J-PAS: A neural network approach to single stellar population characterisation

J-PAS (Javalambre Physics of the Accelerating Universe Astrophysical Survey) will present a groundbreaking photometric survey covering 8500 deg 2 of the visible sky from Javalambre, capturing data in 56 narrow-band filters. This survey promises to revolutionise galaxy evolution studies by observing ∼10 8 galaxies with low spectral resolution. A crucial aspect of this analysis involves predicting stellar population parameters from the observed galaxy photometry. In this study, we combined the exquisite J-PAS photometry with state-of-the-art single stellar population (SSP) libraries to accurately predict stellar age, metallicity, and dust attenuation with a neural network (NN) model. The NN was trained on synthetic J-PAS photometry from different SSP libraries (E-MILES, Charlot & Bruzual, and XSL) to enhance the robustness of our predictions against individual SSP model variations and limitations. To create mock samples with varying observed magnitudes, we added artificial noise in the form of random Gaussian variations within typical observational uncertainties in each band. Our results indicate that the NN was able to accurately estimate stellar parameters for SSP models without any evident degeneracies, surpassing a Bayesian SED-fitting method on the same test set. We obtained the median bias, scatter, and the percentage of outliers: μ = (0.01 dex, 0.00 dex, 0.00 mag), σ NMAD = (0.23 dex, 0.29 dex, 0.04 mag), f o = (17%, 24%, 1%) at i ∼ 17 mag for the age, metallicity and dust attenuation, respectively. The accuracy of the predictions is highly dependent on the signal-to-noise ratio (S/N) of the photometry, achieving robust predictions up to i ∼ 20 mag.

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Journal
Astronomy and Astrophysics
Published
2025-12-02
DOI
https://doi.org/10.1051/0004-6361/202556251
Primary Topic
Galaxies: Formation, Evolution, Phenomena
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article
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article

J-PAS: A neural network approach to single stellar population characterisation

L. A. Díaz-García, Renato Dupke, M. Moles, A. Ederoclite et al.
Astronomy and Astrophysics
Galaxies: Formation, Evolution, Phenomena
article

J-PAS: A neural network approach to single stellar population characterisation

L. A. Díaz-García, Renato Dupke, M. Moles, A. Ederoclite, J. M. Vı́lchez, A. Álvarez-Candal, Cristina Mendes de Oliveira, Silvia Bonoli, S. Carneiro, Siddhartha Gurung-López, H. Domínguez Sánchez, C. López-Sanjuán, D. Cristóbal-Hornillos, L. E. Suelves, Vinicius M. Placco, N. Benı́tez, D. Cristóbal-Hornillos, Iris Breda, K. Taylor, Jesús Vega-Ferrero, J. Alcaniz, R. Abramo, P. Coelho, J. A. Fernández-Ontiveros, J. Varela, A. Hernán-Caballero, C. Hernández–Monteagudo, L. Sodré, H. Vázquez Ramió, Gustavo Bruzual, Javier Cenarro
article en

Abstract

J-PAS (Javalambre Physics of the Accelerating Universe Astrophysical Survey) will present a groundbreaking photometric survey covering 8500 deg 2 of the visible sky from Javalambre, capturing data in 56 narrow-band filters. This survey promises to revolutionise galaxy evolution studies by observing ∼10 8 galaxies with low spectral resolution. A crucial aspect of this analysis involves predicting stellar population parameters from the observed galaxy photometry. In this study, we combined the exquisite J-PAS photometry with state-of-the-art single stellar population (SSP) libraries to accurately predict stellar age, metallicity, and dust attenuation with a neural network (NN) model. The NN was trained on synthetic J-PAS photometry from different SSP libraries (E-MILES, Charlot & Bruzual, and XSL) to enhance the robustness of our predictions against individual SSP model variations and limitations. To create mock samples with varying observed magnitudes, we added artificial noise in the form of random Gaussian variations within typical observational uncertainties in each band. Our results indicate that the NN was able to accurately estimate stellar parameters for SSP models without any evident degeneracies, surpassing a Bayesian SED-fitting method on the same test set. We obtained the median bias, scatter, and the percentage of outliers: μ = (0.01 dex, 0.00 dex, 0.00 mag), σ NMAD = (0.23 dex, 0.29 dex, 0.04 mag), f o = (17%, 24%, 1%) at i ∼ 17 mag for the age, metallicity and dust attenuation, respectively. The accuracy of the predictions is highly dependent on the signal-to-noise ratio (S/N) of the photometry, achieving robust predictions up to i ∼ 20 mag.

Astronomy and AstrophysicsVol. 705
Universidad de La Laguna (ES), Universitat de València (ES), Universidade de São Paulo (BR), Parc Científic de la Universitat de València (ES), Tartu Observatory (EE), California Earthquake Authority (US), Centro de Estudios de Física del Cosmos de Aragón (ES), National Observatory (BR), Instituto de Astrofísica de Canarias (ES), Instituto de Física de Cantabria (ES), Instituto de Geofísica y Astronomía (CU), Instituto de Astrofísica de Andalucía (ES), Donostia International Physics Center (ES), University of Tartu (EE), Universidad Nacional Autónoma de México (MX)
Openalex Percentile: Top 74%
Galaxies: Formation, Evolution, Phenomena
DOIOpen Access PDFOpenAlex
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