, R Saglia, K. Pedersen, A. Cimatti, A Cappi, B. Kubik, R Cabanac, I. Lloro, F Marulli, P Monaco, Y. Ascasíbar, J Carretero, J Lesgourgues, C Valieri, M Archidiacono, A. Mora, P Fosalba, C Colodro-Conde, C. Baccigalupi, S. Ferriol, A Renzi, M Martinelli, M. Kümmel, G Morgante, C.J Conselice, T de Waele
article en
4 citations

Abstract

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.

Astronomy and AstrophysicsVol. 695
Université Paris Cité (FR), Université de Lille (FR), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Centre National d'Études Spatiales (FR), Institut d'Astrophysique de Paris (FR), European Space Agency (FR), Laboratoire AstroParticule et Cosmologie (FR), Observatoire astronomique de Strasbourg (FR), Astrophysique, Instrumentation et Modélisation (FR), Janssen (France) (FR), Laboratoire Univers et Théories (FR), Centre de physique des particules de Marseille (FR), Laboratoire d’Astrophysique de Marseille (FR), Institut d'Astrophysique Spatiale (FR), Lagrange Laboratory (FR), Château Gombert (FR), CEA Paris-Saclay (FR), Laboratoire de Physique Subatomique et de Cosmologie (FR), Institute of Nuclear Physics of Lyon (FR), Institut de Recherche sur les Lois Fondamentales de l'Univers (FR), Institut de Recherche en Astrophysique et Planétologie (FR), Institut de Physique des 2 Infinis de Lyon (FR)
National Aeronautics and Space Administration, European Space Agency, European Commission, Belgian Federal Science Policy Office, Agenzia Spaziale Italiana, Ministerio de Ciencia e Innovación, National Astronomical Observatory of Japan, Agenția Spațială Română, Norsk Romsenter, Fundação para a Ciência e a Tecnologia, Staatssekretariat für Bildung, Forschung und Innovation, European Regional Development Fund, Agencia Estatal de Investigación
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Openalex Percentile: Top 6%
Astronomy and Astrophysical Research
4.77
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